Training Coach

Doctrine

A report may cite any of these as doctrine-N.

1. doctrine-1Contract freeze and fingerprint discipline

   The policy I/O contract (observation layout, scales, history semantics, action pipeline) is frozen and fingerprinted; every exported policy is stamped and verified; contract changes ship as new versioned profiles that leave old artifacts bit-identical, and old policies run forever under their era's pinned profile.

   Case. The 215-dim omni contract was frozen with a three-machine digest; the one contract-level extension (lateral feed-forward) went in as a new `omni_ff` profile with the old profile provably untouched, and the contract checker caught two real wiring bugs before any training (`contract-freeze-and-checker`). A silently changed gait-clock default would have fed old policies a 25% slower clock - closed by pinned legacy profiles (`legacy-profile-pinning`). A stale derived USD forked plant mass 2.2% until an automated source-vs-derived instrument gated it (`derived-asset-staleness-check`). A gain profile is part of the closed loop a policy was trained in and belongs in its stamp; the recovery line's anchored authority was left out of its manifest and recorded as the gap not to repeat (`gain-profile-belongs-in-the-stamp`), and a second policy behind a deploy-side switch made the handoff state itself a contract (`recovery-two-policies-and-a-state-machine`, `walk-recovery-fsm-handoff`).

   Coach application. On any proposal touching obs/action semantics, defaults, or derived assets: demand the version/profile plan, the fingerprint update, and the checker extension in the same change; flag any old artifact that would run under new defaults.

   contract-freeze-and-checkerlegacy-profile-pinningderived-asset-staleness-checkgain-profile-belongs-in-the-stamprecovery-two-policies-and-a-state-machinewalk-recovery-fsm-handoff

2. doctrine-2Attribution by resolved training params - never eval-override knobs

   Capability differences between lineages are explained only by digging each lineage's *resolved* training configuration and eliminating columns; evaluation-side override knobs (kd-scale, power-scale, cycle-time) act on the plant for *every* policy and may serve as deployment mitigations but never as explanations.

   Case. Low-friction robustness across 8 lineages x 3840 cells was traced to kd DR *bandwidth* - every lineage had ground friction pinned to (1.0,1.0), so "trained friction" could not be the axis; the parameter axis and the plant axis were explicitly separated after the first attribution conflated them (`kd-bandwidth-mu-law-attribution`). "Weak turning" on hardware was a power-scale plant effect, not a training gap (`deploy-knob-attribution-before-retraining`); slowing the deploy clock was out-of-distribution, not a feature (`cycle-time-override-is-ood`). The ground truth for what a run trained under is the logged per-run config, not the source tree (`resolved-config-is-source-of-truth`).

   Coach application. Whenever asked "why is lineage A better", require the resolved-param table first; kill zero-variance columns; refuse explanations phrased in eval-knob terms; when a knob helps, label it deployment mitigation.

   kd-bandwidth-mu-law-attributiondeploy-knob-attribution-before-retrainingcycle-time-override-is-oodresolved-config-is-source-of-truth

3. doctrine-3PASS gates become constraints; FAIL gates become objectives

   Once a skill passes its gate, that gate converts into a standing regression constraint (budget <= 2/20 against the parent baseline) for all later training; gates currently failing are the only legitimate objectives of the next rung.

   Case. The C ladder ran one frozen 13-cell x 20-seed matrix at every rung with promotion = "new skill PASS and old skills within regression budget"; C1 was stopped and re-rooted precisely because it trained away the root's backward PASS (`fixed-acceptance-matrix-per-rung`, `preregistered-stop-criteria-per-rung`). The C4 product shipped only at 260/260 cells with zero regression.

   Coach application. Keep the ledger: every PASS adds a constraint row; propose rungs only against FAIL rows; treat any constraint violation as stop-and-attribute, never "the next rung might win it back".

   fixed-acceptance-matrix-per-rungpreregistered-stop-criteria-per-rung

4. doctrine-4One variable per ladder rung - counted against what the checkpoint saw

   A rung changes one variable, where "one" is counted against the checkpoint's actual training state, not against the current config's diff; batching is allowed only when each change owns a disjoint symptom space with a pre-registered ablation order.

   Case. Two rungs failed identically because resuming s1e-500 under the evolved config silently added four plant variables the checkpoint had never seen ("单变量纪律不只看「我改了什么」,还要看「checkpoint 见过什么」" - `resume-state-dr-audit`). v8 legally batched four orthogonal fixes with a written ablation order (`orthogonal-batch-with-ablation-order`); v9 spent one run completing a 2x2 factorial so either outcome convicted a factor (`fill-the-missing-factorial-cell`); v10b's three-way ablation wrongfully convicted the clock and had to be retried fairly.

   Coach application. Before any resume: diff cfg against the checkpoint's logged training state. Before any batch: require the symptom-ownership map and ablation order in writing.

   resume-state-dr-auditorthogonal-batch-with-ablation-orderfill-the-missing-factorial-cell

5. doctrine-5Pre-register risks, readings, and stop criteria before the ladder

   Before a ladder or risky rung, write down the known risks, the interpretation of every plausible outcome, and hit-any-one stop criteria - frozen before training, tightened when priors say results should come fast.

   Case. The C ladder opened with three numbered risks including the exact falsification condition for its own root choice; A/B arms carried "预注册读法(事后不改)" tables; a level expected to fail was run anyway for its pre-registered diagnostic value (`preregister-risks-and-fork-readings`). Stop criteria caught C4-redo rungs at +200 instead of full caps (`preregistered-stop-criteria-per-rung`); hardware sessions pre-registered per-config expected signatures and the disagreement rule "不改结论改账" (`preregistered-real-expectations`, `feasibility-accounts-lock-design-point`).

   Coach application. Refuse to open a rung without the written risk/reading/ stop block; after results, read conclusions off the pre-registered table and flag any post-hoc reinterpretation.

   preregister-risks-and-fork-readingspreregistered-stop-criteria-per-rungpreregistered-real-expectationsfeasibility-accounts-lock-design-point

6. doctrine-6Plant parameters are measured, never invented

   Every plant number carries measurement provenance: armature = N^2 x rotor inertia from no-load tests, friction split by rig and by API column, torque limits shaped by per-joint gait peaks, latency traced through the real pipeline, masses weighed - and DR bands are additive around the measured nominal, sized to the measured dispersion.

   Case. Guessed friction was 2.5x low and guessed damping 5x high (`friction-measured-not-guessed`); armature had been 0 with a 9:1 gearbox (81x reflected inertia, `armature-n2-rotor-inertia`); a uniform torque derating was "the wrong shape" vs measured peaks (`torque-limit-shape-by-measured-peaks`); the delay implementation itself was a wrong plant for a whole lineage (`latency-lerp-reverse-extrapolation`); the run design point was locked by three accounts including the tau_limit/kd speed ceiling (`feasibility-accounts-lock-design-point`); identified friction had to land in the right simulator API columns to act at all (`sim-api-friction-columns`). The recovery and one-leg lines opened with the same kind of accounts before any reward existed - a connected static path and the torque along it for an armless get-up, and the gains single support needs to be holdable at all (`get-up-feasibility-accounts-before-training`, `single-support-gain-authority-probe`).

   Coach application. For any plant value in a config review, ask "measured how?"; reject absolute ranges with no nominal; check API column mapping and derived-asset regeneration whenever measured values land.

   friction-measured-not-guessedarmature-n2-rotor-inertiatorque-limit-shape-by-measured-peakslatency-lerp-reverse-extrapolationfeasibility-accounts-lock-design-pointsim-api-friction-columnsget-up-feasibility-accounts-before-trainingsingle-support-gain-authority-probe

7. doctrine-7Sim2sim gate before sim2real - under deployment conditions

   Every checkpoint passes a second, independently built simulator before hardware, and both the gate and the smoke loop run under the measured deployment conditions (real pipeline delay, honest contact parameters, the deployment gain/power profile).

   Case. The standing order "先sim2sim 再sim2real" (`sim2sim-gate-before-sim2real`); acceptance flipped to match hardware only under measured condim/torsional friction (`eval-plant-honesty-contact-params`); gates moved permanently to `--delay 2` after the kicking incident (`pipeline-latency-is-plant-not-dr`); and the harness itself must be audited - a frame-convention bug in the cross-sim evaluator invalidated a whole line of verdicts (`body-frame-velocity-api-audit`). The recovery line's second simulator caught a torque penalty paid for by bracing the legs together (`torque-penalty-bought-by-leg-bracing`), and a 1.8x torque disagreement between the two plants stayed binding because its one surviving explanation was never tested (`torque-disagreement-between-simulators-unresolved`).

   Coach application. Block any hardware request lacking a second-sim PASS at deployment conditions; when sim2sim and training-side metrics disagree, treat the evaluator as a suspect too.

   sim2sim-gate-before-sim2realeval-plant-honesty-contact-paramspipeline-latency-is-plant-not-drbody-frame-velocity-api-audittorque-penalty-bought-by-leg-bracingtorque-disagreement-between-simulators-unresolved

8. doctrine-8Observation honesty - the actor's inputs are a hardware contract

   The actor observes only signals the real robot produces with realistic noise; privileged truths go to the critic; history windows are estimators and must train under plant variation; rewards on quantities the actor cannot observe buy only average suppression, never closed-loop correction.

   Case. Ground-truth velocity/forces went critic-only (`observation-honesty-critic-only`); frame_hist under zero DR memorized the trainer's plant fingerprint - 0/20 transfer (`history-obs-needs-plant-variation`); world-frame yaw rewards could not teach pull-back because heading is unobservable to the actor - correction was routed to the deploy outer loop instead of breaking the contract (`reward-observability-limit`, `deploy-heading-loop-and-align-training`).

   Coach application. Audit every actor-obs element for hardware existence; require minimal plant jitter whenever history/recurrence exists; for each reward, ask "can the actor see this error?" and route correction tasks to outer loops.

   observation-honesty-critic-onlyhistory-obs-needs-plant-variationreward-observability-limitdeploy-heading-loop-and-align-training

9. doctrine-9Reward economics are audited in realized currency

   Reward design decisions are made on realized per-step magnitudes under the actual policy and command distribution: price the do-nothing optimum before adding a mode, compare achieved values to the computed ignore-floor, calibrate thresholds between measured healthy and sick distributions, and ship every new penalty with a withdrawal clause.

   Case. feet_air_time at weight 2.0 realized 0.038 vs tracking 1.2 - drag was rational (`realized-contribution-audit`); ignoring a vy command cost 28-180x less than ignoring vx until a gated tracking term was added (`reward-cost-of-ignoring-audit`, `gate-new-reward-terms-by-command`); achieved-vs-floor separated "never learned" from "priced out" (`ignore-floor-diagnosis`); the foot-distance wall was placed between measured healthy (0.6% tax) and sick (55%) policies (`calibrate-threshold-between-healthy-and-sick`); the landing penalty carried a pre-registered stand-down condition and actually stood down (`calibration-threshold-with-withdrawal-clause`); two clearance terms were inert until zero-points and gate occupancy were checked (`inert-reward-term-audit`). A get-up policy sat because three gated terms paid the seated pose 84% of the return and the one term that could tell sitting from standing was an exp kernel reading 4.6e-5 at the real error (`seated-basin-dead-exp-kernel`); a torque-tail term was weighted by its measured steady value beside a peer term after the estimate proved 12x off (`tail-torque-needs-hinge-on-computed-demand`).

   Coach application. Never discuss weights in the abstract: demand the realized-contribution table, the ignore-floor number, and the healthy-pay calibration before any reward edit is approved.

   realized-contribution-auditreward-cost-of-ignoring-auditgate-new-reward-terms-by-commandignore-floor-diagnosiscalibrate-threshold-between-healthy-and-sickcalibration-threshold-with-withdrawal-clauseinert-reward-term-auditseated-basin-dead-exp-kerneltail-torque-needs-hinge-on-computed-demand

10. doctrine-10The zero-cost option must be the desired behavior

    For every penalty, name what the zero-cost option is; penalize failure events (slip, saturation excess, contact in flight windows), never the motion or joints that healthy behavior uses; make degenerate strategies fatal via termination where penalties cannot price them out.

    Case. Joint-usage penalties for drift taxed a 1.4%-of-momentum channel 2.7/step and collapsed training; the slip penalty costs a non-slipping gait exactly zero (`penalize-the-slip-not-the-joint`). A frozen-at-clamp joint pays zero action-rate forever - only a pre-clip saturation penalty flips the cheat economics (`saturation-cheating-zero-rate-cost`). Ungated phase shaping made standing 42x more expensive than stepping and cooked the hip motors (`moving-gate-42x-stand-tax`); crouch-shuffling lived until a height termination deleted it (`termination-closes-degenerate-basin`). A gated penalty is an exit: the policy parked just outside an uprightness gate, then just under a height gate, to stop paying a stance tax, and only a positive band plus an always-on guard closed both (`penalty-gate-is-an-escape-hatch`); a soft-limit penalty that charged the standing pose itself bought a 4.1 deg lean (`soft-limit-penalty-charges-nominal-pose`); an unpriced foot attitude was spent on edge-standing (`unpriced-foot-attitude-is-a-free-variable`); and the one-leg line listed its cheapest cheats before training and still met one through a zero-gradient band (`enumerate-cheapest-cheats-before-training`, `binary-band-reward-fake-touchdown`).

    Coach application. Run the "零代价的选项是什么" audit on every proposed term; convert motion taxes into event-conditional penalties; check the termination set against each known degenerate strategy.

    penalize-the-slip-not-the-jointsaturation-cheating-zero-rate-costmoving-gate-42x-stand-taxtermination-closes-degenerate-basinpenalty-gate-is-an-escape-hatchsoft-limit-penalty-charges-nominal-poseunpriced-foot-attitude-is-a-free-variableenumerate-cheapest-cheats-before-trainingbinary-band-reward-fake-touchdown

11. doctrine-11Measurement discipline: independent referees, signs, distributions

    A disputed measurement is adjudicated only by an independent algorithm from raw state; directional ability requires sign-antisymmetry under command reversal; bimodal metrics are reported as mode shares (never medians, never 3 seeds); ratios are not comparable when totals change; reward values compare only within one command distribution; single chaotic events never cross machines.

    Case. The triple reversal - a good metric was "refuted" by a sibling metric that shared the disease (`independent-referee-for-metric-disputes`, `body-frame-velocity-api-audit`); same-signed +/- responses were bias, not turning (`same-sign-response-is-yaw-bias`); the swing median sat in a bimodal gap (`median-hides-bimodal-distribution`); "v6 is jitterier" died on absolute energies (`ratio-metrics-need-absolute-check`); yaw gain measured 15x wrong in an oscillating frame (`heading-integral-not-body-rate`); a 44% improvement evaporated under same-distribution comparison (`same-distribution-reward-comparison`); drift direction was a limit cycle (`multiseed-sign-test-for-drift`); a cross-machine push cliff was chaos (`single-impulse-recovery-is-chaotic`).

    Coach application. Before accepting any surprising number: ask for the independent recomputation, the sign pair, the distribution shape, and the comparison conditions. Retract in writing when a metric falls.

    independent-referee-for-metric-disputesbody-frame-velocity-api-auditsame-sign-response-is-yaw-biasmedian-hides-bimodal-distributionratio-metrics-need-absolute-checkheading-integral-not-body-ratesame-distribution-reward-comparisonmultiseed-sign-test-for-driftsingle-impulse-recovery-is-chaotic

12. doctrine-12The deployment pipeline is plant

    Irreducible pipeline properties - action latency, rate limits, power/torque scaling, teleop command mappings - are part of the nominal plant, modeled from day one and reproduced in every gate; deploy-side scalings are crutches that flag unmodeled plant, and they cannot be algebraically folded into training constants.

    Case. Right-leg kicking was over-trained-delay x loop gain; power 0.8 was a gain-reduction crutch that retired when the delay was modeled (`pipeline-latency-is-plant-not-dr`); power derating damages non-forward axes first (`power-scale-hurts-nonforward-axes`); training at 0.4 scale as the "twin" of deploying 0.5 x 0.8 collapsed 0/20 (`deploy-scaling-not-training-equivalent`); one shared teleop speed sent an out-of-band lateral command and the robot clipped its own foot (`teleop-command-band-per-axis`); the latency DR range had not even covered the measured pipeline (`latency-dr-covers-measured-pipeline`). A rate limiter added at deployment only clipped a policy that kept commanding (`deploy-rate-limiter-windup`); moved into training and anchored on the last command it became an integrator in the balance loop (`slew-anchor-is-an-integrator`); anchored on the measured angle it bounded torque and kept the bandwidth (`beta-anchored-action-target`). The walking lines' safe setting, power-scale 0.8, cut the ends of the recovery policy's full-range travel and left its spikes alone; a gain inside the trained band did the job (`power-derating-cuts-full-range-contract`).

    Coach application. Demand the measured pipeline latency/limits in the plant model and in gate conditions; treat every deploy-side derating as a question ("what is this compensating?"); block per-axis command sources that exceed training bands.

    pipeline-latency-is-plant-not-drpower-scale-hurts-nonforward-axesdeploy-scaling-not-training-equivalentteleop-command-band-per-axislatency-dr-covers-measured-pipelinedeploy-rate-limiter-windupslew-anchor-is-an-integratorbeta-anchored-action-targetpower-derating-cuts-full-range-contract

13. doctrine-13DR budget is finite; its distribution is the measured support

    Robustness is a conserved budget: disturbance training on an already-hardened lineage borrows from existing margins; DR ranges span the measured deployment support - no fictitious tails (they buy degenerate gaits), no single constants (they allow thin-margin specialization); harden the plant only after the task distribution is final.

    Case. The same push dose helped a narrow lineage and damaged a balanced one - budget conservation (`push-dr-conditional-budget-conservation`); wide latency tails bought drag-glide, constant values shipped 60% thinner tilt margins - the answer is a narrow band on the measured support (`dr-tail-plant-continuation`, `constant-value-dr-overfits-margin`); task-first ordering because hardening a soon-to-change task wastes budget (`task-shaping-before-plant-hardening`); COM randomization used deliberately as a behavior-shaping tool, and rolled back on symptom per its own contract (`com-randomization-forces-leg-spread`, `com-dr-rollback-on-symptom`). DR that is switched on can still be thin: the run policy fell in the frontal plane its gain-and-latency randomization never touched (`thin-dr-judged-by-channel-coverage`), and a friction priority settled under one action contract had to be re-measured under the next (`friction-priority-re-measured-after-plant-change`).

    Coach application. Before any DR rung: check the untrained policy against the spec, the lineage's current DR load, and the measured real-world range; after it: audit retained margins, not just the new tolerance.

    push-dr-conditional-budget-conservationdr-tail-plant-continuationconstant-value-dr-overfits-margintask-shaping-before-plant-hardeningcom-randomization-forces-leg-spreadcom-dr-rollback-on-symptomthin-dr-judged-by-channel-coveragefriction-priority-re-measured-after-plant-change

14. doctrine-14Gates measure what hardware feels: posture, margins, stripped assists

    Acceptance batteries carry posture-class rows (tilt max median, per-joint L/R asymmetry, temperature) beside task rows, graded margin columns beside binary gates, chirality scored per side, at least one condition that removes the environment's free stabilization, and validated predictive scalars promoted into the gate.

    Case. Three same-shaped judging errors - survival, displacement, wz-difference - all missed what the operator felt; posture metrics had the predictive power (`task-metrics-vs-posture-metrics`, `stand-gate-posture-not-survival`); binary survival saturated and hid a 60% margin gap (`constant-value-dr-overfits-margin`); v5 passed everything on the ground and failed suspended (`suspension-probe-removes-free-stabilizer`); the hip_roll (l+r) scalar predicted real drift direction and ordering and entered the battery (`hip-roll-sum-predicts-lateral-drift`); averages hide chirality (`chirality-scored-separately`); gait-quality gates are judged at speeds that demand a gait (`low-speed-commands-reward-dragging`). The recovery line added the rest of the kit: where failed episodes end, not only where they started (`end-state-confusion-matrix`); a frozen acceptance distribution with a pinned seed (`frozen-acceptance-distribution-and-pinned-seed`); video of the metric rollout itself (`video-as-acceptance-record`); and the admission that a 10 s episode cannot see a stance that fails after a minute (`episode-length-bounds-what-a-gate-sees`). The one-leg line removed a foot-spacing wall that no gate measured, and the feet met on hardware (`removed-wall-returns-on-hardware`).

    Coach application. Review every battery for posture rows, margin columns, per-side scoring, and an assist-stripped condition; when operator feel and gates disagree, suspect the metric class first.

    task-metrics-vs-posture-metricsstand-gate-posture-not-survivalconstant-value-dr-overfits-marginsuspension-probe-removes-free-stabilizerhip-roll-sum-predicts-lateral-driftchirality-scored-separatelylow-speed-commands-reward-draggingend-state-confusion-matrixfrozen-acceptance-distribution-and-pinned-seedvideo-as-acceptance-recordepisode-length-bounds-what-a-gate-seesremoved-wall-returns-on-hardware

15. doctrine-15Fork and root selection: recoverability, maturity, frozen rewards

    Choose fork roots by which candidate's deficits the coming training can pay back (precision is recoverable; lost plasticity, symmetry, and margins are not); prefer mature checkpoints as roots even when younger ones score better as products; never fine-tune through a reward change - continuation is legal only with the reward frozen and plant/DR widening one rung at a time.

    Case. s1e-500 beat higher-precision candidates because its exclusive strengths were unrecoverable (`fork-root-recoverable-shortfall`); the b300 arm proved maturity is capital against adaptation shock (`root-maturity-vs-product-quality`); the B-arm scatter/half-recover/collapse signature falsified reward-change fine-tuning and drew the legal boundary for S2 continuation (`fine-tune-reward-change-falsified`).

    Coach application. For root debates, build the exclusive-strengths table and ask "which side can be trained back?"; require dual-arm evidence for maturity claims; classify any proposed continuation as reward-frozen or not before approving.

    fork-root-recoverable-shortfallroot-maturity-vs-product-qualityfine-tune-reward-change-falsified

16. doctrine-16Curricula: verified engagement, lineage counters, disease-phase gating

    Automatic curricula must prove they engage (a saturated ratchet is constant DR wearing a curriculum's name); every ramp counts lineage-cumulative progress, not per-process steps; penalties aimed at late-stage pathologies ramp in after exploration noise decays; difficulty rises on measured per-stratum success, never on schedule.

    Case. The s1f ratchet capped at iter 248 and never engaged (`auto-curriculum-engagement-check`); the saturation ramp re-fired at +600 after every resume and no shipped product ever saw the penalty (`curriculum-counter-lineage-steps`); the same penalty worked once gated to the disease phase and became an untouchable mechanism (`gate-penalties-to-the-disease-phase`); record-high aggregate reward hid a fully-failing delay stratum (`aggregate-metrics-mask-subgroup-failure`); bucket share is not a gradient lever (`bucket-share-is-not-a-gradient-lever`). An assist curriculum keyed to a pooled success share was withdrawn on the strength of the categories that already worked (`curriculum-criterion-conditioned-on-lagging-category`); a pace set by per-step income moved only when that income was time-gated (`per-step-income-drives-speed-time-gate`), and the same gate had to be retired in a lineage without the disease (`time-gate-vs-wide-stance-retire-the-fix`).

    Coach application. Ask every curriculum three questions: does it engage (show the internal state)? what does it count (process or lineage)? when is it present (against the pathology's phase)? Check where shipped checkpoints sit relative to every ramp.

    auto-curriculum-engagement-checkcurriculum-counter-lineage-stepsgate-penalties-to-the-disease-phaseaggregate-metrics-mask-subgroup-failurebucket-share-is-not-a-gradient-levercurriculum-criterion-conditioned-on-lagging-categoryper-step-income-drives-speed-time-gatetime-gate-vs-wide-stance-retire-the-fix

17. doctrine-17Probe before training: feasibility first, hypotheses in tables

    After two failed training attempts at a skill, stop training: demonstrate the behavior open-loop, enumerate hypotheses in a written table audited against actual configs cheapest-first, race one probe per side of the sim2real boundary for hardware-only pathologies, and use suspended tests to acquit or convict actuators before blaming authority.

    Case. "在黑暗里试钥匙" - four sidewalk rungs failed until an open-loop probe separated exploration/waveform/authority in one experiment (`open-loop-probe-before-reward-tuning`); the foot-drag mystery fell to a seven-hypothesis config audit (`hypothesis-table-code-audit`); the period-doubling was resolved by racing a reward-side and a plant-side evidence line - and both paid off, one per sub-case (`period-doubling-evidence-race`); the suspended test acquitted the roll actuator in one measurement (`suspended-test-isolates-actuator-authority`). A read-only configuration probe told a wall from a slope in the recovery line's seated basin (`configuration-probe-wall-not-slope`), and the fix it pointed to - where the feet are - took prone from 0/159 to 158/159 (`prone-dead-end-is-foot-placement`); a knob that did not move its variable was recorded as no test of the idea (`dof-vel-penalty-is-not-a-pacing-knob`).

    Coach application. When a skill resists training, prescribe the probe before any further reward edits; require verified target trajectories before imitation terms; keep a falsified-fixes list so closed roads stay closed (`amplitude-cut-falsified-yaw-fix`).

    open-loop-probe-before-reward-tuninghypothesis-table-code-auditperiod-doubling-evidence-racesuspended-test-isolates-actuator-authorityconfiguration-probe-wall-not-slopeprone-dead-end-is-foot-placementdof-vel-penalty-is-not-a-pacing-knobamplitude-cut-falsified-yaw-fix

18. doctrine-18External advice is recomputed locally; values transfer as ratios

    Every external suggestion is classified adopt / already-have / modify / trap by recomputing its claim on the local reward table and probe data; numeric values transfer only as dimensionless ratios (to tracking weight, leg length, sqrt(gL), control rate); citations are verified to exist.

    Case. "Start vy very small" would have destroyed sidewalk learning on this reward table - the gradient scales quadratically (`external-advice-audit-against-own-arithmetic`); swing-height targets and weights transferred correctly only through leg-length and tracking-ratio scaling (`transfer-ratios-not-absolutes`); the "6-step delay" was refused for lacking a control rate (`latency-dr-covers-measured-pipeline`); a borrowed reference's structure was FK-verified and its amplitude re-derived from the division of labor (`reference-structure-fk-amplitude-division`); retrieval agents fabricated verbatim arXiv quotes - only source-verifiable material was used; and one dismissed suggestion later proved right for a different mechanism, and was credited (`cycle-average-tracking-for-gait-quantities`). An advisor's staged state machine turned out to exist in none of the three papers it cited, and reading them changed the plan (`advisor-paraphrase-vs-paper`).

    Coach application. Intercept every "paper X does Y" with the local recomputation; convert absolutes to ratios before comparison; verify quotes; revisit dismissed advice when new mechanisms appear.

    external-advice-audit-against-own-arithmetictransfer-ratios-not-absoluteslatency-dr-covers-measured-pipelinereference-structure-fk-amplitude-divisioncycle-average-tracking-for-gait-quantitiesadvisor-paraphrase-vs-paper

19. doctrine-19Hardware sessions are scripted experiments, not tuning sessions

    Real-robot time executes a pre-registered matrix: risk-ordered (baseline first, fragile last with a spotter), stage-gated (suspended smoke before ground), A/B sessions bracketed by a repeated reference run, operators briefed on measured zero-command and untrained-axis behavior, chirality-aware disturbance protocols, no field tuning - the only legal field changes are scripted, single-variable, and self-reversing.

    Case. The S2 acceptance sheet (`risk-ordered-real-deployment`, `battery-bracketed-real-ab`, `know-zero-command-behavior`, `push-test-chirality-protocol`, `no-field-tuning-protocol`); the RAM-only torque experiment with automatic power-cycle rollback (`reversible-single-variable-field-experiments`); and the sim-veto rule - even sim's condemnations get one safeguarded hardware check when they judge the purpose-built configuration (`sim-veto-needs-real-confirmation`). The recovery line's first real run went ahead with its preconditions unmet and was stopped as dangerous (`first-real-get-up-violent-stage-one-policy`); after it: a staged hang, mat and floor protocol (`staged-hang-mat-floor-for-get-up`), a fixed power-cycle pre-flight and two-machine discipline (`power-cycle-preflight`, `two-machine-config-discipline`), a fall guard replaced rather than switched off (`fall-guard-becomes-a-state`), and logs that are part of the run (`hardware-log-is-the-attribution-input`).

    Coach application. Turn every hardware request into a runbook with order, gates, brackets, briefing, and anomaly plays; refuse improvised parameter changes on the floor.

    risk-ordered-real-deploymentbattery-bracketed-real-abknow-zero-command-behaviorpush-test-chirality-protocolno-field-tuning-protocolreversible-single-variable-field-experimentssim-veto-needs-real-confirmationfirst-real-get-up-violent-stage-one-policystaged-hang-mat-floor-for-get-uppower-cycle-preflighttwo-machine-config-disciplinefall-guard-becomes-a-statehardware-log-is-the-attribution-input

20. doctrine-20Close questions in writing; restart when the debt is structural

    Audited questions get frozen verdicts with citable wording and an explicit reopening bar; hardware verdicts are dated by deployment-stack and calibration state and expire when those change; and when successive rungs shuffle symptoms without net progress, freeze the lineage as regression baselines, pay the structural debts, and retrain minimal - carrying laws and instruments, not weights.

    Case. The chirality and COM questions were closed with frozen wording and "no reopening without new hard evidence" (`frozen-verdicts-semantic-boundaries`); v5/v6's condemnations expired with the deploy stack (`stale-verdicts-under-old-stack`); a 2-degree calibration fix moved the whole runnable envelope (`zero-offset-calibration-shifts-envelope`); plant upgrades are era boundaries with paired re-baselining (`plant-swap-invariants-vs-shifts`); and the 2026-08-05 reset froze v5-v11, fixed the latency FIFO / manifest / sampling / reward-table debts, and restarted - producing the lineage that reached hardware SOTA (`freeze-lineage-fix-structure-restart`, `minimal-reward-table-with-provenance`). The recovery line's real-robot verdicts ended up in three places that disagree, one of them an undated note in a command file (`write-hardware-verdicts-back`).

    Coach application. Maintain the closed-questions ledger and quote it when symptoms recur; stamp verdicts with stack/calibration versions; when a team is three rungs into symptom-shuffling, raise the restart question explicitly with the freeze-fix-restart pattern.

    frozen-verdicts-semantic-boundariesstale-verdicts-under-old-stackzero-offset-calibration-shifts-envelopeplant-swap-invariants-vs-shiftsfreeze-lineage-fix-structure-restartminimal-reward-table-with-provenancewrite-hardware-verdicts-back

21. doctrine-21Name the quantity in the space it lives in

    A goal, reward term or acceptance criterion about the feet, the base or the contact state is computed from the quantity itself - world poses, forces, per-category outcomes - never through a joint-angle, single-signal or pooled stand-in that assumes everything else sits at nominal; and every detector is validated on a behaviour known not to contain the event before it becomes a gate.

    Case. The recovery line was caught three times: |ankle roll| as "flat feet" sold stance width and the real robot slid into the splits, a hip-roll criterion was confounded by 50 deg of yaw, and the joint table said 0.271 m where the feet were 0.159 m apart; task-space terms produced the first flat, wide stance (`joint-space-proxy-for-task-space-quantity`). Flight detection lied in both directions across two lines - foot height flagged 40% false flight on a walking gait, contact force alone flagged slip chatter as hops (`contact-detector-single-signal-lies`). A pooled height average described a robot that did not exist - six in ten standing, four in ten sitting (`zero-partial-credit-is-not-an-iteration-problem`) - and the walking line had learned the same lesson on yaw rate (`heading-integral-not-body-rate`).

    Coach application. For every reward term and gate row, ask what physical quantity it stands for and whether it is measured directly; flag joint-space or single-signal stand-ins for task-space goals, ask for a detector validated on a negative control, and split pooled metrics by category before reading them.

    joint-space-proxy-for-task-space-quantitycontact-detector-single-signal-lieszero-partial-credit-is-not-an-iteration-problemheading-integral-not-body-rate

22. doctrine-22Continuation needs a live gradient; a release is chosen by a scan

    Continue a converged policy only on a change that creates a live gradient, on a short budget, with every checkpoint scanned on the transfer axis; choose a release by running the full battery over a band of checkpoints and stop on signals, never by taking the last one; and when edits to the terminal phase cannot move a behaviour, roll back and retrain with the constraint present from the start, keeping the order in which the lineage acquired its mechanisms as explicit curriculum phases.

    Case. A continuation with no new gradient drifted MuJoCo transfer from 100/98% to 80/28% while every Isaac gate stayed perfect, and a live-gradient continuation at the same depth kept it (`converged-continuation-is-poison`). One-leg checkpoints 100 iterations apart failed 1 and 38 of 40 cells, and late ones degraded (`checkpoint-choice-is-a-full-gate-scan`). Four in-lineage stance fixes failed because the stance was the end of the get-up path, and from scratch it grew right (`stance-decided-by-get-up-path`); fixes stacked on degraded states were rolled back by the user (`stop-stacking-roll-back-and-audit`); and the lineage's final recipe, trained from scratch in one run, sat at 0% because the order of its curriculum was part of the product (`curriculum-history-is-part-of-the-product`). The omni line's short adaptation budgets and mature roots are the same law seen from the other side (`continuation-budget-not-from-zero`, `root-maturity-vs-product-quality`).

    Coach application. Before approving a continuation, ask for the new gradient, the budget and the transfer axis in the scan; before approving a release, ask for the scan; after three rungs without progress on the target, propose rolling back to the last good checkpoint and a from-scratch phase plan instead of a fourth patch.

    converged-continuation-is-poisoncheckpoint-choice-is-a-full-gate-scanstance-decided-by-get-up-pathstop-stacking-roll-back-and-auditcurriculum-history-is-part-of-the-productcontinuation-budget-not-from-zeroroot-maturity-vs-product-quality

Experience cards

86 cards matching “soft-limit-penalty-charges-nominal-pose”.

• A soft joint-limit penalty charged the standing pose itself - the geometric-zero knee sat on its hard limit, so stand_v1 bent its knees to dodge 0.419 per step and leaned 4.1 deg forward; excluding the knee gave 0.24 degsoft-limit-penalty-charges-nominal-pose

  Mechanism understoodstandreward-shapingreward-shapingplant-calibration

  Before training, evaluate every penalty at the nominal pose; if a joint's soft limit sits inside the pose the task requires (a straight knee on its hard stop), exclude that joint from the soft-limit penalty and let the action clip enforce the hard limit.

  Symptom

  stand_v1 (retrained after the default pose moved to the CAD geometric zero and mirror augmentation was added) fixed left/right asymmetry (6.8 -> 0.0 deg) but settled at a 4.1 deg forward lean, where pure PD at the same default settled at 0.1 deg - the policy was actively pushing itself forward, which is exactly the real robot's failure direction.

  Context

  soft_joint_pos_limit_factor = 0.9 shrank the knee's soft limit to -/+0.1047 rad, while the geometric-zero default has the knee at q = 0, exactly on the hard limit. Standing in the nominal pose therefore paid 0.2094 x 2.0 = 0.419 per step in dof_pos_limits (alive earned only 0.5). The policy's way out was to bend the knees to -/+0.1013 rad, and the cost was the forward lean.

  Change

  stand_v1b: the knees excluded from dof_pos_limits (a straight knee IS the standing pose; the hard limit is still enforced by the action clip). No other change.

  Outcome

  stand_v1b: max tilt 0.3 deg, steady tilt 0.24 deg, asymmetry 0.1 deg, height 0.384 m - exactly nominal - with knees at -0.0007 / +0.0005 rad. It became the standing release used on the real robot, and later the standing side of the recovery switch. The same exclusion was carried into the recovery contract (knee at the clip in the standing pose) and the one-leg reward table.

  Mechanism

  A limit penalty whose soft boundary lies inside the nominal pose turns the nominal into a taxed state, and the policy buys its way out with whatever posture change is cheapest - here a knee bend paid for with lean.

  Applies when

  • a standing or default pose has a joint at or near its hard limit
  • a policy settles in a small steady tilt that pure PD does not show
  • soft-limit factors shrink limits uniformly across joints

  “`soft_joint_pos_limit_factor=0.9` 把膝软限位内缩到 ∓0.1047,而几何零位 default **膝盖 q=0 正好压在硬限位上** ⇒ 站在标称姿态每步白扣 `0.2094 × 2.0 = 0.419` (alive 才 +0.5)。策略只能屈膝到 ∓0.1013 躲罚,代价是躯干前倾 —— 恰好是真机的 失效方向。 … 修掉"软限位罚标称姿态"后重训(`dof_pos_limits` 排除膝盖)。**前倾问题彻底消失** … 不再屈膝躲惩罚,高度正好落回标称 0.3840。”

• The standing-pose reward had been pulling toward the narrow stance the whole line was fighting - a zero-training kinematic audit of the target vector found it, after first auditing the wrong nominalpose-target-geometric-audit

  Mechanism understoodrecoveryattributionreward-shapingattributionplant-calibration

  Before training on a pose target, audit it with forward kinematics - is it geometrically consistent (feet flat, intended stance, intended width) and is it the frame you think it is (action nominal vs standing default)? A posture term's target may itself be the attractor you are fighting.

  Symptom

  Several rungs aimed at widening the stance failed; the stance stayed narrow as if something kept pulling it back.

  Context

  The audit (MuJoCo forward kinematics, no training): every 5 deg of hip roll widens the stance ~5.5 cm (0.271 m at 5 deg, 0.383 m at 15 deg); at 47 deg of hip yaw a wide stance cannot be flat-footed (residual foot tilt ~0.7 x hip roll), which explained the stalled rungs. The first report also said the stand_pose nominal (hip roll 25, knee 60) has a 63 deg residual foot tilt - but that was the action frame's nominal (the limit-midpoint squat), which stand_pose never used, despite a docstring warning not to mix them. stand_pose's real target was DEFAULT_JOINT_POS: the contract's all-zero pose, legs parallel, ~0.22 m apart.

  Change

  The disease statement was corrected in writing: the narrow stance was not an accidental by-product of proxy traps but the target stand_pose had been actively rewarding. A stored "narrow the stance" knife was marked toxic. V2.8 moved the target to a flat 15-deg stance (sigma 3 -> 1.5, flat_feet margin 5 -> 20 deg).

  Outcome

  V2.8 still failed in-lineage (stance unchanged, feet nearly overlapping, mu 0.4 transfer 2%) - see stance-decided-by-get-up-path - and the from-scratch V3.1 removed both roll joints from stand_pose and put width into a task-space term, which is what finally produced a 0.355 m flat stance. The 63 deg finding was kept as a warning: an action nominal used as a standing target would be a ready-made pit.

  Mechanism

  A posture term with a sharp kernel around the wrong target is an active attractor; every other term fighting it pays twice.

  Applies when

  • a posture keeps returning despite penalties against it
  • a reward uses a default or nominal pose as its target
  • the contract has more than one "nominal" (action frame vs standing pose)

  “上文"stand_pose 的 nominal (hip25/knee60) 残倾 63°"**审计错了对象**:那是 **动作参考系 nominal**(限位中点蹲),stand_pose 从未指向它(函数 docstring 原文即警告"两者别混",还是混了 —— 记档)。 … **修正后的病根陈述:窄站距不是代理陷阱的意外副产物,而是 stand_pose 一直在主动奖励的目标本身**”

• Fix a too-deep nominal pose before adding any penalties - the default stance defines the basin training starts innominal-posture-before-penalties

  Mechanism understoodwalkreward-shapingreward-shapingplant-calibration

  Before tuning penalties on a degenerate gait, audit the nominal pose and height targets against morphology and published ratios; if the default stance encodes the degenerate behavior, fix it first - and recompute dependent quantities (init height) by FK, not by hand.

  Symptom

  Policy lived in a crouched shuffle; nominal knee angle was 0.5 rad (28.6 deg) - deeper than published configs (Unitree G1 0.3 rad / 17.2 deg, Booster T1 0.4 rad) - so the policy's starting point and its action-space center both sat inside the crouch basin.

  Context

  Initially ranked "secondary" in the local diagnosis, this was promoted to co-first priority by the cross-check against published reward tables, which states that with nominal knee flexion above ~0.4 rad, fixing the posture must precede adding any penalties ("改这个之前别加任何 惩罚都是白费"). Companion base-height items: walk profile had weakened base_height_l2 to -5.0 (base class -10, field standard -10 to -20, "the second most common cause of death"), and the height target must be the STANDING height (0.384), not the crouch height.

  Change

  Nominal knee 0.5 -> 0.3 rad with init_base_height recomputed by MuJoCo FK (0.3739 -> 0.3802); base_height_l2 restored to -10 with standing height target; both bundled as first-priority alongside the clearance term.

  Outcome

  Part of the v5/v6 package that lifted swing height to 34 mm and tracking to 87%; the crouch basin stopped being the default answer.

  Mechanism

  The nominal pose is the fixed point every regularizer pulls toward and the point where action=0 lands; if that point is itself the degenerate posture, every penalty fights the geometry. Correcting the attractor is prior to shaping the gradient field around it.

  Applies when

  • policy converges to a crouched or collapsed posture
  • nominal joint angles were chosen for stability rather than gait
  • base-height reward targets or weights were locally weakened

  “研究明确说"nominal 膝屈超过 ~0.4 rad 必须先改,改这个之前别加任何惩罚"。我们是 0.50,超标。… base_height_l2 在 walk profile 里被减到 −5.0(基类是 −10)。研究说这是"第二常见死因"且应 −10 ~ −20。改回 −10。目标高度用站立高 0.384 是对的(研究要求 target 必须是*站立*高度而非蹲姿)。”

• A stronger action_rate penalty cut the median torque demand under the gate and left the p99 at 4x the limit - only a hinge on the pre-clip (computed) torque, weighted by comparison with a peer term, collapsed the tailtail-torque-needs-hinge-on-computed-demand

  Mechanism understoodrecoveryreward-shapingactuator-modelingreward-shapingmeasurement

  Judge actuator demand against the deployed limit, read the pre-clip demand (applied torque is censored and gives no gradient on the excess), use an L2 rate penalty for the median and a thresholded hinge on computed demand for the tail, and set a new term's weight from its measured steady magnitude next to a peer term rather than from a back-of-envelope estimate.

  Symptom

  After R0.5 hip_pitch delivered torque sat at its 12 N*m limit in a typical get-up (demand 119-125% of the limit, p99 4.2x) - zero control margin at exactly the moment modelling error matters.

  Context

  The 12/17/11 N*m limits are deployment limits written into robot.yaml by set_torque (RS06 at 33% of rated), and simulation uses the same effort_limit - so the gate is judged against them, not the 36 N*m rating (an early reading against the rating was retracted). Applied torque is clipped at the limit - censored data - so demand must be read from computed_torque. With the full-range action contract (hip_pitch scale 1.309, kp 30) a single-step action change of 0.306 already saturates hip_pitch, and action_rate penalizes exactly that change.

  Change

  R3.0: action_rate_l2 -0.01 -> -0.03 (child-run). R3.1: new torque_headroom = sum relu(|tau_computed|/limit - 0.9)^2, normalized so three motor types share a scale. Its weight was first estimated at -0.1, measured in a 12-iteration run at an effective -0.019 (12x smaller - the estimate had mixed a per-episode-peak p99 with a per-step p99, and at 1% of upright it would have been numerically absent), and set to -0.5 so its steady value (-0.095) matched action_rate's (-0.097).

  Outcome

  R3.0: sum |da|^2 -64%, success 99.6 -> 100%, delivered median = demand median (the clamp no longer fired in a typical episode), gate PASS at worst 79.9% - but p99 unchanged (hip_pitch 419-432% -> 427-436%). R3.1: p99 hip_pitch -> 148-189% (-57 to -65%), knee 422-439% -> 233-234%, saturation duty -60%, success 100%; the worst joint became hip_roll at 67.7%. Its cost appears in torque-penalty-bought-by-leg-bracing.

  Mechanism

  A squared-rate penalty presses the whole-episode sum and moves the typical step, not rare spikes; the spikes came from the kp term (large targets while a limb is blocked by the ground - velocity alone could not reach them under vel_limit), and a penalty on applied torque cannot see demand above the clip because every excess sample reads as exactly the limit.

  Applies when

  • torque demand saturates actuator limits in high-effort skills
  • a smoothness penalty improves medians but not peaks
  • a new reward term's weight is set by estimate alone

  “**必须用 `computed_torque` 而不是 `applied_torque`**:后者被 `effort_limit` 削平, 是删失数据,超限样本全被压成"恰好等于限",对超限部分梯度恒为 0。 … 改按同侪定标取 **−0.5**(稳态 ≈ −0.095,与 `action_rate_l2` 的 −0.097 等量)。”

• A training-side rate limit anchored on the last commanded target is an integrator inside the balance loop - two unrelated lineages converged to the same 34-43% re-fall rate, a soft penalty could not fix it, and the bandwidth arithmetic said safety and standing could not coexistslew-anchor-is-an-integrator

  Mechanism understoodrecoverytraining-runactuator-modelingcontract-freezeattribution

  If you constrain actions in training, anchor the constraint on the measured state, not on the previous command - a limiter with memory adds lag inside the balance loop; and when two different lineages converge to the same failure rate, treat the cause as structural and stop adding soft penalties.

  Symptom

  With the rate limit moved into training (V1), policies either could not stand up or stood up and kept falling again: the stand oscillated, fell and climbed back, 34-43% of the time.

  Context

  V1.0-B (user decision: explore new postures from scratch, hard constraint in training): target <- prev + clip(target - prev, +/-S*dt) at the TIGHT rates, anchored on the last issued target like the bridge. Four runs: v1_0 from scratch 0.8% - every category righted under the limit, then knelt (the limit also damped the exploration that had escaped the seated basin in V0); v1_0c continued from R3.1 99.8% get-up but 36-43% re-fall and knee jitter 0.604 rad/s; v1_0p with a pull-assist curriculum (56 N -> 0, fully withdrawn) 39.1% unassisted, re-fall 34-42%; v1_0w with a windup-gap penalty 25.4%, re-fall 18-43%. Removing the limiter from v1_0p gave 0.0%: the policy had co-adapted with it.

  Change

  The rate-limit route was declared dead after four runs and the line was re-rooted on a beta-anchored action space (V2, user approval required).

  Outcome

  V2.0's first acceptance at full authority already showed re-falls of 0-1% (V1: 34-43%), the structural bet paying off before any tuning.

  Mechanism

  Anchored on the previous command, a saturated policy becomes a rate controller - one more integrator in the loop - and active balance through that lag oscillates, while a kneeling sit needs no active control and is stable. The arithmetic: kp 30 needs 0.4 rad of error for 12 N*m; at 4 rad/s that takes 0.1 s, half the pendulum time constant sqrt(0.38/9.8) ~ 0.2 s; keeping standing bandwidth needs S of at least ~8 rad/s, within 20% of the 10 rad/s velocity limit - no bound at all. Anchoring on the measured angle (q + beta*a) makes the full kp*beta authority available in one step, with no memory, and caps the impact at the same time.

  Applies when

  • adding rate limits, slew limits or target filters to a policy's action path
  • a policy stands but oscillates and re-falls after a constraint was added
  • different lineages or curricula land on the same failure signature

  “v1_0p(拉力课程):会站(prone 79.9%),再摔 34~42% —— **两条完全不同 血统、不同学习路径,收敛到同一失败率**。 … 动作饱和时它退化为 速率控制 = 环内多一个积分器;主动站姿平衡穿过该滞后必振荡(v1_0 的跪坐不需 主动控制,所以稳)。对照:**HoST 的 β 锚在当前实测 q,无记忆、无积分器**”

• Every gate on a penalty is an exit - to stop paying a stance tax the policy parked 2 deg outside a 30 deg uprightness gate (lunging), and, from scratch, just under a height gate (crouching); a positive band and an always-on guard fixed bothpenalty-gate-is-an-escape-hatch

  Replicatedrecoveryreward-shapingreward-shapinggate-battery

  Never gate a penalty on a state the policy can leave by getting worse; use always-on guards for what must never happen and positive, gated bands for what you want, and count every gate on a penalty as one more escape route to check in the logs.

  Symptom

  V2.9 added stance_width_task = relu(0.34 m - foot spacing) x standing gates, weight -10. Three checkpoints scored 0% on acceptance: standing height reached, feet on the ground, angular rate low, but the torso leaned 32.5/32.3/31.9 deg in a fore-aft lunge. The training dashboard read "tax paid off, base_height at full value".

  Context

  The penalty was gated by uprightness (tilt < 30 deg) and standing height; its tax had no time gate (500 steps x -1.65) while the standing income sat behind a 3 s zero gate (~325 steps), so leaning just past 30 deg lost a little gated income and saved the whole tax. base_height has no upright gate, so the lunge still collected it. The first metric also measured full horizontal spacing, so a staggered lunge counted as "wide".

  Change

  Two laws written down: a penalty may only carry gates the policy cannot escape by getting worse (make it an always-on guard) or it becomes a positive band ("not earned" is not "escaped"); and width is measured laterally in the base yaw frame. From scratch (V3.1 P1) with the lateral metric but the same gates, the policy parked just under the height gate instead (base_height 1.176/1.5, h ~ 0.30 m against a 0.3264 gate), the beta curriculum never advanced in 1,700 iterations, and the run was stopped early. P1b flipped the penalty into a positive band +2.0 x clamp(lateral / 0.34) x standing gates; P1c added yaw_guard = -5 x relu(|hip_yaw| - 30 deg), always on, no gate, no exemption.

  Outcome

  P1b: lateral stance 0.364 m, all four categories 100%, MuJoCo mu 1.0/0.4 both 100%. P1c: all six acceptance criteria passed for the first time on the line (hip-yaw saturation 1.2%), the guard's tax converging to -0.016 (almost never touched).

  Mechanism

  A gated tax that is not paid is saved, so the policy moves to the cheapest state just outside the gate; a gated income that is not earned is simply lost, so a positive band has no exit. HoST's style penalties are ungated or binary - the same law seen from the other side.

  Applies when

  • adding a penalty multiplied by an uprightness, height, phase or contact gate
  • a policy settles just beyond a gate threshold
  • training metrics look paid-up while acceptance collapses

  “**罚项的门 = 策略的逃生门**。带直立门的负项可以靠"变得更差"(倾出门外) 全时免税;HoST 的 style 罚全部无门控/二值恰是同一律的反面实证。修律: 负项只许挂"变差逃不掉"的门(上限护栏 always-on),或改正向 band (收不到 ≠ 逃掉)。”

• Calibrate a wall penalty by measuring healthy and sick policies - healthy pays ~0, the disease pays a wallcalibrate-threshold-between-healthy-and-sick

  Mechanism understoodwalkreward-shapingreward-shapinggate-battery

  Calibrate every threshold penalty by evaluating its exact formula on replays of at least one healthy and one sick policy: place the threshold between their distributions, size the weight so the sick policy pays a decisive fraction of tracking while the healthy one pays ~0, and pre-compute neighboring thresholds for cheap adjustment.

  Symptom

  Real walk_v7 occasionally clipped its own legs (stance narrowed to 133 mm mean vs nominal 214.5); a foot-distance penalty was needed, but an uncalibrated threshold/weight risked either doing nothing or becoming a reverse barrier.

  Context

  The term (relu(d_min - lateral foot distance), measured in the base yaw frame because world-frame y is meaningless after turning) was calibrated before training by replaying three known policies through the exact reward formula at the acceptance operating point: healthy v5 (183 mm) pays 0.6% of tracking - effectively free; narrowed v7 (133 mm) pays 22% - effective widening pressure; collapsed v8 (93 mm) pays 55% - a wall. d_min 0.16 was placed deliberately between healthy and sick, with alternative thresholds (0.14/0.18) pre-computed in the tool output for later adjustment. The shape self-check was named as a standing question: "先问'零代价的选项是什么'" - the zero-cost region must be exactly the desired behavior.

  Change

  feet_lateral_distance added at d_min 0.16 / weight -10, with telemetry expectation pre-registered (should decay toward 0 as stance learns >160 mm; if bow-legged over-widening >214 appears, only then discuss an upper bound).

  Outcome

  v9_probe on hardware: no leg contact ("没碰腿(N2 兑现)"), stance min 145/126 mm green; the term's zero-cost design left healthy gait untaxed.

  Mechanism

  A relu threshold penalty defines a free region and a priced region; its correctness is entirely in where the boundary sits relative to the healthy and pathological distributions. Replaying known-good and known-bad policies through the exact formula measures both distributions in the term's own currency, making the threshold and weight a placement decision instead of a guess.

  Applies when

  • adding any relu/threshold-style wall penalty
  • a safety margin (foot distance, joint limit, clearance) needs enforcement without taxing normal behavior
  • choosing between candidate thresholds for a new term

  “形状自检 (v6 横向组/v8-B 的教训 —— 先问"零代价的选项是什么"): 标称站距付 0, 健康步态付 ~0, 收窄才付费 … v5(健康) 183 mm … 0.6% ≈ 免费 | v7(收窄) 133 mm … 22% —— 有效推宽 | v8(塌陷) 93 mm … 55% —— 墙 … d_min=0.16 恰在 v5(183)与 v7(133)之间”

• Three hardware accounts locked the run design point - and the knee's real speed ceiling is tau_limit/kd, not the firmware limitfeasibility-accounts-lock-design-point

  Mechanism understoodrunplant-calibrationplant-calibrationactuator-modelinghardwareprocess

  Before opening a dynamic-gait training line, compute the full account set - tau_limit/kd effective speed ceilings, joint ROM under the intended reference geometry, and thermal RMS at the duty cycle - and let the accounts lock the design point; move only to pre-registered in-table alternates, re-running the accounts first.

  Symptom

  The run line was believed to require a firmware raise of the RS06 speed limit (10 rad/s) as a hard precondition, and the feasibility script's motor-envelope scan had marked 80/100 mm foot-lift cells "physically feasible".

  Context

  Three added accounts re-decided everything. (1) Damping tax: in MIT mode tau = kp*(q_des-q) - kd*qd, so sustained rotation is capped at tau_limit/kd = 12/1.5 = 8 rad/s - below the firmware's 10; at peak speeds 6.7-7.9 rad/s the damping term alone eats 10.1-11.9 N*m (84-99% of the torque limit). "提固件 limit_spd 越不过这道税 —— 它是 kd 与限扭的比,不是固件旋钮." (2) Joint ROM: the feasibility script had checked motor envelopes but NOT joint range - the ankle-pitch ROM caps 1:2:1 leg-shortening lift at 62 mm (soft) / 77 mm (hard), so the 80/100 mm "feasible" cells were voided; also firmware-independent. (3) Ankle thermal: duty 0.40 puts ankle RMS at 87% of continuous rating (0.35 -> 93%); long-period big-stride cells hit both ankle torque peak and heat. Verdict: firmware raise DEQUEUED (50 mm design point needs knee 6.7-7.3 < the 8 rad/s effective ceiling < firmware 10); vel_limit stays 10 so sim == robot. The three accounts uniquely lock the design point - 50 mm lift / T 0.60 s / duty 0.40 - "三笔账 唯一锁定,不是调参空间", with pre-registered alternates allowed only inside the table and only after re-running the accounts.

  Change

  Design point frozen from accounts; hardware precondition reversed by arithmetic rather than by test; reference amplitude (0.84 rad = FK inverse of 50 mm) derived, per-joint action scales sized to the required travel (knee 0.9, hip_pitch 0.6, ankle deliberately NOT amplified - hard limit is adjacent).

  Outcome

  A firmware work item left the critical path; an infeasible region of the design space was closed before any training; the remaining risk (knee tracking lag from the damping tax) was pre-registered with its own criterion and in-table fallback (duty 0.35) - "这不是'奖励没调好', 是 plant 账".

  Mechanism

  PD actuators in MIT mode pay kd*velocity out of the same torque budget that tracks position, so the effective speed ceiling is a ratio of configuration constants, invisible to firmware settings; and feasibility is the intersection of ALL constraint families (torque envelope, joint ROM, thermal RMS) - a scan that omits one family certifies impossible cells.

  Applies when

  • planning running/jumping or any high-rate gait on PD actuators
  • a firmware or hardware upgrade is assumed as a training precondition
  • a feasibility scan covers motor limits but not ROM or heat

  “膝的有效速度顶 = τ_limit/kd = 12/1.5 = 8 rad/s,不是固件的 10。… 提固件 limit_spd 越不过这道税 —— 它是 kd 与限扭的比,不是固件旋钮。… 可行性脚本只查了电机包络没查关节 ROM —— 其 80/100mm 的"物理可行"格作废。… 判决:RS06 提固件对 run v0 不是前置,出队”

• A get-up policy righted itself and sat - three terms paid the seated pose 84% of the return, and the only shaping term that could tell sitting from standing was an exp kernel outputting 5e-5seated-basin-dead-exp-kernel

  Mechanism understoodrecoveryreward-shapingreward-shapingattribution

  When a policy parks in a degenerate posture, tabulate what each reward term pays that posture against the target (watch contact terms that reward touching rather than bearing load) and evaluate every exp kernel at the error actually observed; a kernel narrower than the real error is switched off, and widening it is a one-variable repair that adds nothing new.

  Symptom

  R0 converged by iteration 700 and gained 1.7% over the next 2,300; success 0.0% in all four fall categories. Three of four success conditions passed (tilt median 1.0 deg, both feet in contact 99.6%, angular rate low); height passed 0.6% (median 0.204 m against 0.326). The robot knelt in a W-sit: hip yaw +/-47 deg, knees folded to 92% of the hard limit, shins flat, pelvis on the ground, torso vertical.

  Context

  Minimal reward table: upright (1-g_z)/2 +2.0, base_height linear progress +1.5, stand_pose exp(-||q-q_stand||^2/std^2) x upright gate +1.0 with std 1.0, still +0.5 and feet_on_ground +0.5 both x the upright gate, plus regularizers. The upright gate is a hinge that opens below 30 deg of tilt. The pre-registered fallbacks were then checked against the measured state: tightening the tilt gate was falsified (tilt was already 1.0 deg); a success bonus contradicted the spec's own no-cliff-bounty rule; narrowing the categories was useless (all four converged to the same pose); raising init noise was too weak for a basin this deep. Only a half-rise intermediate state addressed it, and a cheaper repair existed.

  Change

  R0.1 (user decision, single variable): stand_pose std 1.0 -> 3.0. Not a new term and not a bounty - repairing a declared term that was numerically dead. The two runs' logged env.yaml differ in log_dir and std only.

  Outcome

  R0.1 58.2% overall (R0 0.0%): supine 91.5%, side 82.2%, mid 55.9%, prone 0/156; knees fully straight; ||q-q_stand||^2 9.99 -> 0.91 and the stand_pose term 4.6e-5 -> 0.90; get-up ~1 s, no re-falls, the curve still rising at the 3,000-iteration cap. Prone stayed at zero and needed a different fix (see prone-dead-end-is-foot-placement).

  Mechanism

  Sitting earned upright 1.98/2.0, still 0.43/0.5 and feet_on_ground 0.46/0.5 - 3.0 of a 3.57 per-second return - because feet_on_ground asked for contact, not load. The only terms separating sitting from standing were base_height (+0.70/s for standing) and stand_pose, whose kernel at the real 9.99 rad^2 error (75% of it in the two knees) was exp(-9.99) = 4.6e-5 with a gradient near 1e-4. Standing up meant risking 3.0/s to gain 0.70/s while unfolding knees at 92% of their limit under load. With std 3 the same term is exp(-9.99/9) = 0.33 - a live gradient, three quarters of it on the folded knees.

  Applies when

  • a policy converges early to an upright but low, seated or kneeling pose
  • a posture-matching exp term reads ~0 in the training logs
  • contact-based rewards saturate while the task metric does not move

  “**关键:`feet_on_ground` 只问"触地"不问"承重", 跪坐时双脚确实贴地,照样满分。** 三项 3.0/s = 总回报 3.57/s 的 84%。 … **exp(−9.99) = 4.6e-5** —— 权重 1.0 的项实际输出 5e-5、梯度 ~1e-4, **不是"还没学会",是数值上根本不存在**。 … **R0.1 决定(用户 2026-08-09 定,单变量)**:`stand_pose` 的 `std` **1.0 → 3.0**。 不是加新奖励、不是悬崖悬赏,而是**修复一个已声明但数值失效的项**”

• A torque-tail penalty was paid for by bracing the legs against each other - the second simulator's leg-contact count caught it, and the first explanation ("the trainer can't see self-collision") was retracted from the run's own configtorque-penalty-bought-by-leg-bracing

  Observed oncerecoverysim2sim-gatesim2simreward-shapingattribution

  When a penalty lowers a demand metric, look for what the policy traded to get there - keep self-contact frames and foot spacing as standing sim2sim readouts - and check any "the trainer cannot see X" explanation against the run's resolved config before it enters the record.

  Symptom

  After R3.1's torque_headroom term collapsed the demand tail, MuJoCo success fell 100 -> 98% and leg-leg contact frames at mu 1.0 rose 750 -> 2,190 (worst rollout 177 -> 450). The one failure (prone seed 2) had the legs crossed, one foot on the other leg, trapped at 0.067 m - visible on video.

  Context

  Across the ten prone seeds, foot spacing and leg-leg contact frames were monotonically anti-correlated, and the failure was the extreme of the series. Pulling the legs toward the midline shortens the hip_roll lever arm and lowers torque demand. At the time the spec explained it as Isaac training without self-collisions ("a free lunch in a simulator without self-collision").

  Change

  Leg-leg contact frames and foot spacing were tracked in every MuJoCo gate; R3.2's candidates were "train with self-collision on" or "a minimum leg spacing term" - not stacked.

  Outcome

  The next rung's joint-velocity penalty incidentally erased the dependency (2,190 -> 86 frames). On 08-10 the runs' logged env.yaml showed enabled_self_collisions true in both r3_1 and v2_2 (inherited from walk v10): the tangle was physically learned bracing, visible to both simulators, and the Isaac/MuJoCo contact-count gap was mesh and contact fidelity. The "self-collision debt" narrative was withdrawn for the whole line.

  Mechanism

  A penalty on demand rewards any configuration that lowers demand; legs pressed together act as a mutual support that fails when contact geometry shifts slightly.

  Conflicts

  §24 attributes the dependency to self-collisions being disabled in training; §36 retracts that from the runs' env.yaml ("§24's mechanism explanation was wrong") and keeps the older sections unedited as history.

  Applies when

  • a torque, impact or energy penalty improves its metric and cross-sim success drops
  • legs or links approach each other after a regularization change
  • an explanation relies on a simulator setting nobody checked in the run config

  “prone 十个 seed 逐条看,脚距与腿-腿接触帧数单调反相关, 而唯一失败的那条正是最极端的一条 … 机制上说得通:把腿收到身体中线附近能缩短 `hip_roll` 力臂、降低力矩需求”

• Guessed joint friction was 2.5x low and damping 5x high - measure, then DR around nominalfriction-measured-not-guessed

  Mechanism understoodwalkplant-calibrationplant-calibrationdomain-randomization

  Measure frictionloss and damping separately (they need different rigs), put the measured value at DR center, and express DR as an additive band around that nominal - a DR range around a guessed value can exclude the real robot entirely.

  Symptom

  Old MJCF friction values were invented, not measured; when finally measured, every guessed value was wrong by a large factor in some direction.

  Context

  Joint friction split into Coulomb (frictionloss, tau_c) and viscous (damping b). Measured with the robot hung from a crane (吊机测) while armature was measured no-load; the two measurements are deliberately separated. Old MJCF: frictionloss 0.05, damping 0.1, DR joint_friction range [0, 0.1] "凭空拍的" (made up out of thin air).

  Change

  Replace guessed values with measured ones - frictionloss: RS06 0.15 / RS02 0.12 / RS00 0.13 N*m (old 0.05, i.e. 2.5x too low); damping: 0.02 N*m*s/rad on all three motor types (old 0.1, i.e. 5x too high). DR reshaped from an absolute made-up range [0, 0.1] to an additive band around measured nominal: joint_friction_add [-0.05, +0.10].

  Outcome

  "摩擦定稿(与 armature 一起, plant 参数第一次全部来自实测)" - friction frozen as part of the first fully-measured plant; DR now brackets a measured truth instead of spanning an invented interval.

  Mechanism

  Coulomb friction and viscous damping have opposite behavioral signatures (constant-torque threshold vs velocity-proportional drag); guessing both wrong in opposite directions gives a plant that is simultaneously too easy to start moving and too hard to move fast. DR centered on a wrong nominal makes the policy robust to a family of plants that does not contain the real one.

  Applies when

  • plant friction/damping values have no measurement provenance
  • DR ranges are absolute intervals rather than bands around a nominal
  • policy is over- or under-damped on hardware relative to sim

  “测关节摩擦。吊机测，而armature应该空机测试。… frictionloss τ_c (N·m) │ 0.15 │ 0.12 │ 0.13 │ 0.05(低 2.5×) … damping b (N·m·s/rad) │ 0.02 │ 0.02 │ 0.02 │ 0.1(高 5×) … DR │ joint_friction_add: [−0.05, +0.10] 叠标称 │ 旧 [0, 0.1] 凭空拍的”

• Mirror augmentation over an asymmetric default injects systematic error - symmetrize the default first and verify the transform bit-exactmirror-augmentation-needs-symmetric-default

  Mechanism understoodwalkobservation-designobservation-honestycurriculumprocess

  Before enabling any symmetry augmentation, make every constant inside the observation encoding exactly symmetric, and validate the mirror transform against forward kinematics to machine precision - an unverified augmentation is a new error source, not a regularizer.

  Symptom

  Mirror data augmentation was about to be added while both default poses (standing_pose, walk nominal_pose) were asymmetric - stale hand-tuned compensations from before a ground re-calibration, with hip_yaw differing 2.40 deg between sides and the foot soles actually tilted (pitch 2.88/1.35 deg, roll -2.47/+0.25 deg).

  Context

  The observation encodes joint_pos_rel = q - default. Under mirroring q_l -> -q_r, the relation (q-default)_l -> -(q-default)_r holds only if default_l = -default_r; with an asymmetric default, augmentation produces observation pairs that are NOT mirror images, i.e. "default 不对称时做镜像增强会引入系统性错误,比不做还糟" (worse than not doing it). The fix: adopt model geometric zero as standing default (MuJoCo FK verified: sole pitch/roll exactly 0, asymmetry 0.00 deg) and a symmetric crouch for walk (hip -0.25/knee -0.5/ankle -0.25 satisfying hip - knee + ankle = 0 to keep soles flat). The mirror transform itself was verified bit-exact before use: pseudovector vs polar-vector sign patterns (ang vel [-1,1,-1], gravity [1,-1,1], cmd [1,-1,-1]), joint swap-and-negate; FK check that left-foot pose under q equals the mirror of right-foot pose under mirror(q), measured error 0.00e+00.

  Change

  Defaults symmetrized first (with init heights recomputed by FK), stand policy retrained on the new default so both policies share one default; augmentation enabled only after the FK mirror test passed.

  Outcome

  stand_v1 achieved exact left/right pairing (l_knee -0.1013 / r_knee +0.1013), six-pair asymmetry 0.0 deg, height fluctuation 7 -> 1 mm, 33% less mean |action|.

  Mechanism

  Augmentation asserts an equivariance of the observation encoding; any asymmetric constant inside the encoding (the default) breaks the asserted symmetry, so the augmented data teaches a false invariance. Verifying the transform against FK geometry tests the assertion end to end, independent of the training stack.

  Applies when

  • adding mirror/symmetry augmentation to locomotion training
  • defaults or trims were hand-tuned per side at any point
  • observations are expressed relative to a default pose

  “观测里 joint_pos_rel = q − default。镜像下 q_l → −q_r,要让 (q−default)_l → −(q−default)_r 成立,必须 default_l = −default_r。default 不对称时做镜像增强会引入系统性错误,比不做还糟。… 位置误差与姿态矩阵误差实测均为 0.00e+00。”

• Set torque limits per joint from measured gait peaks - a uniform percentage is the wrong shape, and training must use the deployed numberstorque-limit-shape-by-measured-peaks

  Mechanism understoodwalkactuator-modelingactuator-modelinghardwareplant-calibration

  Measure per-joint torque peaks in the actual gait and set each limit as measured-peak x margin capped at rating; then propagate the same numbers into training and add an automated deploy-time consistency check - never derate by a uniform percentage, never let training assume torque deployment will not grant.

  Symptom

  A uniform 50% torque derating (18/8.5/7) had piled safety margin on the joints that never use it while cutting the busiest joint below half its measured demand.

  Context

  Per-joint gait peaks were measured (walk_v5 at cmd 0.3/0.6): RS06 (hip_pitch/knee) uses 5.5-5.9 N*m = 15-16% of its 36 N*m rating - cutting it to 12 is a free safety win; RS02's ankle_pitch runs at 16.2 N*m = 95% of its 17 N*m rating - "它是速度的硬件瓶颈", no room to cut; RS00 measured 36-44%, capped at 11. The resulting shape 12/17/11 replaced the uniform percentage. Sweeps across several limit sets (rated / 50% / 14-17-11 / 12-17-11) produced identical speed, lift, and landing force - within this range the limits do not shape the gait; what matters is consistency: "关键是训练和硬件必须是同一个数", because the exporter fills effort_limit from tau_limit, and a policy trained at rated 36/17/14 "会假设有三倍力矩可用" while deployed at 12/17/11 (exactly the v5 cross-generation inconsistency later suspected in its wild kicking).

  Change

  robot.yaml tau_limit set to the measured-shape 12/17/11, firmware written to match, and train/isaac_values.py regenerated so training sees the same limits; the deploy tool self-checks limits against robot.yaml on every run.

  Outcome

  Free safety margin captured where demand is low, the real bottleneck joint left at rating, and the train/deploy torque worlds unified with an automated consistency check.

  Mechanism

  Torque demand is grossly unequal across joints in a gait (15% vs 95% of rating here); a uniform percentage misallocates the safety budget by construction. And since the trainer treats effort_limit as a plant truth, any train/deploy mismatch is an invisible plant gap of exactly the mismatch ratio.

  Applies when

  • choosing safety torque limits for a legged platform
  • training-vs-deployment actuator limit audit
  • one joint runs near rating while others idle

  “曾用统一 50%(18/8.5/7)是错的形状: 把余量堆在用不到的 RS06 上, 却把 ankle_pitch 砍到需求的 52%。… RS02 在 0.6 m/s 已用到额定 95%, 它是速度的硬件瓶颈 … 实测多组限幅 … 完全一致 —— 限幅在这个范围对步态零影响, 关键是训练和硬件必须是同一个数。… 若训练仍按额定 36/17/14, 学出的策略会假设有三倍力矩可用。”

• A proposal in the runbook - torque and action limits as versioned safety tiers (classroom / research / expert) written to motor RAM and read back, separate from the reward's effort penalty - recorded as a proposal, its implementation unrecordedsafety-limits-are-a-layer-not-a-reward

  Hypothesisinfrareal-deployhardwareprocessreal-acceptance

  Keep hardware limits as an explicit, versioned safety layer (tiers written and read back at start, the persisted default the safest one) and the effort penalty as a behaviour layer; when a skill needs more torque, change tier deliberately rather than trading one layer against the other.

  Symptom

  Running and jumping need more torque than the deployed limits allow, and the temptation is to trade the training-side effort penalty against the hardware limit, or to hand a new user a robot "tuned however the last person left it".

  Context

  A message pasted into the operator runbook (undated, citing Berkeley's practice of storing the full motor configuration as JSON with write and read-back scripts) proposes: configuration is a versioned artifact, not a verbal agreement; three safety tiers in robot.yaml beside the gain and policy profiles - classroom (RS06 limited to 10 N*m, lateral joints clamped: "however bad the policy, it only moves awkwardly"), research (14 N*m, clamps at twice the measured need, the default) and expert (the 36 N*m rating, joint limits only, requiring an explicit flag); deploy writes the tier to motor RAM at start and reads it back, while the stored copy stays classroom so a power cut returns to the safest state. It frames limits as the safety layer and the effort penalty as the behaviour layer - more torque for running means switching tier, not weakening the penalty.

  Change

  None recorded: the message ends by asking which to do first, a rollback or the tiers.

  Outcome

  The sources do not record the tiers being implemented; the deployed limits stayed at 12/17/11 N*m through the recovery and one-leg lines (the one-leg spec treats raising the RS06 limit as a separate, unapproved hardware decision). Related and recorded elsewhere: torque limits were written to RAM only in a scripted, self-reversing field experiment.

  Mechanism

  Hardware limits bound the damage any policy can do; reward terms shape what a policy prefers. Mixing them either weakens safety to buy behaviour or distorts behaviour to buy safety.

  Applies when

  • a new skill needs more torque than the deployed limits
  • robots are handed to students or new users
  • motor configuration lives in people's heads or in the firmware only

  “配置是版本化的产物,不是口头约定。 … deploy_policy 启动时按档写进电机 RAM 并读回校验(落盘的那份永远保持 classroom,断电自动回到最安全状态)。 … 限幅是安全层,dof_torques_l2 是行为塑造层,它们在不同的层,不冲突。跑步要更大力矩就换档,而不是去动训练里的省力惩罚。”

• An edge-triggered landing penalty missed the tail and fired after the harm - penalize overspeed continuously inside the contact windowpenalize-tail-before-touchdown

  Mechanism understoodwalkreward-shapingreward-shaping

  Penalties aimed at impact/violation events must (a) price the excess over a threshold, not the mean, and (b) be active on the approach (state-gated window), not triggered by the event - check your control rate can even see the event you are penalizing.

  Symptom

  The v7 landing penalty (vz^2 on the contact-force rising edge, weight -10) did not bite: landing-velocity 95th percentile stayed at 2.61 m/s against a 0.3 target.

  Context

  Two structural faults were identified: (1) it penalized the MEAN over sparse events - many soft landings dilute the occasional violent slam, while the damage (GRF peaks, motor peak load) lives in the tail; (2) it fired AFTER touchdown - at 50 Hz evaluation the rising edge is aliased by physics decimation, so the read vz is often the already-decelerated post-impact value: underestimated, and with no shaping gradient before contact. Replacement: continuous penalty while the sole is inside a height gate (h < 0.03 m): relu(-vz - 0.30) - only the excess over an allowed approach speed is penalized (tail only), and gradient exists for several frames BEFORE touchdown. The sole-height computation again subtracts the 0.0585 m link offset ("WALK_DIAGNOSIS 坑#1, 别再踩"); the edge-triggered version was kept as a diagnostic only.

  Change

  feet_landing_vel reformulated: edge-event vz^2 -> in-window relu(-vz - v_ok) with v_ok 0.30 (conservative vs the sqrt(L)-scaled human value ~0.19, to be tightened after passing), h_gate 0.03, weight unchanged -10.

  Outcome

  The failure analysis of the first form was written before the second was trained; the v_ok escalation path (0.30 -> 0.45 if the robot becomes afraid to land) was pre-registered in the risk table.

  Mechanism

  Sparse-event mean penalties optimize the average case while the constraint is a quantile; and any penalty evaluated only at/after a discrete event gives the optimizer no gradient along the approach trajectory that determines the event. A state-gated continuous excess penalty fixes both: it prices only violations and shapes the approach.

  Applies when

  • impact/landing penalties fail to move tail percentiles
  • a penalty is triggered by contact edges at a coarse control rate
  • designing constraint-style penalties for rare violent events

  “罚的是均值路径:上升沿是稀疏事件 … 大量软着陆稀释偶发猛砸;而伤害在尾部 … 罚在触地后:50 Hz 评一次,上升沿被物理 decimation 混叠,读到的 vz 常是撞完已减速的值——既低估,又没有触地前的塑形梯度。”

• Every new penalty ships with a pre-registered withdrawal clause - if healthy gait must pay above the cap, the term stands downcalibration-threshold-with-withdrawal-clause

  Replicatedwalkreward-shapingreward-shapingprocess

  Introduce every new penalty with: the zero-cost-option audit, a replay-calibrated weight formula (healthy pays a fixed small fraction of tracking), and a pre-registered withdrawal condition - and let the clause fire without argument when the calibration says the term cannot be afforded.

  Symptom

  Three same-shaped crashes had established a failure archetype: v4's clearance, v8a's landing window (weight off by 58x uncalibrated), and v6a's bare hip_yaw suppression all combined a zero-cost "don't move" option with a fee on any motion - a reverse barrier that pushes policies toward standing still.

  Context

  The v11 landing-window penalty was therefore introduced under a calibration-threshold protocol: (1) shape chosen with the window tightened (h_gate 0.03 -> 0.02, because 0.03 equaled the clearance target and priced the entire descent); (2) weight from a FORMULA, not judgment: measure the term's raw value on healthy replays (v5/v10b), set w = -(0.10-0.15 x tracking reward) / raw_healthy; (3) withdrawal clause pre-registered: if healthy gait must pay >15% of tracking no matter the tuning, the term is withdrawn to the next version rather than forced in - "不硬上". The companion hip_yaw quieting term ran the same protocol (calibrate on replays, healthy pays <=5%) and was later retired entirely when a structural fix (zero action scale) made its shaping tax unnecessary.

  Change

  Penalty introduction protocol: shape audit (what is the zero-cost option?), replay-based weight formula, healthy-pay cap with a written stand-down condition - all before training.

  Outcome

  The landing term was in fact withdrawn under its clause (v12 records "P5 落地窗口罚 已撤 … 维持撤下"), demonstrating the protocol firing as designed instead of the fourth same-type crash.

  Mechanism

  A penalty's damage mode is mispricing healthy behavior; since the healthy price is measurable in advance on replays, both the weight and the go/no-go decision can be computed rather than discovered by a ruined training run. The withdrawal clause converts "make it work" pressure into a clean deferral.

  Applies when

  • adding any motion-taxing penalty to a working gait
  • a proposed term's weight has no measurement behind it
  • a previous same-shaped term crashed training

  “权重公式而非拍脑袋:先在 v5/v10b 回放上量 h_gate=0.02 的原始值,w = −(0.10~0.15 × 跟踪奖励) / raw_健康;标定门槛:若健康步态无论如何要付 >15% 跟踪,本项撤下留 v12,不硬上 (v4 clearance/v8a-B/v6a 三次同型翻车的教训:代价为零的"不动"选项 + 一动就收费 = 反向壁垒)。”

• Train with self-collisions ON (filtering nested-link ghost pairs) - the reward wall prevents, the physics makes cheating impossibleself-collision-physics-plus-reward-wall

  Mechanism understoodwalkplant-calibrationplant-calibrationsim2simreward-shaping

  Never train a contact-risk behavior with self-collisions disabled; enable them with an audited filter list for nested/overlapping pairs (zero contacts across a pose sweep), record the fps cost, and keep a calibrated distance penalty as the preventive layer on top.

  Symptom

  walk_v8 logged 107 frames of leg-on-leg contact while still earning 0.751 tracking score - because training-side self-collisions were OFF, leg clipping was literally imperceptible to the policy ("碰腿在训练里 根本感知不到").

  Context

  Enabling self-collisions naively is its own trap: an Isaac audit had shown PhysX auto-filters adjacent bodies (base-hip clean for free) but nested links generate ghost forces - calf and ankle_roll overlap 65 mm at the zero pose, producing 12x body-weight phantom forces. The v10 recipe: enable self-collisions, explicitly filter only the two nested pairs (l/r calf-ankle_roll), then run a zero-contact audit at three poses (nominal stand, walk crouch, swing-extreme) requiring contact count = 0, adding any residual pair to the filter and re-auditing; a 500-iter sanity run for NaN and an fps-cost record (measured -8.8%). Redundancy with the reward-side foot-distance wall was argued, not assumed: "N2 离得远(奖励侧预防),SC 碰了疼(物理侧兜底)" - the reward keeps distance at range, the physics makes contact hurt - so the v8-style "clip legs and still score" outcome becomes physically impossible.

  Change

  enabled_self_collisions=True + 2-pair filter + three-pose zero-contact audit (re-verified at 0.00 N after the later mass update) + fps budget recorded.

  Outcome

  Leg contact entered the training signal; the audit protocol caught the nested-pair ghost-force hazard before it corrupted training; combined with the calibrated distance wall, later versions held contact = 0 on hardware and in sim.

  Mechanism

  A hazard absent from the training physics cannot be learned about, no matter the reward; but collision meshes that interpenetrate at rest inject large fictitious forces if enabled blindly. Filtered enabling plus a pose-swept zero-contact audit gives true contact physics with no phantom energy - and layering prevention (reward) with consequence (physics) covers both learning and enforcement.

  Applies when

  • real robot self-contacts while training scored it healthy
  • enabling self-collisions on a model with nested collision meshes
  • deciding between reward-side and physics-side fixes for clipping

  “PhysX 自动过滤相邻体(base↔hip_pitch 免费干净),幽灵力只在 calf↔ankle_roll(零位嵌套 65mm,12 倍体重)。… 与 N2 互补不冗余:N2 离得远(奖励侧预防),SC 碰了疼(物理侧兜底)—— v8 那种 107 帧互碰拿 0.751 跟踪分的事从此物理上不可能。”

• A curriculum ramp keyed to the process step counter re-fires on every resume - and shipped policies never saw the penaltycurriculum-counter-lineage-steps

  Mechanism understoodomnicurriculumcurriculumattribution

  Key every curriculum/ramp schedule to lineage-cumulative progress, not per-process counters; and audit where your shipped checkpoints sit relative to every ramp - a penalty that no product ever experienced is not part of your training.

  Symptom

  vx+0.30 died at a fixed relative time in every resumed run: resume at 500 -> slide at 1100, zero at 1300; resume at 700 -> slide at 1300, zero at 1500 - absolute depths offset by exactly the resume offset, relative timetable identical.

  Context

  ramp_reward_weight (the saturation penalty ramp) read env.common_step_counter, which restarts at 0 for every run including --resume. So start_step=600 meant "600 iters after THIS resume", not "lineage iteration 600". The A/B arm pair was the clean proof: their env.yaml differed only in log_dir, only resume point distinguished them, and the omni CurriculumManager had exactly one active term - nothing else could produce that timetable. Second consequence: every shipped checkpoint (s1e-500 at +500, C2-700 at +200, A800 at +100) was selected before its run's +600, so the saturation penalty weight was 0.000 for every product ever shipped - explaining saturation 33% and raw |action| 1.9 against clip 1.0 (hip_roll in bang-bang), i.e. half the heat budget.

  Change

  Two independent recommendations recorded: (1) make the ramp count lineage steps (add the checkpoint's iteration offset on resume) or pin terminal weights in downstream rungs instead of ramping; (2) give the saturation penalty its own rung - never mixed into a skill-learning rung (that would be two variables again).

  Outcome

  Explained the recurring +600 death of the highest-amplitude command and the persistent actuator saturation of all shipped products with one root cause; honest caveat booked (at +800 the weight is only -0.086, small, but vx+0.30 is the command demanding the largest action amplitude, so it is squeezed first).

  Mechanism

  Resumable training splits "the lineage" from "the process"; any schedule keyed to process-local counters silently re-applies its transient to every descendant run, and any product-selection habit that picks checkpoints early systematically samples the pre-ramp regime - the curriculum exists in the config but never in any shipped policy.

  Applies when

  • resumed/forked training with any scheduled reward or DR ramp
  • a metric dies at a fixed offset after each resume
  • shipped policies show behavior a late-schedule penalty should prevent

  “ramp_reward_weight 读的是 env.common_step_counter，它每个 run 从 0 开始，--resume 也不例外。… 原始 run / 臂B | 500 | 1100 = +600 | 1300 = +800；臂A | 700 | 1300 = +600 | 1500 = +800 … 所有出品其实从没见过饱和罚。… 这解释了 sat_max_pct 33%、raw |a| 最大 1.9（clip 是 1.0）—— hip_roll 一直在 bang-bang，而罚它的那一项权重恒 0。热账的一半在这里。”

• A joint frozen at the action clamp pays zero action_rate forever - penalize pre-clip saturation to make the cheat cost moneysaturation-cheating-zero-rate-cost

  Mechanism understoodwalkreward-shapingreward-shapingaction-rategate-battery

  Whenever actions are clipped and any smoothness/rate penalty exists, add a pre-clip saturation penalty so living at the clamp costs more than oscillating - and audit for frozen-at-clamp joints (action std ~0, |a| at exactly the clip value) as a standing acceptance row.

  Symptom

  With action_rate_l2 raised to -0.2, walk_v7's hip_pitch actions froze at exactly +/-1.000 (the clamp), reproduced bit-for-bit on hardware (splits frozen at +/-0.35 rad); the gait-shaping term joint_pos_ref collapsed to 0.026-0.035. The repo had died in the same trap once before (walk_v0: four joints pinned at +/-1.0).

  Context

  Mechanism: a joint pinned at the clamp has action-rate cost exactly zero and forever zero - under a strong smoothness tax, "push to the clamp and freeze" becomes the dominant optimum. Lowering the weight (-0.2 -> -0.1) only reduces temptation; the frozen state still costs nothing, so the structural fix adds action_saturation = sum(relu( |a_raw| - 0.9)) at weight -1.0, computed on the PRE-clip network output - post-clip, |a|=1.01 and |a|=3 punish identically and the out-of-range gradient dies (v0's old disease: mean |a| 1.71 soaked in saturation). Economics: freezing at |a|=1.0 now pays 0.1/joint/step (two hips = 40% of alive) vs ~0.0004/step for the healthy reference oscillation - the cheat flips from free to ~250x negative. Honest limits were recorded: A1 does not forbid freezing at 0.89 (the anti-freeze pressure must come from the oscillation demand of joint_pos_ref), and the alternative "rate on post-clip target" was rejected as 换汤不换药 - a pinned target also has zero rate.

  Change

  v8-A: add action_saturation (-1.0, thresh 0.9, pre-clip) AND halve action_rate_l2 (-0.2 -> -0.1, still 3.3x the v5 value); success criterion pre-declared (joint_pos_ref telemetry returns to v6 scale).

  Outcome

  Booked as the structural repair of the v7 freeze; also fixed a config hygiene trap discovered on the way - action_rate was assigned twice in __post_init__ (v5 comment line then v7 line), merged to one assignment "别再留两处赋值给下次审计埋雷".

  Mechanism

  Clipping creates a zero-gradient, zero-cost absorbing region in action space; any penalty on action derivatives makes that region strictly optimal once entered. Only a penalty on clamp proximity itself (measured pre-clip so depth of violation is visible) restores a slope out of the absorbing region.

  Applies when

  • joints sit at exactly the action clip with near-zero variance
  • raising a smoothness penalty degrades gait amplitude
  • shaped-oscillation terms collapse after a rate-weight increase

  “钉死在钳位的关节 action_rate 代价精确为零且永远为零;−0.2 之下"推到钳位冻起来"成了压倒性最优 … 本仓第二次栽在同一坑(walk_v0 死于四关节钉死 ±1.0)。回调权重(−0.2→−0.1)只降低诱惑不消除作弊 … 算在 clip 前的原始网络输出上 … 作弊收支从"白赚"变成"倒贴 ~250 倍"。”

• Prove a new penalty actually fires - two ways a clearance term silently did nothinginert-reward-term-audit

  Mechanism understoodwalkreward-shapingreward-shapingplant-calibration

  Before training with a new reward term, log its realized per-step value under the current policy and confirm it is nonzero where intended - check coordinate zero-points against FK and check who occupies the term's gate; and never weaken the term that creates the states your new term needs.

  Symptom

  A newly designed swing-height clearance penalty could have trained as a no-op twice over, and the companion advice to lower feet_air_time actively backfired when tried.

  Context

  Instance 1 (zero-point offset): the proposed code used body_pos_w of the foot link, but that is the ankle_roll_link frame origin, which sits 0.0585 m above the ground even with the foot flat on it - so (0.03 - 0.0585) is always negative and the penalty is永远 0; the 0.0585 offset must be subtracted (verified identical in MuJoCo FK and Isaac). Instance 2 (gate occupancy): the clearance penalty fires only in swing phase; a dragging policy keeps both feet in contact, so the penalty is constantly 0 for exactly the policy it was meant to fix - and worse, any slight lift immediately incurs it, a reverse threshold. Lowering feet_air_time to 0.5 on that advice measurably collapsed air time to 0.0002 (below v3). Corrected understanding: "clearance 是把已有的摆动相抬高, 造出摆动相仍要靠 air_time" - air_time creates the swing phase, clearance raises it.

  Change

  Fixed the height zero-point; kept feet_air_time as the swing-phase creator with clearance layered on top; both errors documented as corrections to the team's own earlier advice.

  Outcome

  With both fixed, swing height rose from 22-23 mm (v2) to 29 mm (v5) to 34 mm (v6); the inert-term failure class entered the standing checklist.

  Mechanism

  A penalty's gradient exists only where its gate is occupied and its argument crosses its threshold; frame offsets shift the threshold out of reach, and phase gates can have zero occupancy under exactly the policy being treated. Terms interact as an ecology - one term must create the states in which another can act.

  Applies when

  • adding any gated or thresholded penalty (clearance, impact, slip)
  • a new term produces no behavioral change at any weight
  • body-frame positions are used in reward code

  “body_pos_w 是 ankle_roll_link 坐标系原点,平放触地时仍高出地面 0.0585 m。… (0.03 − 0.0585) 恒为负 → 惩罚永远是 0 … clearance 惩罚只在摆动相生效,拖地时两脚始终触地 → 惩罚恒 0;而一旦轻微抬脚就立刻扣分,对正在拖地的策略是反向门槛。… 正确认识:clearance 是"把已有的摆动相抬高",造出摆动相仍要靠 air_time。”

• Curriculum-gate a penalty to the phase where its disease occurs - early on it only taxes explorationgate-penalties-to-the-disease-phase

  Replicatedwalkcurriculumcurriculumreward-shapingaction-rate

  For penalties aimed at late-stage pathologies (freezing, saturation, degenerate attractors), ramp the weight in only after exploration noise has decayed; anchor the terminal weight to measured healthy-vs-sick raw values, and shift all related tripwires to after the ramp completes.

  Symptom

  The action_saturation penalty, applied from iteration 0 in v8a, taxed exploration itself: with init_noise_std 1.2 the sampled actions paid ~-2.45/step before any policy had formed - while the disease it targets (clamp freezing) is a LATE pathology (v9 froze at iteration ~2624).

  Context

  v10 re-introduced the same penalty behind a curriculum gate: weight 0 until iter 1000, ramping linearly to -1.0 by iter 2000 - present only when the disease can occur, absent while exploration noise dominates. The trust argument was evidence, not hope: in v8a the term, while active, had pulled joint_pos_ref from 0.041 up to 0.155 and climbing - proof it can extract a policy from the frozen pit. Weight magnitudes were anchored to measured raw values (healthy v5 0.310 / v6 0.106 vs frozen v7 1.145 / v9 1.22 per step: at -1.0 healthy pays 6-18% of tracking, frozen pays 60%+, standing ~0). v10c then isolated the gated term as THE anti-freeze mechanism by single variable, upgraded to untouchable status in v11: "S 的门控机制(v10c 单变量铁案:任何情况下 不许撤,只许调终值)" - and v11 dared to relax other penalties only because S stood guard.

  Change

  action_saturation gated 0 -> -1.0 over iters 1000-2000 (later terminal value tuned -1.0 -> -0.5 with the gate mechanism itself frozen); tripwires adjusted to respect the gate's timing (freeze check moved to iter 2500-3000 to give the ramped term its effect window).

  Outcome

  Freezing stopped recurring while early training kept full exploration; the mechanism graduated from experiment to invariant within two versions.

  Mechanism

  A penalty's incidence depends on who occupies its support: early in training that is exploration noise (whose suppression starves learning), late it is the converged pathology. Time-gating aligns the penalty's presence with its target's presence, buying the constraint without the exploration tax - and tripwire timing must then be computed from the gate schedule, not from ungated precedents.

  Applies when

  • a structural penalty punishes exploration in early training
  • a late-onset pathology (freeze/saturation) needs a standing guard
  • deciding when a curriculum ramp should engage

  “v8a 实锤它的病根是"罚在采样动作上"——init_noise_std 1.2 的早期等于罚探索(~−2.45/步);而冻结是晚期病(v9 速率 2624 才死平)… 门控让它只在病发期在场。… v8a 里它在场时 joint_pos_ref 从 0.041 爬到 0.155 且仍在升——有从低谷爬出的实证。”

• Decompose the offending quantity by channel first - then penalize the failure event, not the jointspenalize-the-slip-not-the-joint

  Mechanism understoodwalkreward-shapingreward-shapingattribution

  Before penalizing motion to fix a side effect, measure which channels actually carry the offending quantity; prefer penalties conditioned on the failure event that are exactly zero for healthy behavior - and do not medicate behaviors that measurement shows are not sick.

  Symptom

  Heading drift with support-foot yaw slip (v5: 212-284 deg accumulated over 15 s); the previous v6 draft had attacked it by penalizing lateral joints (a roll 4.0 / yaw 2.0 "home" group) - which collapsed training into the standing basin.

  Context

  Before choosing the penalty target, the yaw angular momentum was decomposed by joint group with MuJoCo subtree_angmom weighted by real walking joint velocities: pitch-class joints (hip_pitch + knee) carry 95.3%, hip_roll 3.3%, hip_yaw 1.4%. The failed "home" group had been taxing 2.7/step to manage a 4.7% channel. The replacement, feet_yaw_slip (-0.2, |support-foot yaw rate| while in contact), targets the failure event itself and - decisively - costs a non-slipping gait exactly zero, which "横向回家组做不到". The same rung's do-not-do table applied the complementary principle to foot spacing: measured 196-214 mm, stable, no crossing - "没病不吃药" (no disease, no medicine).

  Change

  Removed joint-usage penalties for the drift problem; added the event-conditional slip penalty (-0.2, realized tax 0.141/step = 12% of tracking) alongside the existing linear-slip term.

  Outcome

  Turn-gain left/right difference improved 70% -> 19% and heading 185 -> 60.3 deg by v6 without a standing-basin collapse; the 2.7/step lateral tax never returned.

  Mechanism

  Penalizing joints taxes every use of a channel including healthy use, and if the channel carries little of the offending quantity the tax buys nothing while pushing the optimum toward immobility. An event-conditional penalty (slip while in contact) prices only the failure, leaving the healthy gait's cost surface untouched - and the channel decomposition tells you in advance whether a joint-side fix can even work.

  Applies when

  • choosing a penalty target for drift/slip/impact problems
  • a proposed penalty taxes joints or motions rather than failure events
  • a previous joint-penalty attempt collapsed the gait

  “pitch 类 (hip_pitch + knee) 占偏航角动量 95.3% … hip_yaw 1.4% … 压 hip_yaw 是管 1.4% 的通道收 2.7/步 的税 —— 上一轮正是这样把策略推进了站立盆地。滑移项不惩罚走路: 不打滑的步态代价为零, 这是横向"回家"组做不到的。”

• Anchoring the action on the measured joint angle (target = q + beta*a), with a beta curriculum down to tau_limit/kp, bounded torque by construction, removed the re-falls and later stood the robot up on hardwarebeta-anchored-action-target

  Mechanism understoodrecoverytraining-runactuator-modelingcontract-freezecurriculum

  For large-motion skills on position-controlled actuators, bound the action relative to the measured joint angle with a per-joint authority of tau_limit/kp, curriculum the authority down from full range, keep the curriculum state out of the observation and pin acceptance at the deployed authority - and make the deployment code refuse to run the anchored contract without a measured q.

  Symptom

  The V0 full-range absolute action produced violent targets; the V1 command-anchored rate limit made standing oscillate. Both failure modes came from how the action becomes a target.

  Context

  V2.0 (user approved, from scratch): BetaAnchorJointPositionAction, target = q_measured + beta_j(m)*a, memoryless per step. beta_j(m) = floor + m*(beta0 - floor), beta0 = the contract half-range (m = 1 reproduces V0 authority), floor = min(tau_limit/kp, beta0): hip_pitch 1.309 -> 0.40, knee 1.047 -> 0.40, hip_yaw -> 0.917, the other joints unchanged - the tightening lands exactly on the joints the V0 torque account convicted. m drops 0.1 per step when a standing-share EMA exceeds 0.35. beta is NOT in the observation, so the 45-dim contract is untouched; acceptance is pinned at m = 0 because the Python curriculum state is not saved in the checkpoint. The deployment chain got a new profile (recovery_v2: action_anchor current_q, explicit per-joint beta written into the contract, independent of the gain profile), and policy_io raises if q is missing rather than silently falling back to the absolute contract; the old profile's check reproduced its pre-change deviation bit for bit.

  Change

  New action term and beta curriculum; later the RS06 floor was lowered 0.40 -> 0.30 -> 0.25 (kp*beta 7.5 N*m) and the stamped deployment profile was synced to 0.25.

  Outcome

  First acceptance at m = 0 (v2_0b): re-falls 0% in every category, the torque gate passed for the first time on the line (worst 69.9%), knee jitter 0.004; supine 98.8 / side 88.8% with prone and mid still failing (fixed by the conditional pull curriculum). MuJoCo showed demand at or under the limits (hip_pitch 11.7/12 against V0's 26.8). Lowering beta cut impact (hip_pitch demand 9.7 -> 8.5 N*m) but barely slowed the get-up - it had become coordination-limited. Enabling the policy moves the target only +/-beta around the current pose, so there is no homing fling; the 08-11 real get-up and the later v3_1p1c both run on this contract.

  Mechanism

  kp*beta caps the proportional torque in a single step with no build-up delay and no memory, giving both a hard impact bound and full balance bandwidth.

  Applies when

  • a skill needs full joint range but hardware torque limits are low
  • absolute position targets cause impacts or saturation
  • changing the action semantics of a contract that deployed policies share

  “**动作项** `BetaAnchorJointPositionAction`:`target = q_实测 + β_j(m)·a`, 逐步无记忆 … **Play/验收钉 m=0(= floor = 部署档)**:python 课程状态不进 checkpoint, Play cfg 显式 `beta_m_start=0` … 判读:**结构赌注兑现** —— 站姿零再摔 + 力矩账首过(kp·β 封顶按构造)”

• Knee swing collapsed because it directly trades against the slip penalty - price the conflict explicitly and clamp what reward cannot holdknee-swing-vs-slip-pricing

  Mechanism understoodwalkreward-shapingreward-shapingattribution

  When a behavior collapses as another metric improves, look for the term pair trading them and set their price ratio deliberately (with escalation and reverse tripwires pre-registered); where the policy actively spends action budget to undo your target, stop paying more reward and clamp the target space structurally.

  Symptom

  Knee peak-to-peak swing collapsed across generations - v5 33 deg, v10 26-30, v10b 7-8, v11 6.5-8.6 - and rolling the clock back did not recover it, acquitting the clock; the collapse tracked the gated slip penalty instead: "屈膝与不打滑在当前奖励里直接对抗" - v10b's excellent 93 deg slip was purchased with knee amplitude.

  Context

  Reward-side flexion fixes had failed three times: raising reference amplitude backfired twice (v9/v11), and v11's deep-squat default was actively fought by the policy - it spent 0.68 of action budget pulling the squat straight ("被策略花 0.68 动作拉直反杀"). v12's design accepted the conflict as real and attacked on two tracks: (1) ECONOMICS - a direct knee_swing_amplitude reward (+0.3, target 0.55 rad, capped at 0.6/step = 55% of tracking), explicitly opposed to the slip penalty by design ("显式对立——这正是设计:v12 就是这场对抗的定价实验"), with an escalation ladder (K +0.3 -> +0.5, then slip -0.5 -> -0.3, one layer at a time) and a reverse tripwire (slip telemetry back at v10 levels -> slip weight to -0.8, accept ~20 deg knee compromise); (2) STRUCTURE - knee target bounds [0.2, 0.9] rad so full straightening is physically impossible (straightest 11.5 deg) and the 0.68 fighting budget is released. A bonus falsifiable prediction was attached: phase-lock strength tracks amplitude (v9_probe 48 deg locked 2.5 Hz; v11 low-amplitude 1.36 Hz unlocked), so if K works, hardware phase-lock should return - one change, two verdicts.

  Change

  knee_swing_amplitude reward + knee target clamp + pre-registered escalation/reverse levers; the failed reward-side-only approach retired.

  Outcome

  The lineage was frozen before v12 trained (strategic reset), but the diagnosis stands as the walk line's clearest example of two reward terms trading a behavior between them, with the pricing experiment and structural clamp fully designed and calibrated.

  Mechanism

  When two terms price opposite aspects of one motion (swing amplitude creates yaw momentum that becomes slip), the optimizer settles wherever the price ratio puts it - patching one side moves the equilibrium, not the conflict. Explicit pricing makes the trade a designed quantity; structural clamps remove the regions where the policy spends budget fighting the designer.

  Conflicts

  The pricing experiment (K vs slip) was designed and calibrated but never trained - the 2026-08-05 reset suspended v12; the collapse attribution table and the 0.68-action counterattack are measured, the remedy's效果 is untested.

  Applies when

  • one gait quality degrades in lockstep with another's improvement
  • the policy visibly fights a default pose or reference
  • repeated reward-side fixes for the same behavior have failed

  “膝摆塌在 v10→v10b,头号嫌疑是门控滑移罚(四代实测膝 p2p:v5 33° / v10 26~30° / v10b 7~8° / v11 6.5~8.6°;退时钟没救回 → 非时钟)——"屈膝"与"不打滑"在当前奖励里直接对抗 … 奖励侧修屈膝已三败 … v11 深蹲 default 被策略花 0.68 动作拉直反杀”

• The real robot's right-leg kicking was over-trained-delay times loop gain - irreducible pipeline latency is plant, model it fully from day onepipeline-latency-is-plant-not-dr

  Mechanism understoodomniactuator-modelingactuator-modelingreal-acceptanceattributionsim2sim

  Measure the end-to-end action pipeline delay and build it into the nominal plant and every acceptance gate from day one; treat power/scale deratings that "fix" oscillation as gain-reduction crutches flagging an unmodeled delay, and expect higher-feedback-gain policies to be MORE delay-fragile.

  Symptom

  On hardware, s1c/s1d at action scale 1.0 always kicked wildly with the right leg (s1c only ran as SOTA at power 0.8; s1d only at 0.7) - while sim showed nothing under default evaluation.

  Context

  Sim reproduced the incident item by item once the real pipeline delay was injected: s1d@1.0 with --delay 1 fell at 10.2 s, --delay 2 at 5.2 s; s1c@1.0 stressed (r_hip_roll saturation 5 -> 16%; "右脚" = the policy's chirality makes the right leg its high-gain leg); and the combos that worked on hardware (s1c@0.8+delay2, s1d@0.7+delay2) all survived in sim. Mechanism: the real pipeline is ~1-2 ticks (BusWorker next-cycle pickup + CAN round trip) but S1.1 trained only to 1 tick - "超训延迟 × 全环路增益 = 振荡;衰减 = 压环路增益换稳定" (delay beyond training x full loop gain = oscillation; the power derating had been buying stability by compressing loop gain). s1d was MORE fragile than s1c because its yaw 3-layer stack had learned higher feedback gain - higher gain, lower delay tolerance. Three changes: latency DR widened to cover reality; acceptance gates and smoke runs moved permanently to --delay 2 ("门必须在真机条件下预测 真机"); and the doctrine written twice-paid: "不可约的管线属性(延迟、 限速)不是'随机化选项',是 plant 本体,第一天就该全额建模" - S1's nominal-then-robust staging falsified by hardware for the second time. The later s1e hardware run at power 1.0 (no kicking, normal force) closed the loop: "0.8 = 旧代拐杖" - the derating had been a crutch for the under-modeled delay, not a real requirement.

  Change

  Latency modeled as plant from day one of any lineage (measured 1-2 ticks covered, bridge-layer rate limits likewise modeled by default); every gate and smoke evaluation issued under --delay 2.

  Outcome

  Kicking reproduced, explained, and eliminated in the s1e generation at full scale and full power; the deploy-side crutches (0.7/0.8) retired for the new lineage.

  Mechanism

  Feedback oscillation onset is a product of loop gain and phase lag; a policy trained below the real delay learns gains that sit past the real stability margin, and any output derating masks it by scaling gain down. Since pipeline delay is deterministic hardware property - not an uncertainty - it belongs in the nominal plant, and every evaluation must include it or the gate predicts a robot that does not exist.

  Applies when

  • hardware oscillation/kicking that sim only reproduces with added delay
  • a policy only runs on hardware at reduced power/scale
  • defining what belongs in the nominal plant vs the DR list

  “真实链路延迟 ~1~2 拍 … S1.1 只训到 1 拍——超训延迟 × 全环路增益 = 振荡;衰减 = 压环路增益换稳定。s1d 比 s1c 更脆 = yaw 三层栈学出更高反馈增益,增益越高延迟容忍越低。… 教训入账:S1「先标称后鲁棒」第二次被真机证伪——不可约的管线属性(延迟、限速)不是"随机化选项",是 plant 本体,第一天就该全额建模。”

• A frame-history observation under zero DR memorizes the trainer's plant fingerprint - the estimator must see variation to learn estimationhistory-obs-needs-plant-variation

  Mechanism understoodomniobservation-designobservation-honestydomain-randomizationsim2sim

  If the observation carries history (stacked frames, RNN), keep at least minimal plant variation (gain/latency jitter) on from the first iteration - "nominal first, robust later" is structurally invalid for estimator-bearing contracts.

  Symptom

  omni_s1 (fresh 215-dim contract with a 5-frame history window, trained with DR fully off): training all green, yet the MuJoCo gate scored 0/20 on all eight doors - falls within 2 s, seven checkpoints, not one transferred.

  Context

  The history window exists precisely to let the actor implicitly estimate line velocity and actuator dynamics (the actor is denied base_lin_vel by observation honesty). Under a constant plant that implicit estimator has nothing to estimate - it learns the trainer's exact response fingerprint instead, and any other simulator's micro-differences are out-of-distribution: "5 帧窗按设计就是隐式估计器, plant 恒定时它学到 Isaac 精确响应的指纹". The planned "nominal-first-robust-later" staging was declared STRUCTURALLY incompatible with history observations: "估计器要见过变化才学估计, 否则学背诵" (an estimator must see variation to learn estimation, otherwise it learns recitation). Honest confound note kept: this is mixed with "zero DR does not transfer, period" - but both attributions prescribe the same fix, so no control was run.

  Change

  S1.1: minimum actuator jitter turned on from day one - kp/kd +/-10%, latency 0-1 frame (friction/COM/mass still nominal, no push - those stay for the S2 ladder).

  Outcome

  Transfer restored: survival 0/20 -> 20/20, speed 19/20, foot distance 20/20 (remaining failures moved to gait quality, a different disease); the staging doctrine was amended - history-carrying contracts never train under a frozen plant.

  Mechanism

  A recurrent/history channel fits whatever temporal structure minimizes loss; with a deterministic plant the cheapest structure is the plant's own impulse-response signature, yielding features that are simulator-specific rather than physics-general. Plant variation forces the channel to carry state-estimation features that transfer.

  Conflicts

  Attribution is explicitly confounded with the simpler "zero DR never transfers" reading ("与「零 DR 本身就不迁移」混杂 … 两种归因处方相同, 不做对照") - the source chose not to spend a control run separating them.

  Applies when

  • adding frame stacking or recurrence to an actor observation
  • a nominal-plant policy fails a cross-simulator gate within seconds
  • planning DR staging for a new contract

  “frame_hist × 零 DR = plant 指纹过拟合——5 帧窗按设计就是隐式估计器, plant 恒定时它学到 Isaac 精确响应的指纹, MuJoCo 的微小差异即 OOD, 2 s 内摔, 七个 checkpoint 无一迁移。「先标称后鲁棒」的分段与历史观测结构性冲突:估计器要见过变化才学估计,否则学背诵。”

• A single run's drift direction may be a limit cycle, not a policy bias - check the sign distribution across seedsmultiseed-sign-test-for-drift

  Mechanism understoodwalksim-evalmeasurementattributiongate-battery

  Distinguish "bias" from "broken symmetry limit cycle" by the sign distribution over many seeds; report drift as (mean, sign split), and never compare single-run drift magnitudes across versions.

  Symptom

  Net yaw over 15 s appeared to worsen from -41 deg (v2) to -84 deg (v4), inviting the conclusion that the new version drifted more.

  Context

  The Isaac-side view across 32 environments told a different story: per-env yaw was mixed-sign (20 negative / 12 positive) with mean ~0 - the drift is a limit cycle whose direction depends on initial conditions, not a systematic policy bias. The single MuJoCo run had sampled one draw from that distribution, so its magnitude could not be compared across versions as if it were a property.

  Change

  Evaluation rule: before classifying drift as systematic, run multiple seeds and examine the sign distribution; single-trajectory drift magnitudes are samples, not properties.

  Outcome

  The v2-vs-v4 drift "regression" was reclassified as not-established; later drift work (hip_roll l+r bias) used cross-policy, cross-seed evidence instead.

  Mechanism

  Symmetric dynamical systems can settle into either of two mirrored limit cycles; the selected cycle is decided by noise and initial state. A statistic whose sign is initial-condition-dependent has no meaning as a single sample - only its distribution does.

  Applies when

  • comparing heading drift or lateral drift across policy versions
  • a symmetric-looking behavior shows a consistent direction in one run
  • deciding whether to fix "drift" in reward or calibration

  “偏航反而变差(−41° → −84°):注意 Isaac 侧 32 env 的逐 env 偏航是正负混合(20/12)、均值 ≈0,说明这是极限环性质(方向随初值)而非策略偏置 —— MuJoCo 单次跑测到的是分布里的一个样本,不能当作系统性偏差。要判断需多种子统计。”

• The fallen-state reset was designed, not sampled from SO(3) - fixed category shares with jitter, a low drop that settles physically, equal left/right shares for mirror augmentation, and a numeric check before trainingfallen-pose-reset-distribution

  Observed oncerecoverytraining-runcurriculumdomain-randomizationprocess

  Build a fallen-start distribution from named, physically plausible categories with jitter and a settle phase, keep mirrored categories at equal probability, and check the realized shares and penetration numerically before spending a training run on it.

  Symptom

  A get-up policy can only learn from the fallen states its resets produce; uniformly random orientations produce ground-penetrating and limit-jammed states the robot can never be in.

  Context

  R0 reset_root_fallen: supine 30%, prone 30%, side_l 15%, side_r 15%, mid (random axis 50-125 deg) 10%, +/-15 deg jitter, full yaw, dropped from 0.28-0.40 m and left to settle under physics, joints uniform inside the soft limits with a 5% margin plus small random velocities. Random SO(3) was rejected (the advisor agreed). side_l and side_r must have equal probability because mirror augmentation turns a left fall into a right fall. The advisor had proposed supine and prone only for R0; the spec included side and mid because the feasibility accounts showed physical solutions for all of them, and wrote "narrow back to supine+prone" down as the first fallback. With no display on the training box the reset was checked numerically instead of by eye.

  Change

  Category mix as above; realized shares, settle height and penetration measured over 512 envs before the first run. A fallen-state bank (real falls, settled and stored) was pre-registered for R2.

  Outcome

  Realized shares 29.3/31.6/16.4/17.8% against the config, settle +0.262 m, final penetration 0/512 (a 0.10 m peak at the write instant, ankle links only, pushed out within 80 ms because the 0.28 m drop floor is shorter than a fully extended leg). R0's failure was a reward basin, not a reset artifact. The fallen-state bank stayed unbuilt through V3.1 (checklist item open); R0.3 later re-sliced the prone share into roll_l/roll_r bands, which is what forced the acceptance distribution to be frozen separately.

  Mechanism

  A category-structured, physically settled start distribution keeps training on states the robot can actually occupy, and equal mirrored shares keep mirror augmentation a pure doubling of data rather than a bias.

  Applies when

  • designing reset distributions for get-up, recovery or multi-contact skills
  • mirror/symmetry augmentation is on and the task has chiral start states
  • no viewport is available to inspect resets on the training machine

  “角度 jitter ±15°、yaw 全域、0.28~0.40 m 低空放下由物理沉降,关节软限位内 均匀(留 5% 余量)+ 小随机速度。**不用 random SO(3)**(会采出穿地/极限卡死 等现实不可能状态,顾问同判) … side_l/side_r **概率必须相等**(镜像增强的样本同分布前提)”

• Torque caps cannot soften footfalls - impact is falling-mass momentum, only the reward can treat itlanding-impact-not-fixed-by-torque-caps

  Mechanism understoodwalkreward-shapingreward-shapinghardwareactuator-modeling

  Classify each hardware symptom by the physics that sets it: quantities fixed by ballistic momentum at contact must be treated through the policy's trajectory (reward terms on approach velocity/force), never through actuator caps - and size such penalty weights against your own tracking reward, not a lighter robot's.

  Symptom

  Footfalls slammed at 1.78x body weight in sim baseline (human walking: 1.2-1.5x); the tempting hardware-side fix was cutting actuator torque limits.

  Context

  Measured directly: scaling torque limits from x1.0 down to x0.4 left peak landing force essentially unchanged (1.75 -> 1.78x body weight) - the impact force comes from the momentum of the falling mass at touchdown, not from motor effort. The fix has to change the trajectory, i.e. the policy, i.e. the reward: feet_contact_forces penalty above a threshold of 113 N (= 1.2x the 9.58 kg robot's weight), clipped, weight -0.005. The weight was sized locally, not copied: the reference robot's -0.001 would amount to 0.9% of tracking reward on this robot ("策略不会理它" - the policy would ignore it); -0.005 gives 4.4%.

  Change

  Added threshold-type contact-force penalty (-0.005, threshold 1.2x body weight) as one of v6-minimal's three changes; hardware torque cuts explicitly rejected as a footfall treatment.

  Outcome

  Landing force 1.72x -> 1.55x by v6 (target <1.5x, missed by 3% - progress booked honestly); the torque-cap dead end was documented so it would not be retried.

  Mechanism

  At touchdown the ground stops a ballistic mass; the impulse is set by approach velocity and effective inertia, which motors can no longer influence in the final instant. Only earlier trajectory choices (approach velocity, timing) reduce it - and those are selected by the reward, not by actuator limits.

  Applies when

  • footfall impact or landing noise on hardware
  • proposals to derate torque as a softness fix
  • importing contact-force penalty weights from another robot

  “⚠️ 硬件限扭降不了落脚力 —— 砸地力来自下落质量的动量: 实测 tau ×1.0→×0.4, 落脚力 1.75→1.78× 体重纹丝不动。只有这条奖励能治。… ⚠️ 权重不能用 Pi 的 −0.001 —— 实测在我们身上只占跟踪奖励的 0.9%, 策略不会理它 (Pi 6.94 kg 更轻)。−0.005 给到 4.4%。”

• action_rate weight is the sim2real bandwidth knob - re-tune it whenever a rate limiter is removedaction-rate-weight-vs-bandwidth

  Observed oncewalkreward-shapingaction-ratereward-shapingactuator-modeling

  Set action_rate weight relative to real actuator bandwidth, and re-tune it any time another smoothing/limiting element (filter, slew limiter, gain) changes - reward weights are load-bearing parts of the actuator model.

  Symptom

  With a low action_rate_l2 weight the policy learns fast actions; the unmodeled part of the actuator response is then excited hardest, and sim2real "直接崩" (collapses outright). With too high a weight, actions become so slow the robot cannot maintain balance.

  Context

  The reference developer called action_rate_l2 the single most important reward for transfer, with side-by-side video evidence that the high-penalty, slower policy is clearly better on hardware. Lucen context: the team had just removed the SOFT_SPD=1.0 velocity limiter, which had been an implicit actuator-bandwidth constraint - leaving action_rate as the only remaining constraint on action speed.

  Change

  Decision recorded: after removing SOFT_SPD, re-evaluate the action_rate weight rather than keep the old value, since its effective role changed from "additional smoother" to "sole bandwidth constraint".

  Outcome

  Logged as a priority follow-up ("重新评估 action_rate 权重 - 拆掉 SOFT_SPD 之后这一项的作用变了"); the failure mode it guards against is training high-frequency actions the real actuators cannot track.

  Mechanism

  Slower actions stay inside the frequency band where the ideal-PD sim actuator and the real actuator agree; fast actions probe the band where unmodeled delay, inductance, and bandwidth limits dominate, so model error is amplified in exact proportion to action speed. Any removed external rate limit transfers that constraint's entire job onto the action_rate penalty.

  Conflicts

  The low/high tradeoff evidence is the external developer's report (with video); the Lucen-side entry is a pre-registered risk and decision, not yet an on-robot A/B at the time of writing.

  Applies when

  • removing or adding an action filter, slew limiter, or low-level speed cap
  • real robot shows high-frequency chatter or overheating absent in sim
  • tuning smoothness rewards before a hardware deployment

  “权重低 → 动作快 → 执行器模型不准的部分被放大，sim2real 直接崩 / 权重高 → 动作慢 → 好迁移，但可能慢到无法维持平衡 … 我们刚拆掉 SOFT_SPD=1.0 的限速器，等于把执行器带宽约束整个移除了。action_rate 惩罚现在是唯一还在约束动作速率的东西，需要重新评估权重”

• Isaac splits Coulomb friction into static and dynamic columns - wiring only static means zero loss during motion, silently discarding the identified valuesim-api-friction-columns

  Mechanism understoodinfraplant-calibrationplant-calibrationactuator-modelingdomain-randomization

  When installing identified actuator parameters, map each measured quantity to the simulator's exact API column for the operative regime (dynamic for moving loss, viscous for damping), verify per joint after landing, and audit how randomization intervals fall on each column's nominal.

  Symptom

  The hardware-identified Coulomb friction (tau_c) was about to be installed into the trainer through the friction= field alone - which in Isaac 5 populates only STATIC friction, so during motion the joints would lose no torque at all: "只给 static 则运动中不损耗, 辨识的 τ_c 走路时等于没接" (the identified tau_c would effectively not be connected while walking).

  Context

  The v12 integration wired all three columns deliberately: armature= and friction= from the 2026-08-04 hardware identification, PLUS dynamic_friction= (Isaac 5 splits Coulomb into static/dynamic; the moving-loss column is dynamic) and viscous_friction= (= the measured joint damping 0.02, aligned to MJCF's damping). Each value was re-checked per joint after landing. A DR interaction was audited and booked rather than hidden: randomize_joint_parameters jitters ALL friction columns with ONE interval - the [-0.05, +0.10] band was calibrated against the Coulomb nominal, and landing on the viscous nominal 0.02 it becomes [0, 0.12], "偏宽但保守" (wide but conservative), accepted with the note that pre-viscous behavior was already [0, 0.10] on a base of 0.

  Change

  Measured actuator parameters installed across all applicable API columns (armature, static, dynamic, viscous), with the DR side effect on shared randomization intervals audited and recorded.

  Outcome

  The first generation where the identified plant actually acts during motion in the trainer; the silent-column failure mode documented before it cost a training run.

  Mechanism

  Physics engines decompose "friction" differently (single coefficient vs static/dynamic/viscous columns); a measured parameter is only installed when it reaches the column the solver reads in the regime that matters (motion, not stiction). Randomizers that share one interval across columns rescale the band by each column's nominal - a hidden unit change.

  Applies when

  • installing identified friction/armature into any trainer
  • porting plant parameters between simulators or engine versions
  • joint losses in sim do not match bench measurements during motion

  “并额外传 dynamic_friction=(Isaac 5 把库仑拆 static/dynamic 两列,只给 static 则运动中不损耗,辨识的 τ_c 走路时等于没接)与 viscous_friction=(= joint_damping 0.02,对齐 MJCF damping)。… randomize_joint_parameters 用同一个 friction 区间抖三列, [-0.05,+0.10] 是按库仑标称标的,落到粘滞标称 0.02 上成了 [0,0.12]”

• Audit which joints your imitation term constrains - a task that needs deviation is fighting the referenceimitation-term-scope-audit

  Mechanism understoodomnireward-shapingreward-shapingcurriculum

  List which joints your imitation/deviation terms actually constrain and check the new skill's required motion against that list; for balance-coupled joints deliver references as feed-forward residuals, not absolute-position targets - and never assume "reference = 0" is neutral.

  Symptom

  Sidewalk would not learn despite a dedicated tracking reward; meanwhile the gait-shaping imitation term (joint_pos_ref) computed its error norm over ALL 12 joints while its reference covered only the 6 sagittal joints - roll/yaw reference was constantly 0.

  Context

  Two prior generations had shown the forward gait itself was taught by joint_pos_ref, not discovered by PPO (v6 halved the shaping and swing height collapsed 35 mm -> 4 mm). So the reference is load-bearing - but sidewalk requires hip_roll to deviate from nominal, and the all-joints norm punished exactly that deviation: "一边悬赏一边罚过程" (posting a bounty while punishing the process). A follow-up experiment (C4-redo3, free_roll=True releasing the 4 roll joints from the norm) raised the regularization headroom 6x -> 27x yet sidewalk stayed flat and released hip_roll wandered, killing other skills - net negative, withdrawn. A --roll-absolute probe showed the converse failure: pinning roll to a clock-driven absolute trajectory drove tilt 6.9 -> 13.7 deg. Conclusion recorded: absolute-position imitation cannot teach actions that must be superimposed on state feedback.

  Change

  The audit reframed the problem: neither punishing roll deviation nor freeing roll nor absolute roll tracking works; the reference for a balance-coupled joint must be delivered as feed-forward under the policy's residual control (see feedforward-for-phase-locked-skills).

  Outcome

  free_roll rung: joint_pos_ref term rose 0.887 -> 1.104 (release confirmed effective) but vy stayed flat; regularization hypothesis eliminated by experiment.

  Mechanism

  An imitation error norm defines a cage: joints inside it are pulled to the reference in absolute position, so any skill requiring systematic deviation is taxed per step; but joints carrying active balance cannot follow absolute references either, since their correct position depends on state. The scope and the delivery mechanism of the reference are therefore design decisions per joint, not defaults.

  Applies when

  • adding a skill that moves joints your reference sets to zero/nominal
  • an imitation or deviation penalty coexists with a new tracking reward
  • considering releasing joints from a shaping term mid-lineage

  “前进步态也不是 PPO 自己发现的，是 joint_pos_ref 教出来的（v6 砍半塑形 → 抬脚 35 mm 塌到 4 mm…）。而 ref_joint_offset 原本只写 6 个矢状面关节，roll/yaw 参考恒 0 —— 侧走既没被教，roll 一偏离 nominal 反被 joint_pos_ref 扣分。一边悬赏一边罚过程。”

• A joint-velocity penalty meant to slow the get-up cut joint speed 16% and left the get-up time unchanged - the knob never moved the variable, so the idea it was meant to test stayed untesteddof-vel-penalty-is-not-a-pacing-knob

  Mechanism understoodrecoveryreward-shapingreward-shapingattribution

  Before reading a result as a test of an idea, check that the knob actually moved the independent variable; velocity regularizers smooth a schedule they do not set, and a schedule driven by per-step task income moves only when that income's time structure does.

  Symptom

  The get-up took 0.6-0.9 s in Isaac with large torque demand; the user proposed getting up more slowly so less torque would be needed.

  Context

  The idea had support in the accounts: the acceptance bound is an upper bound of 5 s (5-8x margin), the quasi-static squat path peaks at 25% of the limits, and rolling over needs no momentum. R3.2 raised dof_vel from -1e-3 to -5e-3 as the single variable.

  Change

  dof_vel -1e-3 -> -5e-3 (child-run from R3.1).

  Outcome

  Get-up medians moved +0.02-0.04 s (noise); raw joint velocity -16%; torque demand median got worse (hip_pitch 46-48% -> 63-67%) as the new term competed with torque_headroom on the same joints; MuJoCo 98 -> 96%. Verdict FAIL on the knob, not on the idea, and the rung was not adopted. When pace was later attacked through the income's time structure (V2.5/V2.5b), the MuJoCo get-up moved into the 3.5-4.5 s design band.

  Mechanism

  The pace was set by base_height_progress paying for every step spent high (stand earlier, earn more); a velocity regularizer only smooths motion along the same schedule and does not change when the robot stands up.

  Applies when

  • trying to make a skill slower or gentler with smoothness penalties
  • an experiment's primary metric did not move and a verdict is being written
  • two penalties act on the same joints

  “**关键判读:`dof_vel` 罚只把关节速度压了 16%,而起身用时一点没变。** 也就是说**这一级根本没有把"慢下来"这个自变量推动起来** —— 所以它**不构成对 用户假说的检验** … 起身节奏由 `base_height_progress` 的逐步计酬决定(早站起来就多 拿),速度正则只在同一条时间轨迹上把动作抹匀,不改变何时站起来。”

• The +/-50 mm lateral COM randomization meant to spread the legs coincided with legs pulling IN - rolled back per its own pre-registered contractcom-dr-rollback-on-symptom

  Observed oncewalkdr-tuningdomain-randomizationattributiongate-battery

  When adopting a DR value that covers no local measurement, write its intent and rollback trigger into the config at adoption time; roll it back as the control arm the moment the symptom contradicts the intent, and promote the symptom's metric into the acceptance battery.

  Symptom

  After v7 adopted the reference developer's oversized lateral COM randomization (+/-50 mm) explicitly to force leg spread, the real robot's legs narrowed instead - lateral mean 154 mm / closest 107 mm in sim (nominal 214.5), narrower still on hardware with occasional leg contact.

  Context

  The rollback was clean because the adoption had been honest: the robot.yaml comment recorded the intent AND that the +/-50 value covered no local measurement (only a 16/7 mm measured offset existed; even the prior widening to +/-20 was subjective), plus the reference's own reported side effect (base sway) and the note "这一项要单独跑、 单独归因". When the opposite symptom appeared, v8 returned y to +/-20 mm as the control arm ("要么没起作用、要么帮了倒忙 … 按约定退回做 对照"), kept x/z untouched (a noise-level difference not worth another variable), and named the second suspect: the landing penalty itself, via the reference's own three-link chain (landing penalty -> stance narrows -> spacing penalty needed). A gate lesson was booked in the same table: v7's sim numbers had ALREADY crossed the line (154/107 vs v5's 182/147) - "这个指标本可拦下 v7" - so foot-distance became a standing acceptance row (min >120 mm, zero leg-leg contacts).

  Change

  base_com_offset_m y: 0.050 -> 0.020 (x/z kept), regenerated through the export tool rather than hand-editing derived files; foot-distance acceptance row added.

  Outcome

  A borrowed DR lever with no local measurement basis was retired the moment its symptom contradicted its purpose, at single-variable cost; the metric that would have caught it pre-hardware entered the gate.

  Mechanism

  DR ranges shape behavior through the policy's robustness strategy, which is jointly determined with every reward term; a lever that forces stance width on one robot can be dominated by a stronger narrowing pressure (landing softness) on another. Levers adopted without local measurement must carry their own rollback trigger, because there is no nominal to argue from when they misbehave.

  Conflicts

  Causality is not fully closed in the source: the narrowing may come from the landing penalty rather than the COM lever ("腿距的第二嫌疑人是 ④ 本身"); the rollback is the pre-agreed control experiment, not a verdict that the lever caused the narrowing.

  Applies when

  • importing DR ranges or behavioral-forcing randomizations from references
  • a DR lever's observed effect contradicts its documented purpose
  • a sim metric existed that would have caught a shipped regression

  “⑥ 的本意 … 是逼策略把脚分开;真机结果是脚向内收且偶发相碰——要么没起作用、要么帮了倒忙。… 注释当时就写了"这一项要单独跑、单独归因"。现在症状出现了,按约定退回做对照。”

• Three times a joint-angle stand-in for a foot-level quantity was gamed or lied - the absolute ankle roll sold stance width to buy flat feet, a hip-roll criterion was confounded by 50 deg of yaw, and the joint table said 0.271 m where the feet were 0.159 m apartjoint-space-proxy-for-task-space-quantity

  Replicatedrecoveryreward-shapingreward-shapingmeasurementreal-acceptance

  Express foot-level (task-space) goals and acceptance criteria in task space - link attitude and lateral spacing from world poses - never through joint angles that assume other joints are at zero, and measure the task-space value before trusting a joint-space estimate of it.

  Symptom

  The line's first real-robot get-up (v2_6, 2026-08-11) was "fairly stable", but after standing the feet were too close and the robot slid into the splits and fell several times; later reports added that it also got up through a split posture.

  Context

  (1) flat_feet penalized sum |q_ankle_roll|, but a flat foot is ankle roll compensating hip roll; the proxy taxed the compensated wide solution, and the untaxed combination was hips straight plus ankles at zero - flat and narrow. On hardware the lateral support shrank to hip width plus centimetres, lateral balance rested on 11 N*m ankle motors, and the feet slid apart. (2) The next rung's acceptance criterion "hip roll >= 20 deg" assumed zero hip yaw; at 50 deg of yaw the lateral contribution is x cos 50 ~ 0.64 - the same substitution again, inside a criterion. (3) A new task-space diagnostic reading the foot links' world poses measured v2_6c's stance at 0.159 m where the kinematic audit from joint angles had said 0.271 m (0.271 x cos 47 ~ 0.17).

  Change

  Rule written into the spec: task-space quantities are never expressed through joint-space proxies. V3.1's stance terms were all task-space: flat_feet_task from the foot links' world orientation, lateral foot spacing in metres, stand_pose stripped of both roll joints.

  Outcome

  From scratch with task-space terms (V3.1 P1c): lateral stance 0.355 m, foot residual tilt median 0 deg / P75 2.0 deg, all six criteria passing, mu 1.0-0.4 all 100%.

  Mechanism

  A joint proxy bundles the goal with everything else those joints do; the optimizer finds the combination the proxy does not tax, and a joint-based criterion silently assumes the other joints sit at their nominal.

  Applies when

  • rewarding flat feet, stance width, foot placement or end-effector pose
  • an acceptance criterion is written in joint angles for a geometric goal
  • joints with large yaw or coupled axes are involved

  “**病根 = 关节空间代理**:`flat_feet` 罚 Σ|q_ankle_roll|(§41 取的简易口径)。 "脚掌平"的运动学正解是 **踝滚补偿髋滚**(q_ankle_roll ≈ −q_hip_roll) … 代理把"脚平"和"站距"绑死在一起卖了。”

• Prone get-up sat at 0/159 until two gated hinge terms moved the seated feet - first sideways (561 -> 360 mm), then fore-aft (-168 -> +56 mm) - and success went to 158/159 with nothing else changedprone-dead-end-is-foot-placement

  Mechanism understoodrecoveryreward-shapingreward-shapingattributioncurriculum

  When a stuck state and a successful state differ geometrically, penalize the discriminating quantity with a gated hinge that is exactly zero in the state the policy actually reaches (measure it - not the nominal), then re-probe: flattening one axis can move the discriminant to another.

  Symptom

  Prone falls always righted and then sat with the feet splayed wide or tucked behind the hips, from where the policy never stood (0% for four generations).

  Context

  Four lines of evidence pointed at foot position: the configuration probe (ankles 215 mm apart stood 52.3%, 561 mm apart 0.0%); FK showing the action contract's nominal (a = 0) is itself a 465 mm straddle, so the action_rate and still terms were pulling toward the splits; biomechanics (feet tucked under the body cut peak hip-extension torque 148.8 -> 32.7 N*m, -78%); and HoST's foot-displacement term, which this reward table lacked. The earlier "not a reward hole" reading was corrected to "a gradient hole, not a level hole": at the dead point the heaviest term (upright) was saturated with zero gradient, still paid for not moving, and the one live gradient (base_height) pointed at the thigh-horizontal torque barrier. A prone ROM scan had already ruled out pushing up from prone.

  Change

  R0.4: feet_spread_excess = clamp(ankle distance - 0.215, 0, inf) x upright gate, weight -2.0, plus a height-decay factor added after measuring that the policy's real standing stance was 406 mm, not the 215 mm nominal (the plain version would have taxed every successful stand 0.38/s). R0.5: the same shape on the fore-aft axis, feet_fore_seated = |fore-aft offset - 0.05| x upright gate x height decay, target +50 mm (the measured natural offset of standing postures). One variable per rung.

  Outcome

  R0.4: seated ankle distance 561 -> 360 mm, supine/side exactly unchanged, prone 0 -> 1.9%, mid 45.9 -> 62.2%; a probe then showed the discriminant had moved to the fore-aft axis (standing starts +42 to +51 mm, the prone seat -168 mm). R0.5: supine 99.4, prone 99.4, side 100, mid 100%, re-falls 0%; both geometry terms collapsed to ~0 near iteration 13,100 as base_height rose, and the prone fore-aft offset went -168 -> +56 mm - the term's own target, closing the causal chain. The cost, unmeasured at the time: action jitter rose 33% (sum |da|^2 6.82 -> 9.06).

  Mechanism

  An upright-gated hinge is inert while the robot rolls and exactly zero in the achieved stance, so it adds gradient only inside the stuck basin; a seated robot with its feet behind or outside its COM must make a kinematically unfavourable transition to stand, and moving the feet under the body removes it.

  Applies when

  • a get-up or transition skill fails from one start category only
  • successful and failed episodes differ in a measurable geometric quantity
  • a shaping term might tax the posture successful episodes already use

  “`recovery_r0_5`,唯一变量 = 追加 `feet_fore_seated`(与 R0.4 同形状,只换测量轴)。 … 对照 R0.4 的 prone(3.1%,360 mm,**−168 mm**):前后偏移从 −168 走到 +56, 正是这一项的目标量,**判别量被消掉后成功率随之到顶** —— 因果链完整。”

• Four in-lineage attempts to widen the standing stance failed - remove a tax, add a joint-space knife, change the target, add a task-space metric penalty - because the stance was the end state of the get-up path; trained from scratch with the right terms it grew right from day onestance-decided-by-get-up-path

  Replicatedrecoveryattributioncurriculumfork-selectionreward-shaping

  A posture a skill ends in is shaped by the path the policy takes to reach it; if several single-variable edits to the terminal-phase reward cannot move it, stop editing that phase and retrain with the terminal constraint present from the start.

  Symptom

  v2_6c stood with its feet 0.159 m apart (task-space) and its hips yawed 45-47 deg the same way, which split on the real robot. Standing-phase reward edits did not move it.

  Context

  V2.7-A removed the flat-feet tax on compensated stances (stance unchanged); V2.7b added a hip-roll lower-bound hinge (+5 deg in 3,000 iterations, yaw ratchet); V2.8 changed the stand_pose target to a wide flat stance (stance unchanged, yaw not unwound, feet nearly overlapping, mu 0.4 transfer 2%); V2.9 penalized lateral spacing in metres (the policy parked just outside the penalty's gate in a lunge, 0% success). The v2_6c get-up goes through a split and closes the feet together as it rises.

  Change

  In-lineage stance surgery was formally closed. V3.1 trained from scratch with task-space stance terms present from the first iteration (and, after P1, a positive width band instead of a penalty).

  Outcome

  V3.1 P1b: lateral stance 0.364 m, foot tilt 0.0 deg, all four categories 100%, MuJoCo mu 1.0 and 0.4 both 100% - with a symmetric toe-out the kinematic audit had not enumerated. P1c (with a yaw guard): 0.355 m, all six acceptance criteria passing, mu 1.0-0.4 all 100%; it became the product.

  Mechanism

  A converged policy does not rebuild the path that produced its terminal posture; a standing-phase gradient only finds the nearest hack around the posture the get-up delivers.

  Applies when

  • the final posture of a transition skill is wrong and resists terminal-phase shaping
  • repeated continuation rungs produce hacks instead of the intended posture
  • deciding between another in-lineage fix and a from-scratch retrain

  “窄站距 + yaw 扭是 v2_6c 起身策略(劈叉起身 → 双脚并拢收势)的**结构性 终态**,不是站立段的孤立参数 —— 站立形态由起身路径决定,在血统内只动 站立段奖励改不动它。”

• Fall recovery was defined as the whole chain - any fallen pose, a stable stand, a clean hand-back to walking - and built as a second policy behind a deploy-side switch, not folded into the walking PPOrecovery-two-policies-and-a-state-machine

  Observed oncerecoveryprocessprocesscontract-freezereal-acceptance

  Define a recovery skill by the whole chain it must complete, including the hand-back to the next controller; if it is built as a separate policy, make the switching logic and its handoff contract a deliverable of their own, and keep the recovery observation contract a subset of the locomotion one so a unified policy stays possible later.

  Symptom

  A walking robot that falls needs a human to stand it back up. The design question on 2026-08-09 was whether to teach getting up inside the existing omni walking policy or beside it.

  Context

  The user set the goal as "any fallen pose -> stand up alone -> stand stably", and the spec named the real difficulty as the full chain fall -> recovery -> stable stand -> correctly initialised walking history and clock -> walking, making the deploy state machine a first-class deliverable. A unified single policy had a real-robot precedent (arXiv:2605.18611, a state-dependent gate near 37 deg tilt) but was deferred until a recovery policy and an omni policy were each reliable. The line ran on its own branch and worktree with every walk/stand/omni/run config path untouched. The development path copied the G1 learned get-up logic (arXiv:2502.12152): first find any feasible get-up (ugly accepted), then add smoothing, torque and real-robot constraints. The recovery contract kept the base 45-dim observation (command slice held at 0, no gait phase, no frame history - their reasons do not apply to a skill without a clock or a velocity task), so it stays a prefix of the 215-dim omni contract and a later merge is not foreclosed.

  Change

  Two policies and a deploy-side switch instead of one retrained walking policy; recovery got its own minimal contract (45 dims, full-range action, later the beta-anchored profile) and its own acceptance battery.

  Outcome

  The split held for the whole line: on 08-14 deploy_policy gained a second (PolicyIO, ONNX) pair behind --recovery-policy, each loaded under its own manifest contract, and the runbook runs stand_v1b or omni_c4_ff800 as the locomotion side with recovery_v3_1p1c. The literature scan of 08-10 found that every verified get-up implementation deploys one end-to-end policy (or softly gated experts) and stages only on the training side - so the runtime state machine here is the walk/recovery switch, not a staged get-up.

  Mechanism

  A separate policy keeps each reward table single-purpose and lets a proven walking lineage stay byte-frozen; the cost moves to the handoff, where every piece of state one policy leaves behind (history, clock, last action, command) must be reset for the other.

  Applies when

  • adding fall recovery or get-up to a robot that already walks
  • choosing between one unified policy and a switched pair of policies
  • designing the observation/action contract of a secondary skill

  “先做 recovery policy + omni policy 两个策略,部署侧状态机切换;不把 recovery 硬塞进现有 omni PPO。 … 任务定义:**任意跌倒姿态 → 自己站起来 → 稳定站立**。真正的难点不只是"起身", … omni walk**(§6 部署状态机是本 spec 的一等公民,不是附录)”

• Two simulators disagreed 1.8x on one policy's torque demand - four explanations were eliminated with numbers, the surviving suspect was never tested, and neither reading was allowed to cancel the othertorque-disagreement-between-simulators-unresolved

  Hypothesisrecoverysim2sim-gatesim2simactuator-modelingattribution

  When two simulators disagree on a safety-relevant quantity, eliminate the measurement explanations one at a time with numbers, name the survivor as a hypothesis, keep the pessimistic reading binding, and run the direct test (replay one action sequence open-loop through both plants) before the quantity is used to pass a gate for hardware.

  Symptom

  For R3.0, Isaac read hip_pitch torque demand at 75-80% of the limit (PASS); MuJoCo read the same policy at 148-152% (1.5x over the limit).

  Context

  Candidates were eliminated one by one: PD gains (identical, RS06 kp 30 / kd 1.5), the settle window (both skip the first 0.5 s), the statistic (worse-of-two-legs vs per-joint - explains ~15%), self-collision (the no-contact subset still read 152%), sampling (500 Hz peak vs 50 Hz sample - ~9%). About 1.8x remained. The prime suspect - Isaac's implicit actuator (PD solved inside the PhysX integration, chosen to mimic kHz firmware PD) against MuJoCo's 500 Hz explicit PD - was named but untested.

  Change

  The Isaac PASS was recorded as valid for the Isaac plant only; the prescribed decider was an open-loop replay of one action sequence through both plants, compared step by step, needing no training. It was upgraded to a precondition of R3.1.

  Outcome

  R3.1 ran in parallel with, not after, the replay, and the replay never appears as done. By §40 it was "the oldest open account" on the line, to be fed by real-robot logs - which the spec also never records. Meanwhile the 50 Hz sample under-read the peak by 34% as smoothing narrowed the spikes, so Isaac-side readings grew more optimistic exactly when the comparison mattered more.

  Mechanism

  Each measurement artifact explained a slice of the gap; what remained is a plant difference, and an untested plant hypothesis cannot license discarding the pessimistic simulator on a safety-relevant quantity.

  Conflicts

  The spec prescribes the open-loop replay (§23), upgrades it to an R3.1 precondition, then records that R3.1 ran in parallel without it (§24), and lists it as the oldest open account before hardware (§40); nothing through §50 (2026-08-14) records a result. Every later "torque gate PASS" on this line is an Isaac-plant reading plus a MuJoCo check, never a reconciled one.

  Applies when

  • Isaac and MuJoCo (or sim and hardware) report different torques, contacts or slips
  • implicit vs explicit actuator models are in play
  • a gate passes on one simulator only

  “**同一个策略,一侧判 PASS 一侧判超限 1.5 倍。** … 口径项全部扣掉后仍剩 **~1.8×** 没有解释。 … 但**这条没有验证,不能拿它当结论去抵消 MuJoCo 的读数**。 … (做法:拿同一条 动作序列在两边开环回放,逐拍比 τ —— 不需要重训)”

• Drop the frozen policy into chosen configurations - a squat 2.7 cm lower than the stuck pose stood 52% of the time, the stuck W-sit 0%, and the interpolation between them showed a wall, not a slopeconfiguration-probe-wall-not-slope

  Mechanism understoodrecoveryattributionattributionmeasurementcurriculum

  When a policy is stuck, probe the frozen policy from a grid of hand-placed start configurations, including interpolations between the stuck state and a nearby state it escapes from; one read-only experiment separates height, torque, sampling and configuration and tells you whether to prevent entry or train the exit.

  Symptom

  After R0.3 the policy stood from 62% of starts and never from the W-sit it fell into; height, torque, missing samples and reward were all plausible suspects.

  Context

  A read-only probe placed the R0.3 policy directly into specified configurations. Squats (hip, knee, ankle) = (-.65,-1.3,-.65) stood 100%, (-1.0,-2.0,-1.0) 89.8%, (-1.2,-2.4,-1.2) at 0.176 m 52.3%; the measured W-sit at 0.203 m 0.0%; the account-(3) hand-over state (146 deg tilt) 34.4%; linear interpolations from the W-sit toward the squat at 25/50/75% stood 0.0/0.0/3.1%. The squat family's quasi-static torque is 16% of the limits, and the W-sit was visited ~9 s per episode in training. FK showed the squat family (-a,-2a,-a) keeps the torso vertical, the feet flat and the COM over the feet all the way from 0.146 m to 0.384 m.

  Change

  Height, torque and sampling were eliminated in one experiment; the next rungs targeted entering the W-sit (foot placement) instead of escaping it, and seeding the dead point itself was ruled out because it was already visited every episode.

  Outcome

  Pure configuration: the W-sit (hips externally rotated +/-47 deg, knees folded 110 deg, shins flat, feet beside the body) is a different place from the sagittal squat (feet flat under the COM). The policy's standing skill was bound to a narrow sagittal family, and the wall was confirmed by the interpolation. The foot-placement rungs that followed took prone from 0/159 to 158/159.

  Mechanism

  A learned skill covers the neighbourhood of the states it succeeded from; a start state outside that neighbourhood fails regardless of height or torque, and an interpolation that stays at zero until close to a working state shows the boundary is sharp.

  Applies when

  • a policy stalls in a specific posture and several causes are plausible
  • deciding between reverse-curriculum seeding and entry-prevention shaping
  • a feasibility account says a path exists but the policy does not take it

  “**决定性对比:比死点矮 2.7 cm 的蹲姿站立 52.3%,死点 0.0%。** 所以不是高度、 不是力矩(蹲姿族准静态力矩膝 1.96/12、踝 1.24/17,只占 16%)、也不是训练采样 (死点每局被访问 ~9 s)。**是纯位形问题** … 插值实验进一步显示这**不是坡是墙** —— 走到 75% 仍只有 3.1%”

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