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

31 cards matching “fork-root-recoverable-shortfall”.

• Choose the fork root by which candidate's shortfalls are recoverable, not by headline scorefork-root-recoverable-shortfall

  Mechanism understoodomnifork-selectionfork-selectionprocess

  When picking a checkpoint to fork from, rank candidates by whether their weaknesses are trainable-back, not by current headline metrics; prefer the candidate whose deficits the upcoming training directly pays for.

  Symptom

  Multiple candidate checkpoints for the omni-command ladder root, each best at something different: fric-3000 had the best tracking precision (vx 88-91%) and hardened plant robustness; s1e-500 had lower precision (vx 82%) but was the only candidate that could still walk backward.

  Context

  Root selection ran as a data probe, not a preference vote: 6 candidates x 8 out-of-distribution omni commands x 20 seeds = 960 cells (probe_omni_0808.json). s1e-500 @pw1.0 survived 20/20 in all eight conditions including backward at 67% tracking; the deep-trained fric lineage scored backward 0-3/20 despite better forward precision.

  Change

  Decision criterion made explicit: list what each candidate exclusively wins at, then ask which of those wins the loser could train back. fric-3000's exclusive wins (precision, plant robustness) are both retrainable - precision is directly optimized by the reward, plant hardening is a planned later pass. s1e-500's exclusive wins (backward plasticity 20/20 vs 2/20, disturbance margin 159/160 vs 125/160, push chirality symmetry 40/40 vs 17/40) had all been shown unrecoverable - push-level rungs failed twice, chirality never recovered even with mirror augmentation on. Root = s1e-500.

  Outcome

  s1e-500 carried the whole C ladder; its backward skill was preserved through C2/C4 gates (regress budget <=2/20 enforced), and the final C4 product passed a 260-cell battery at 20/20 everywhere.

  Mechanism

  Training can re-earn anything the objective directly pays for, but capabilities that earlier training destroyed and never restored (plasticity, symmetry, robustness margins) are empirically one-way doors. The information-bearing comparison is therefore recoverability of each candidate's deficit, which the team stated as "独占项的可恢复性正好相反 —— 这就是判据" (the exclusive items' recoverability is exactly opposite - that is the criterion).

  Applies when

  • selecting a resume/fork root among several checkpoints
  • one candidate is more precise but another retains a skill the rest lost
  • planning a task-extension ladder from an existing lineage

  “fric-3000 赢在精度（vx 88~91%…）与 plant 鲁棒性 → 两样都训得回来…；s1e-500 赢在可塑性（C1 20/20 vs 2/20）、抗扰余量（159/160 vs 125/160）、手性对称（推 ±6 N·s 40/40 vs 17/40）→ 三样都训不回来 … 独占项的可恢复性正好相反 —— 这就是判据。”

• The best checkpoint to SHIP is not the best checkpoint to CONTINUE FROM - maturity is capital against adaptation shockroot-maturity-vs-product-quality

  Mechanism understoodomnifork-selectionfork-selectioncurriculumgate-battery

  Decide shipping points and fork roots separately: gates rank products, but a root candidate must prove itself by surviving a continuation under the next rung's shift (dual-arm if in doubt) - and prefer the more-trained point as root when product metrics conflict with maturity.

  Symptom

  A band re-audit found s1e-300 beat the incumbent root s1e-500 on nearly every quality gate (stepping 19/20 vs 13/20 with historically-best 26.9 mm swing, speed gate 14/20 vs 2/20, heading 26 vs 54 deg/20 s) - suggesting the root had been mis-picked and the younger point should take over.

  Context

  The dual-arm control settled it the other way: continuing the S2 PD rung from s1e-500 adapted smoothly (3/3 smoke throughout), while the b300 control arm (same config, from s1e-300) fell into a survival valley under the PD shock (+100 iters: 1/3 -> 0/3), never climbed out within budget, and its 800-iter product scored 13/20 survival - eliminated. Verdict: "幼年点自身指标再好也扛不住新 DR 适应冲击, 成熟度是本钱,s1e-500 根被数据背书" - a young point's own metrics, however good, do not survive new-DR adaptation shock; maturity is capital. The audit still yielded value: the band scan (200-1000, per-100) mapped the lineage's arc (200 dragging -> 300 peak -> 400+ decay -> 900+ drift blowout), and 300 remains the better PRODUCT answer for shipping-as-is questions.

  Change

  Selection doctrine split into two questions with different answers: best-product point (quality gates at the point itself) vs best-root point (survives adaptation shocks; more training age = more capital), each decided by its own evidence - and root claims settled by a dual-arm continuation test, not by point metrics.

  Outcome

  s1e-500 kept the root role with data behind it; the S2e ladder built on it passed rung after rung, while the b300 line was closed at the cost of one control arm.

  Mechanism

  Early checkpoints sit near sharp optima with less accumulated robustness structure; their headline metrics reflect the narrow training distribution, not resilience to distribution shifts. A continuation rung is itself a distribution shift, so the root property being selected for is shock tolerance - observable only by actually continuing, never by static gates.

  Applies when

  • a younger checkpoint outscores the current root on quality gates
  • choosing the base for a robustification or command ladder
  • a continuation run stalls in an early survival valley

  “b300 对照臂 … PD 冲击下存活谷(+100 起 1/3→0/3),预算尽未爬出,800 档 20-seed 存活 13/20 出局——幼年点自身指标再好也扛不住新 DR 适应冲击,成熟度是本钱,s1e-500 根被数据背书”

• Pre-register the ladder's risks and how each future result will be read - before trainingpreregister-risks-and-fork-readings

  Replicatedomniprocessprocess

  Before a training ladder or risky rung, write the risks, the stop rules, and how every plausible outcome will be interpreted - then do not edit them after seeing results.

  Symptom

  Without pre-registration, ladder results get rationalized after the fact; the team had already seen post-hoc reads go wrong and adopted written pre-commitment.

  Context

  The C-ladder execution sheet opens with three numbered pre-registered risks: (1) S2 plant robustness will not carry into omni - a full S2 redo is budgeted from the start; (2) the s1e recipe has a collapse valley at iter 1500+ (recorded twice), so every rung runs a watch_ckpt --every 100 smoke loop with stop-on-degradation; (3) the root's OOD survival edge may partly be "it is slower / commits less" - with the reading fixed in advance: if C1 training raises tracking while survival drops, the edge was bought with slowness, and root selection reopens. Later rungs went further, pre-registering a full result-to-conclusion table for the A/B arms ("预注册读法（事后不改）") and even pre-registering the author's own doubt that a level would fail and what its failure would prove.

  Change

  Standing practice: before each rung, write down (a) known risks with their mitigations, (b) the interpretation of each possible outcome, (c) stop criteria - all frozen before the run starts ("开训前写死，事后不许改").

  Outcome

  When arm-A/arm-B and redo results arrived, conclusions were read off the pre-registered table instead of argued; a predicted-likely-FAIL level (C4-redo3) was still run because its pre-registered value was eliminating the regularization hypothesis - which it did.

  Mechanism

  Pre-commitment converts each training run into a decisive experiment: outcomes falsify or confirm named hypotheses instead of being absorbed into a story; it also makes negative results valuable (a FAIL that eliminates a hypothesis advances the search).

  Applies when

  • launching a multi-rung training ladder
  • running an A/B fork whose outcome will drive a fork/root decision
  • a rung is expected to fail but is run for its diagnostic value

  “三条预注册风险 … 若 C1 训后跟踪提上去而存活掉下来，说明这条优势是速度买的、不是通用性 → 那时重开选根。”

• Before resuming a checkpoint, diff the current cfg against what the checkpoint was trained withresume-state-dr-audit

  Replicatedomnitraining-runfork-selectiondomain-randomizationattributionprocess

  "One variable per rung" counts variables against what the checkpoint actually experienced: audit the checkpoint's logged training config and align every unintended difference before resuming.

  Symptom

  Two consecutive rungs (C1 back-mode, C2' forward-turn) failed from the same root with the same full-regression signature despite adding different new modes - so the mode was not the cause.

  Context

  Both runs resumed s1e-500 with the then-current cfg, which carried PD band (0.8,1.2) plus three DR events (base_com, joint_friction, push_robot) accumulated by later lineages. Verified on the training machine from the source of truth (the run's logged params/env.yaml): s1e-500's actual training state was PD +/-10% (0.9,1.1) and all three DR events None. Resuming it under the new cfg meant eating 4 new plant variables plus a new mode at once - the intended "1 variable" was actually 5. A worse variant (c1_redo from s2e_pd-1400) added push +/-0.3 to a root that had never seen it: near-total collapse within +100 iters.

  Change

  C2 aligned the cfg to the checkpoint's training state before resuming (PD back to (0.9,1.1), three DR events off) - making the new mode the only true variable. Permanent rule recorded: compare the checkpoint's training-time DR with the current cfg before any resume.

  Outcome

  C2 trained successfully from the same root that had "failed" twice (wz 20/20 with genuine sign-antisymmetric response by iter 700-800); the A/B falsification ("两个不同模式同签名崩") plus the env.yaml verification closed the attribution.

  Mechanism

  A resumed policy is instantly evaluated (and its value function trained) under whatever plant distribution the cfg specifies; every DR term the checkpoint never adapted to is a distribution shift applied on day one, compounding with the intended change. Single-variable discipline is therefore a property of (cfg diff) x (checkpoint history), not of the cfg diff alone.

  Applies when

  • resuming or forking any checkpoint under an evolved config
  • a resumed run degrades broadly within the first few hundred iterations
  • two different changes from the same root fail with the same signature

  “A/B 定谳：两个不同模式同签名崩 → 病因不是模式，是「从 s1e-500 续训」。… s1e-500 训练态 = kp/kd ±10% (0.9,1.1)，base_com / joint_friction / push_robot 全 None；而 cfg 里带着 (0.8,1.2) + … 三个 DR —— 从它续训等于一次吃 4 个新 plant 变量 + 新模式 … 永久教训：续训前必须比对 checkpoint 的训练态 DR 与现行 cfg。单变量纪律不只看「我改了什么」，还要看「checkpoint 见过什么」。”

• One fixed acceptance matrix for every rung - new skill must PASS while every old skill stays within a regression budgetfixed-acceptance-matrix-per-rung

  Replicatedomnigate-batterygate-batteryprocesscurriculum

  Freeze one acceptance matrix for the whole ladder; every promotion requires the new skill's PASS plus bounded regression on every prior skill, measured against the parent's baseline under the current (bug- fixed) metric code - and include command transitions, not just steady states.

  Symptom

  Sequential skill training silently trades old skills for new ones (C1 trained away the root's backward ability); without a constant measurement frame, each rung's numbers are incomparable and regressions hide.

  Context

  The C ladder ran the same 13-cell command matrix at 20 seeds per cell at every rung (stand; vx +0.15/+0.30; vx -0.10/-0.20; vy +/-0.10; wz +/-0.20; two vx&wz combos; two vx&vy combos), with promotion requiring "新技能 PASS 且旧技能不明显退化" - old-skill regression budget <=2/20 against the parent's recorded 20-seed baseline. For the transition-rich final rung, a command-switch block was added (forward->stop, stop->backward, forward->turn, left->right, turn->forward; survival + re-track within 2 s) because "每个 steady command 都会做 ≠ 命令切换不会摔" - steady-state success does not imply switch safety, and the joystick does switches. The C4 product's gate ran 260 cells (13 x 20) all 20/20.

  Change

  Battery frozen once, reused verbatim per rung; baselines re-measured per parent (and re-measured again after the metric-frame fix, since old baselines were taken with the buggy coordinate reading - "旧基线是坏坐标系的， 不可引用").

  Outcome

  Regressions were caught at the rung that caused them (C1's backward loss, C2's vx+0.30 decay), and cross-rung comparisons stayed valid for the ladder's whole life.

  Mechanism

  A constant matrix makes every rung's output a point in the same metric space, so "did we lose anything" is a table diff, not a judgment call; the per-skill regression budget converts previously earned PASSes into standing constraints on all future training.

  Applies when

  • designing gates for sequential skill addition
  • promoting a checkpoint to be the next rung's root
  • after any evaluation-code fix (old baselines must be re-measured)

  “新技能 PASS 且旧技能不明显退化才晋级。… C5 追加：命令切换验收（steady ≠ transition） forward→stop、stop→backward、forward→turn、left→right、turn→forward，各 20 seed，判存活 + 切换后 2 s 内是否重新跟上。”

• The trainer read a stale USD after the URDF mass update - regenerate derived assets and gate on an automated equality instrumentderived-asset-staleness-check

  Mechanism understoodinfraplant-calibrationplant-calibrationprocesssim2sim

  For every derived plant artifact (USD from URDF, generated value files), pair the generation step with an automated source-vs-derived equality instrument, prove the instrument can fail, gate training on its PASS, and re-run physical audits after every regeneration.

  Symptom

  Measured link masses had been committed to URDF/MJCF (total 9.58 -> 9.792 kg, weighed values), but Isaac reads the derived USD asset - which still carried the old masses: a silent 2.2% mass fork between the training plant and the evaluation plant.

  Context

  The v12 checklist made USD regeneration a hard precondition ("硬性 前置") and, crucially, backed it with an instrument: check_usd_mass.py compares USD vs URDF per-link mass AND inertia trace, validated by showing it FAILs the stale asset naming 9 offending links, then PASSes after re-conversion (13/13 links consistent, total 9.7920). The self-collision filter audit was re-run after the regeneration (three poses, 0.00 N) because a regenerated asset invalidates physical audits done on the old one. This milestone was also where the three plants first aligned: "三边 plant 首次对齐(armature+摩擦+ 实称质量)就在这一代".

  Change

  convert_urdf re-run on the training machine, regenerated asset committed, check_usd_mass.py PASS required as an acceptance gate for the generation; dependent audits repeated post-regeneration.

  Outcome

  The 2.2% plant fork was closed before it could distort a generation's acceptance numbers; the staleness class of bug now has a permanent detector instead of a memory.

  Mechanism

  Source-of-truth edits do not propagate to derived binary assets by themselves; any consumer reading the derivative silently trains or evaluates on the old plant. An automated equality check between source and derivative - proven able to fail - turns an invisible staleness into a red gate, and regeneration invalidates every audit performed on the old artifact.

  Applies when

  • editing masses/inertia/geometry in URDF or MJCF sources
  • a trainer or evaluator consumes converted/derived assets
  • plant numbers differ between simulators for no visible reason

  “25ba997 把连杆质量更新为实称值(总重 9.58→9.792 kg,URDF/MJCF 已改),但 Isaac 读的是 train/assets/laika_v2.usd —— 仍是旧质量。… 否则 Isaac(9.58)与 MuJoCo(9.79)质量分叉 2.2%,v12 验收数字失真。验收门:python tools/check_usd_mass.py 必须 PASS … 对旧资产实测 FAIL/9 连杆点名,仪器已验证”

• Fine-tuning through a reward-table change was falsified (scatter, half-recover, collapse) - continuation training is legal only with the reward frozenfine-tune-reward-change-falsified

  Replicatedomnicurriculumcurriculumfork-selectionreward-shapingprocess

  Never fine-tune through a reward-table change - retrain from zero; reserve checkpoint continuation for frozen-reward plant/DR widening, reset noise_std when branching, and watch for the scatter/half-recover/collapse signature as the abort trigger.

  Symptom

  The s1c A/B experiment: arm B fine-tuned from an existing checkpoint under the revised reward (same contract, same network, changed reward + small DR) and failed with a characteristic signature - scatter, half-recover, fall back ("打散→半恢复→摔回"); arm A trained from zero under the same config won decisively (full shaping lifted swing to 21.6 mm within 500 iters; shipped at 5500).

  Context

  Verdict recorded: "从零 + 强塑形是本机唯一验证过的发育路径" (from-zero plus strong shaping is this machine's only validated development path). The signature became a standing stop criterion in every later rung that touched a reward ("s1c B 臂签名,出现即停"). Crucially the boundary of the law was drawn explicitly when S2 continuation training was proposed: "当年证伪的是「奖励表中途改版的 fine-tune」… S2 续训奖励表全程冻结、只逐级加宽 plant/DR,属另一类" - continuing a checkpoint with the reward FROZEN while widening plant/DR one rung at a time is a different class and was allowed (and then worked, powering the whole S2/C lineage) - with the honest fallback that if frozen-reward continuation ever collapses, that rung retrains from zero and the doctrine gets re-examined with data. Fine-tune arms also need mechanical care: reset the checkpoint's collapsed noise_std (terminal 0.033 "会杀死探索") and account for iteration counters re-zeroing (curriculum gates fire immediately).

  Change

  Reward changes and lineage continuation permanently separated: reward revisions -> from-zero retrain; plant/DR widening -> frozen-reward continuation with per-rung gates; the B-arm signature promoted to a universal tripwire.

  Outcome

  No later reward revision was attempted by fine-tune; frozen-reward continuation carried S2 (PD/COM/friction rungs) and the C command ladder successfully from the s1e root.

  Mechanism

  A trained policy sits in an optimum of its reward's geometry; changing the reward moves the optimum but leaves the policy's exploration noise near-zero and its value function calibrated to the old returns - it disassembles the old solution faster than it can assemble the new one. Widening DR under a frozen reward instead keeps the optimum's identity and asks only for local robustification.

  Applies when

  • proposing to fine-tune an existing policy under a revised reward
  • planning a robustification ladder from a validated checkpoint
  • a continued run scatters then partially recovers then collapses

  “B 臂 fine-tune 证伪(打散→半恢复→摔回——从零 + 强塑形是本机唯一验证过的发育路径)。… 当年证伪的是「奖励表中途改版的 fine-tune」(B 臂,塑形突变致终盘摔回);S2 续训奖励表全程冻结、只逐级加宽 plant/DR,属另一类;若 s2_lag1 续训本身塌方,回退方案 = 该级从零重训,续训教义再议(拿数据说话)。”

• A nonzero response with the same sign for + and - commands is bias, not abilitysame-sign-response-is-yaw-bias

  Replicatedomnisim-evalmeasurementgate-batteryattribution

  Before crediting any directional skill, test both command signs: response must flip sign with the command; a same-signed pair is a bias to subtract, not an ability to report.

  Symptom

  Root-selection probe showed nonzero wz "tracking percentages" on turn commands, tempting the read that candidates could partially turn.

  Context

  During C-ladder root selection, s1e-500's measured yaw rate was +0.084 rad/s for cmd +0.3 and +0.093 rad/s for cmd -0.3 - same sign both ways. The same check on the C2 baseline gave wz+0.20 -> -0.13 and wz-0.20 -> +0.12 (again same sign), while the alternative root s2e_pd-1400 gave +0.16 / -0.16 - opposite signs, i.e. a genuine 16% command response.

  Change

  Reading corrected and written into the execution sheet: percentages on directional commands are meaningless unless the +cmd and -cmd responses have opposite signs; all three candidates were re-classified as "cannot turn, cannot sidewalk - C2/C3/C4 learn from zero". Acceptance criteria thereafter required "tracking >=50% AND left/right opposite-signed".

  Outcome

  Prevented crediting turn/sidewalk ability that did not exist; the antisymmetry clause became a standing part of every turn and sidewalk PASS condition (C2, C4, C4-redo levels all carry "且左右反号").

  Mechanism

  A constant yaw (or lateral) bias projects onto any command's sign convention and shows up as fake fractional tracking; only sign-antisymmetry under command reversal distinguishes a feedback response to the command from an open-loop offset.

  Applies when

  • evaluating turn/sidewalk/any signed-command tracking percentages
  • a candidate shows partial tracking on an axis it was never trained on
  • writing PASS criteria for a new directional skill

  “C2/C3 那些非零的 wz 百分比不是转向能力 —— 转向+ 与 转向− 的实测同号（s1e：cmd +0.3 → +0.084，cmd −0.3 → +0.093 rad/s），那是恒定偏航偏置。… 三个候选都不会转、都不会侧走。”

• Three same-shaped judging errors - task metrics (survival, tracking, displacement) cannot stand in for posture metricstask-metrics-vs-posture-metrics

  Replicatedomnireal-acceptancereal-acceptancegate-batteryattribution

  Keep validated posture-class rows (tilt, per-joint L/R asymmetry, temperature) in every acceptance battery alongside task rows; when operator feel contradicts the gates, suspect the metric class before the operator - and never build a new skill on what is actually an asymmetry defect.

  Symptom

  The C2 product judged "full pass" on task metrics (A800: turn-gap 7 pp, vx+0.30 19/20) felt WORSE in the operator's hands than the half-pass 700: A800 tilted up to 12.90 deg (700: 6.64), drifted left while standing, showed larger per-joint asymmetries, and ran its hip_rolls 5 degC hotter.

  Context

  The re-judgment catalogued three same-type metric errors in one campaign: (1) stand judged by SURVIVAL - missed 0.5-1.4 m wandering; (2) stand ranked by DISPLACEMENT - ordering was opposite to real feel (tilt ordering matched); (3) chirality judged by wz-tracking GAP - measured turning symmetry while the robot's actual disease was postural left/right asymmetry, "两个不同的东西,且结论相反". Common pattern named: "我一直用「任务指标」当判据,而真机手感对应的是「姿态 指标」… 任务类指标不能替代它". The fix was already in the data: the per-joint left/right asymmetry table (printed identically by sim2sim and deploy) agreed with hardware in direction on every row - "判据可用、有预测力,我只是没把它写进 PASS 条件". Shipping decision followed the posture read: product reverted to 700 ("又一次「买到 精度、卖掉别的」"), and the C4 root moved to 700 as well, with the sharpest line of the episode: A800's left-drift "like sidewalking" is probably its frontal-plane asymmetry defect, not a capability - "在缺陷上建能力是危险的".

  Change

  Two posture quantities with demonstrated real-robot predictive power promoted into every PASS battery: tilt-max median and per-joint left/right asymmetry (both sim-computable, deploy-homologous); motor-temperature readout added to session close-out.

  Outcome

  Deployment flipped to the posture-better checkpoint; the hip_roll temperature table (43-48 degC vs 25-28) confirmed the earlier 90%-of-heat account; the run-line acceptance battery inherited the posture rows from birth ("任务类替代不了姿态类").

  Mechanism

  Task metrics measure goal attainment under the evaluator's episode definition; posture metrics measure the body state trajectory that operators, motors, and downstream skills actually experience. The two can rank candidates oppositely because task success tolerates postural pathology - so a battery without posture rows is blind to exactly what hardware feel reports first.

  Applies when

  • hardware feel disagrees with a green acceptance table
  • choosing between checkpoints that split task vs posture metrics
  • selecting the root for a skill that resembles an existing defect

  “共同模式:我一直用「任务指标」(存活 / 跟踪率 / 位移)当判据,而真机手感对应的是「姿态指标」(倾角、逐关节左右不对称)。→ 验收判据里必须有姿态类指标,任务类指标不能替代它。… A800 的「左飘像 side walk」很可能 … 是它更大的额平面不对称的表现 —— 在缺陷上建能力是危险的。”

• When hardware underperforms, audit deployment knobs before prescribing retrainingdeploy-knob-attribution-before-retraining

  Mechanism understoodomniattributionattributionreal-acceptanceprocess

  Before any "retrain it" decision, reproduce the symptom in sim under the exact deployment configuration; if the symptom follows the deployment knob rather than the checkpoint, fix the knob or randomize it in training - never top-up-train the skill.

  Symptom

  Real-robot feedback after the C4 deployment - "turning is weak" - with two retraining options on the table: top up turn training, or restart from the s1e root.

  Context

  The sim account showed the policy turned well (75-81% at pw1.0); the robot was deployed at power-scale 0.8. The 3-6 pp difference between C2 and C4 policies at the same power was noise; the 40-50 pp difference between power levels was the entire effect. Both proposed retraining paths would have burned budget on a non-existent training gap, and restarting from s1e would additionally have discarded the sidewalk skill that took four rungs and a coordinate-bug hunt to obtain.

  Change

  Decision: retrain nothing. (1) Try pw1.0 on hardware first - sim says net gain; (2) only if 1.0 is unacceptable (heat/feel), the correct training fix is power/torque randomization in the S2 plant line (one variable, fixes turn and backward together) - not skill top-up; (3) restart-from-root explicitly ranked worst.

  Outcome

  The "weakness" was fully explained by the deployment knob; the sim/real signatures matched the earlier power-derating law verbatim ("与 C2 时代 power 衰减主要伤非前进轴 逐字吻合").

  Mechanism

  The policy's competence is defined under its training plant; deployment knobs (power scale, teleop mapping, command bands) silently define a different plant. Attributing a deploy-plant effect to a training gap produces exactly the wrong fix - more training on the wrong variable.

  Applies when

  • real robot underperforms a skill that sim says is fine
  • proposals on the table include retraining or re-rooting
  • deployment uses any override the trainer never saw (power scale, remapped commands, different control rate)

  “正确的训练修法不是补训转向，而是训练时加 power/力矩随机化让策略在 0.8 下自己补偿 —— 单变量，属 S2 plant 线，一次同时修好转向与后退；从 s1e 重训是最差选项：丢掉四轮 + 一个指标 bug 才换来的侧走，而 C2 的转向本来就没问题。”

• The shipped checkpoint was chosen by scanning checkpoints on the full gate - neighbours 100 iterations apart failed 1 and 38 cells, late checkpoints degraded - never by taking the last one, and training stopped on signals, not on a schedulecheckpoint-choice-is-a-full-gate-scan

  Replicatedonelegsim-evalfork-selectiongate-battery

  Choose a release checkpoint by running the full acceptance battery over a band of checkpoints (including the transfer axis), stop training on measured signals rather than a fixed iteration count, and expect adjacent checkpoints to differ sharply.

  Symptom

  Gate results moved sharply and non-monotonically between checkpoints of the same run, and the last checkpoint was often not the best.

  Context

  One-leg V0r1: the 2,000 neighbourhood was best; from 2,500 on the nominal gates degraded (late overtraining); 2,000 itself had one real micro-hop (17.7 mm over 5 frames); 2,300 was all green and shipped. V0r2: failed cells per checkpoint 2,000:19, 2,100:38, 2,200:1, 2,300:3, 2,400:27, 2,500:12, 3,000:18 - 2,200 shipped. The recovery line learned the same from the other side: stopping v2_6 early at a scheduled point left a policy whose re-fall rate had spiked to 9-22% before consolidation healed it ("stop on signals, not on the schedule"), and a continuation's transfer decayed checkpoint by checkpoint while Isaac stayed perfect.

  Change

  The acceptance rule "scan checkpoints, do not look only at the last one" is written into the one-leg gates (called the S1 discipline); release candidates are chosen from the scan.

  Outcome

  Both one-leg releases were mid-run checkpoints (2,300 and 2,200) chosen by the full 40-cell battery.

  Mechanism

  PPO keeps changing the policy after the gates saturate; with no gradient toward the gate's conditions, later checkpoints wander, so gate quality is a noisy function of iteration.

  Applies when

  • picking which checkpoint of a run to export and stamp
  • a run is stopped at a fixed iteration budget
  • final-checkpoint results are worse than mid-run smoke tests

  “Isaac 侧 S1 纪律: 验收扫 checkpoint，不是只看最后一个。 … 扫描判决: 2000 邻域最优——2500+ 标称面退化（⑤③② 散挂, 晚期过训）, 2000 有一例真微跳（L s100 μ1.2, 17.7mm/5帧）, 2300 全绿。”

• Continuing a converged policy on a change that carried no new gradient drifted its transfer from 100/98% to 80/28% over 3,000 iterations while every Isaac gate stayed perfect - scan every checkpoint on the second simulator's friction axisconverged-continuation-is-poison

  Observed oncerecoverytraining-runfork-selectionsim2simcurriculum

  Before continuing a converged policy, check that the change creates a live gradient; if it does not, cap the budget at a few hundred iterations, and in every continuation scan each checkpoint on the second simulator's transfer axis (for example low friction) - trainer-side gates can stay perfect while transfer decays.

  Symptom

  V2.7-A (swap the flat_feet term for a compensated version, continue from v2_6c) finished with the line's best Isaac score (100%) and a MuJoCo transfer collapse: mu 1.0 98 -> 80%, mu 0.4 98 -> 28%; the stance it was meant to widen had not moved.

  Context

  The new term's calibration run showed a near-zero tax from the start: the policy already satisfied it, so the reward landscape offered nothing new. A checkpoint scan on MuJoCo mu {1.0, 0.4} located the damage: +100 iterations 100/98% (better than the baseline), then 86/54, 60/38, 80/28 - monotonic decay with training length, while entropy and action noise rose (7.77 -> 8.18, 0.588 -> 0.612): drift, not sharpening.

  Change

  Rule written in: with no new gradient, a continuation budget is short (at most a few hundred iterations) and the MuJoCo transfer axis enters every checkpoint scan. The next rung (V2.7b, a live stance-width gradient) was budgeted at 1,000 iterations with mu {1.0, 0.4} scans every 100 and a stop-on-signal rule.

  Outcome

  V2.7b kept transfer at the same depth (mu 1.0 98% / mu 0.4 92% at +1,000, where A had already rotted to 86/54) and at +3,000 (100/96%): a live gradient preserved transfer. V2.8 then broke that pattern (mu 0.4 2%): the gradient must also be compatible with the policy's existing form.

  Mechanism

  On a converged reward landscape PPO keeps updating without a signal to follow, and the random walk is pulled toward whatever the training plant rewards idiosyncratically - invisible in the trainer's own gates.

  Conflicts

  The drift mechanism is the spec's reading of one decay series plus one contrasting run; V2.8 is recorded as an exception to "live gradient keeps transfer".

  Applies when

  • fine-tuning a converged policy with a small reward change
  • a continuation run's trainer-side metrics improve while real or cross-sim results worsen
  • choosing which checkpoint of a continuation to ship

  “**checkpoint 扫定死因**(μ1.0/μ0.4):**29500(+100 iter)= 100/98%** (优于基线!)→ 30400 = 86/54 → 31400 = 60/38 → 32398 = 80/28 —— **迁移随续训长度单调衰减**。 … **教训入库:收敛均衡上的长续训是毒药 —— 无新梯度时 续训预算须短(≲数百 iter),且 MuJoCo 迁移轴必须进 checkpoint 扫描。**”

• 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。”

• Four consecutive fixes were each continued from the previous fix's degraded state until the user stopped the ladder - "change parameters, don't stack errors" - rolled back to the last good checkpoint and audited the target geometry firststop-stacking-roll-back-and-audit

  Observed oncerecoveryprocessprocessfork-selectionattribution

  When successive rungs each start from the previous rung's output and the target symptom does not move, stop, roll back to the last good checkpoint and re-derive the next change from an audit; keep the measurements, discard the stacked remedies.

  Symptom

  After the real-robot splits, the V2.7 ladder tried to widen the stance: a term swap (A), a new stance knife (b), more iterations (甲), a doubled weight (乙). Stance barely moved while hip yaw ratcheted 46.7 -> 49.9 -> 52.5 deg toward its 60 deg limit.

  Context

  Each rung started from the previous rung's output. The user ruled on 2026-08-11 that things had gone wrong from V2.7-A: go back to v2_6 and rethink which parameters to change instead of stacking errors.

  Change

  乙 was killed at start and not counted; the product baseline rolled back to v2_6c model_29399; every measurement and law learned on the ladder was kept ("the data is real; what stacked was the treatment"). Before any new training, a zero-training kinematic audit of the stance targets was run.

  Outcome

  The audit found the stand_pose target itself rewarding the narrow stance (pose-target-geometric-audit) and showed geometrically why the yawed stance could not be widened with flat feet - so 乙 was proven unnecessary without running it. The next in-lineage attempts still failed, which is what established that the stance is set by the get-up path.

  Mechanism

  A rung continued from a degraded state inherits its compensations, so each new fix answers the previous fix's side effects; the yaw ratchet was the visible trace of that stacking.

  Applies when

  • three or more corrective rungs in a row without progress on the target metric
  • a side-effect metric ratchets in one direction across rungs
  • a new rung is being planned from the latest (not the best) checkpoint

  “用户裁:"从 V2.7-A 开始就出问题了,应该回到 2.6 再思考如何改变参数而不是 错误叠加。"认账:A 的补丁 → b 的新刀 → 甲的加时 → 乙的加权,每级都从上级 的**退化状态**续(yaw 46.7→52.5° 的棘轮就是叠加痕迹)。 … 数据是真的,叠加的是处置。”

• A time gate that had cured one lineage's rushing made the from-scratch lineage trade away its stance width twice (0.364 -> 0.235 m, 0.355 -> 0.251 m) - its disease was absent there, so the fix was retired and the pre-gate checkpoint shippedtime-gate-vs-wide-stance-retire-the-fix

  Replicatedrecoveryreward-shapingreward-shapingcurriculumfork-selection

  Carry a fix into a new lineage only if its disease is present there; a mechanism that cured one lineage can be net negative in another, and when doubling a term's weight recovers almost nothing, treat the two objectives as structurally in conflict and remove the one whose purpose is gone.

  Symptom

  V3.1's phase 2 (the 3 s zero gate on standing income, continued from P1b) kept 100% success on every friction level and slowed the get-up, but the lateral stance drifted 0.364 -> 0.235 m and hip yaw crept to 57 deg against its 60 deg limit. With the width band's weight doubled (P2c, after P1c) it drifted again, 0.355 -> 0.251 m, below the pre-registered 0.30 m failure line.

  Context

  The zero gate had been introduced in V2.5/V2.5b to slow the old lineage's get-up. In V3.1 the rushing was already absent: P1c got up in 0.90-1.06 s with a worst torque ratio of 73.3%, better than the stamped v2_6c, because the full beta curriculum, second-difference smoothing and pull curriculum had cured the violence inside training.

  Change

  Recorded as a candidate law with two data points - the zero gate and a wide stance are mutually exclusive here - and the zero gate was removed from the V3.1 recipe. P1c (the pre-gate checkpoint) went through the full stamp-level acceptance instead.

  Outcome

  P1c passed everything: all six criteria, lateral stance 0.355 m, foot tilt P75 2.0 deg, mu {1.0, 0.8, 0.6, 0.4} x 10 seeds all 100%. recovery_v3_1p1c.onnx was stamped and pushed to the robot channel.

  Mechanism

  The zero gate moves the income toward "stay stable until the end", and under low-friction DR a wide stance has a slip tail, so survival outbids the width band; doubling the band's price bought back only 0.016 m - an auction that does not converge signals structural conflict, not an under-priced term.

  Applies when

  • porting reward mechanisms from an old lineage into a fresh recipe
  • a width, margin or posture metric erodes during a late training phase
  • a weight increase produces a negligible change in its target

  “**定律候选(二实证):归零门 × 宽站互斥**。 … 加价翻倍只挽回 0.016,竞拍不收敛)。 … **归零门是 v2_5 血统的历史包袱,对 V3.1 配方是净负资产,P2 阶段除名 —— P1c 即终点形态**。”

• Every rung gets written stop criteria - hit any one, stop; tightened when priors say results should come fastpreregistered-stop-criteria-per-rung

  Replicatedomnitraining-runprocessgate-battery

  Freeze per-rung stop criteria (old-skill floors, new-skill deadline, oscillation signature, known pathology signatures) before training, stop on first hit - and shorten the deadline in proportion to how fast your mechanism says results should appear.

  Symptom

  Continued training past the point of degradation had previously destroyed capabilities (C1 trained away the root's backward skill); without hard stop rules, sunk-cost reasoning keeps runs alive too long.

  Context

  Standard rung protocol - eval_c_matrix every 100 iters at 5 seeds with a fixed criteria list, frozen before training ("开训前写死，事后不许改"): (1) stand or vx+0.15 at <=3/5 for two consecutive points; (2) vx+0.15 tracking <70% for two consecutive points (vs recorded root baseline); (3) oscillation signature - survival repeatedly crossing zero between adjacent checkpoints, stop on first occurrence, do not wait for confirmation; (4) new-skill-no-progress deadline (e.g. wz tracking <30% or still same-signed after iter 400); (5) a known lineage pathology signature (s1c arm-B: scatter -> half-recover -> collapse).

  Change

  Stop budgets scale with prior knowledge: when C4-redo4's feed-forward was already proven open-loop, the no-progress deadline was tightened from +500 to +100 iters ("前馈是开环就给 71~96% 的 … 若 +100 还没有 … 不值得再烧").

  Outcome

  Multiple rungs were stopped exactly on criterion (C4-redo criterion 4 at iter 1200; redo2/redo3 at absolute 900), converting each into a clean hypothesis test instead of a drifting run; no rung burned its full cap on a dead hypothesis.

  Mechanism

  Degradation of retained skills is a lineage-level injury that later training often cannot undo, so detection latency is capital loss; and a stop rule written before the run cannot be bent by hope. Tightening deadlines when priors predict immediate results converts "no result yet" into evidence against the mechanism.

  Applies when

  • starting any resumed/curriculum training rung
  • deciding whether to keep training a run that shows early regression
  • a mechanism-backed change should produce results immediately

  “每 100 iter eval_c_matrix.py --seeds 5，命中任一即停 … 存活在相邻 checkpoint 间反复跨越 0（出现即停）… 收紧版：绝对 iter 900（= +200）时 vy±0.10 跟踪仍 <30% 即停”

• Fix the task first, harden the plant second - DR budget spent on a dying task is wastedtask-shaping-before-plant-hardening

  Mechanism understoodomniprocessdomain-randomizationprocesscurriculum

  Freeze the task/command distribution before spending DR budget on plant robustness; if the task will still change, schedule plant hardening as a final pass and book the interim robustness gap explicitly.

  Symptom

  Tempting default ordering was to keep the plant-hardened (S2) lineage and teach it new commands; but the S2 plant adaptation had been earned on the straight-walk task, and the new omni tasks (sidewalk, in-place turn) use completely different contact patterns.

  Context

  The team had direct evidence that DR robustness is a budget that gets reallocated when the data distribution changes ("push/μ 两轮已实证 DR 预算有限且会被重分配") - robustness trained under one task/command distribution does not persist when training continues under another.

  Change

  Ladder order set to: first C (task shaping - add command modes until the task family is final), then a second S2 pass (plant hardening) on the C product. The plant-robustness gap this creates mid-ladder is accepted and booked explicitly ("此处不欠账" - the debt is assigned to the second S2 pass, not denied).

  Outcome

  The first S2 pass was not wasted: its laws (kd bandwidth <-> low mu, push need not be trained, ground mu need not be trained, bistability) let the second pass drop from five rungs to three. The C ladder itself ran on the softer plant band without incident.

  Mechanism

  DR robustness is carried by the policy's visited-state distribution; changing the task changes that distribution, so robustness bought under the old task partially dissolves. Hardening before the task is final means paying for robustness on states that will no longer be visited - "给一个即将不存在的任务花预算" (spending budget on a soon-to-not-exist task).

  Applies when

  • deciding ordering between skill/command expansion and DR hardening
  • a hardened lineage is proposed as the root for a task change
  • robustness regressions appear after adding new command modes

  “S2 的 plant 适应是为直行步态调的，C4 侧走/C3 原地转是完全不同的接触模式，先硬化再改任务 = 给一个即将不存在的任务花预算（push/μ 两轮已实证 DR 预算有限且会被重分配）。故顺序改为 先 C（任务定型）→ 再 S2（plant 硬化）。”

• Score left and right separately - averages hide chirality breaking that mirror augmentation does not preventchirality-scored-separately

  Replicatedomnigate-batterygate-batteryreal-acceptance

  Report every mirrored skill as two numbers with an explicit gap budget; never accept an average, and never assume augmentation guarantees symmetry - measure it per lineage and treat breakage as hard to reverse.

  Symptom

  Policies developed quantified left/right asymmetry (e.g. C2-700 turned right at 82% but left at 67% - a 15 pp gap; push tolerance 40/40 symmetric on the root vs 17/40 on a deep-trained descendant), and averaged metrics would have reported healthy midpoints.

  Context

  The repo had policy-level symmetry-breaking evidence strong enough to make separate scoring a battery rule: "左右必须分开打分 … 平均 vy 跟踪会把它掩盖". Notably, chirality broke and never recovered even though mirror augmentation (command-level mirror_prob 0.5) was on the whole time - augmentation reduced but did not prevent asymmetry, and once broken it stayed broken through subsequent rungs. PASS conditions therefore carried explicit symmetry budgets (left/right tracking gap <=10 pp), and sim's predicted asymmetry (700: right faster than left) was flagged for direct real-robot timing confirmation.

  Change

  Battery rule: every directional skill reports left and right (CW/CCW) as separate rows with a max-gap budget; mirror augmentation treated as mitigation, not proof of symmetry.

  Outcome

  The 700-vs-A800 asymmetry gap (15 pp vs 7 pp) became a first-class selection criterion; C4 product shipped with a measured 5 pp gap.

  Mechanism

  Averaging over mirrored conditions cancels antisymmetric error exactly where it matters; and symmetry lost during training is a lineage injury (like plasticity loss) that later rungs do not spontaneously heal, so it must be gated, not assumed.

  Applies when

  • evaluating turn/sidewalk/push-recovery or any mirrored skill
  • relying on mirror/symmetry augmentation
  • selecting between checkpoints with similar average scores

  “左右必须分开打分（left/right lateral、CW/CCW turn 各自一行）—— 本仓已有 policy-level symmetry breaking 的量化证据，平均 vy 跟踪会把它掩盖。”

• mj_objectVelocity returns inertial-principal-axis frame - one API assumption poisoned eval and observations for a whole linebody-frame-velocity-api-audit

  Mechanism understoodomnisim2sim-gatemeasurementsim2simobservation-honestyattribution

  Verify every frame-sensitive API against a hand-computed truth (rotate raw qvel yourself, or command a known world velocity and check where it lands) before trusting any evaluation or observation built on it - especially when a model's inertial frame is rotated from its body frame.

  Symptom

  Sidewalk vy read ~0 under every condition; separately, whole-policy performance was mysteriously mediocre in sim2sim while training-side numbers looked fine. Four training rungs were declared FAIL partly on these readings.

  Context

  base_link's URDF inertial frame is rotated 90 deg about x relative to the body frame (iquat = [0.7071, 0.7071, 0, 0]). mj_objectVelocity(flg_local=1) rotates into ximat - the inertial principal-axis frame - not the body frame, and returns center-of-mass point velocity, not body-origin velocity. Consequences measured: the "vy" column was actually vertical velocity vz (walking at cmd 0.25: old reading +0.0093 vs true -0.0424); the angular velocity fed to the policy in sim2sim was [wx, wz, -wy] - a different quantity than Isaac and the real IMU provide. RMS check over 8 s of walking: y/z axes swapped between v6[:3] and the qvel truth.

  Change

  Fixed sim2sim and both probes to compute ang_b = qvel[3:6] and lin_b = xmat.T @ qvel[0:3] (identical quantity to Isaac's root_ang_vel_b / root_lin_vel_b), with a standalone reproduction script (frame_bug_repro_0809.py).

  Outcome

  Re-scoring the "failed" C4 lineage under correct coordinates reversed the verdicts: c4r4 checkpoints showed vy 80-126% tracking (old reading: +/-2%) and vx+0.30 at 91-95% where the old metric said 0/5 - the bad frame both mis-measured vy and, via corrupted policy observations, systematically depressed all measured performance. Final product passed 260/260 cells.

  Mechanism

  A simulator API's frame convention is part of the observation contract; when the model's inertial frame is rotated relative to the body frame, frame-agnostic use of a "local" velocity silently permutes axes. Feeding a policy an axis-permuted angular velocity is an observation corruption that degrades behavior everywhere, not just on the axis being studied.

  Applies when

  • building or auditing a cross-simulator evaluation harness
  • one measured axis reads near-zero under all conditions
  • sim2sim scores are inexplicably worse than training-side metrics
  • URDF/MJCF inertial frames are rotated relative to body frames

  “base_link 的 iquat = [0.7071, 0.7071, 0, 0] … mj_objectVelocity 用的是这个 … 喂给策略的 base_ang_vel 是 [wx, wz, −wy] —— MuJoCo 侧观测与 Isaac / 真机 IMU 不是同一个量；vy_mean 报的是竖直速度 vz —— 前进 cmd 0.25 时旧读数 +0.0093，真值 −0.0424。”

• Close a question with an audit, then freeze the wording - later symptoms may not reopen it without new hard evidencefrozen-verdicts-semantic-boundaries

  Mechanism understoodomniprocessprocessattributionplant-calibration

  When an audit closes a hardware-vs-policy question, record the closing evidence, freeze a citable wording for future recurrences, and set the reopening bar explicitly; separate robustness perturbations from plant-truth questions so a DR rung's failure can never silently reopen a closed measurement.

  Symptom

  Recurring directional bias on the robot kept re-suggesting "maybe the hardware/COM/mechanics are asymmetric", threatening to re-litigate questions that audits had already closed - burning attention each time a descendant policy leaned or drifted.

  Context

  Two boundary decisions were written as permanent: (1) semantic separation - "S2④ COM ±20mm = 纯鲁棒性扰动,不再承担「解释真机后仰」任务" - if the COM-DR rung degrades, the ONLY allowed conclusion is "policy insufficiently robust to COM uncertainty"; reopening "is the CAD COM wrong" is forbidden because the mass audit was completed and closed (@63f9212). (2) a frozen wording for chirality, to be quoted verbatim whenever left/right bias appears in later rungs: observed directional bias = policy-level spontaneous symmetry breaking; plant asymmetry = no supporting evidence after the mass + model symmetry audit; mitigation candidate pi_sym queued, not blocking. The evidential basis was quantitative: the root policy was perfectly symmetric under +/-6 N*s pushes (40/40) while descendants broke (17/40, 13/40) - "手性是 S2 训练中获得的, 根没有; 机械侧已双 PASS 关案, 不重开".

  Change

  Closed questions carry (a) the audit commit that closed them, (b) a frozen citable wording for recurrences, and (c) an explicit evidence bar for reopening ("无新硬证据不得重开").

  Outcome

  Later chirality observations (C2's 15 pp turn gap, hip_roll drift bias) were handled as policy-lineage properties with policy-side mitigations, without a single hardware re-audit cycle.

  Mechanism

  Symptom classes recur under different guises; without a frozen verdict each recurrence re-runs the same expensive investigation and risks a different (worse-informed) conclusion. Freezing verdict plus wording converts recurring symptoms into citations, while the evidence bar keeps the closure honest rather than dogmatic - the root/descendant symmetry comparison is what makes "it's the training, not the machine" checkable at any time.

  Applies when

  • a recurring symptom keeps suggesting an already-audited hardware cause
  • writing conclusions for a completed calibration/audit
  • a DR rung's degradation invites re-measuring the plant

  “若 S2④ 退化,结论只能是「当前 policy 对 COM 不确定性不够鲁棒」,不得重开「CAD COM 是不是错了」… 手性冻结表述 … Plant asymmetry: no supporting evidence after mass + model symmetry audit … 无新硬证据不得重开机械不对称”

• A sim veto needs real confirmation too - the worst sim cell was scheduled as the most informative hardware runsim-veto-needs-real-confirmation

  Mechanism understoodomnireal-acceptancereal-acceptancesim2simattributionprocess

  Never let sim alone both condemn a purpose-built configuration and escape audit: spend one cheap, safeguarded hardware run on the condemned cell, pre-registering what agreement and disagreement would each imply about the proxy.

  Symptom

  The fric-2400@kd1.0 combination was sim's worst cell across the board (survival 17/20 - the only miss, mu0.4 1/20, push 103/160, zero-cmd 2/20), yet it was the only product specifically trained for the kd1.0 deployment gain - discarding it on sim evidence alone would leave the sim's own validity untested exactly where it mattered.

  Context

  The team had been burned in the other direction before ("Isaac 指标三次 零预警" - training-side metrics gave zero warning three times), so the symmetric rule was written: sim's rejection also needs hardware confirmation ("sim 判被支配 ≠ 真机被支配 … sim 的否决也要真机确认"). The run was pre-registered with a dual reading: real matches sim -> the S2f ladder closes and the fork root is settled; real clearly better than sim -> the MuJoCo proxy has a systematic bias in the kd1.0/low-margin region, "那比选型本身重要得多" - and every S2f sim acceptance would need re-scoring.

  Change

  The condemned configuration was kept on the hardware roster (last, spotted, minimal exposure) explicitly as a proxy-validation probe, not as a deployment candidate.

  Outcome

  Session design captured either result as progress: selection confirmed, or a proxy bias discovered that would re-price the whole ladder's verdicts.

  Mechanism

  Every sim verdict is a joint statement about the policy AND the proxy; cells where a policy was purpose-trained for the exact condition sim condemns are where proxy error is most likely and most costly. Testing the veto converts a selection decision into a calibration measurement of the evaluator itself.

  Applies when

  • sim rejects the configuration that targets the actual deployment condition
  • the eval proxy's calibration has never been checked in that regime
  • deciding which hardware runs are worth their risk

  “但它也是唯一为 kd1.0 部署档专门训的产物 —— sim 判被支配 ≠ 真机被支配, 「Isaac 指标三次零预警」的教训反过来同样成立: sim 的否决也要真机确认。… 若真机明显好于 sim → MuJoCo 代理在 kd1.0/低裕度区有系统性偏差, 那比选型本身重要得多。”

• A constant-value plant rung passed every binary gate with record scores - and shipped 60% thinner posture margins that hardware exposedconstant-value-dr-overfits-margin

  Mechanism understoodomnidr-tuningdomain-randomizationreal-acceptancegate-battery

  Randomize deployment-critical axes over a narrow band spanning the measured real support - never a single value, never a fictitious tail - and report graded margin quantities (tilt margin) next to binary gates, because saturated gates rank thin-margin and thick-margin policies identically.

  Symptom

  s2_lag1 (trained at constant 1-frame latency) posted the strongest sim gate sheet in history (20/20 everywhere) yet was unstable on hardware, while s1e (trained across the full 0-3 frame band) was the every-run-stable SOTA at the same power.

  Context

  The sim autopsy (new --delay-jitter harness modeling the BusWorker's time-varying phase drift): 18 runs across constant and time-varying delays ALL survived - time variation alone does not kill - but the tilt-margin ordering reproduced hardware exactly: s1e 7.7-9.1 deg (thickest) < s2_lag1 10.7-15.0 < s1c 16.2-18.2. Attribution: constant-value training permits precise specialization to that one value; s1e's band diversity forced cross-value robustness - "恒定 1 帧训练 vs s1e 的 0~3 帧全带——分布多样性逼出跨值鲁棒,恒定值允许精确 特化" - so the constant-rung policy's margins were ~60% thinner, fine in sim's clean world, pushed over the line by real-world disturbances. Tool lesson booked: "存活门二值饱和后掩盖裕度差" - binary survival gates saturate and hide margin differences; graded margin columns (tilt-max) belong in the report. The synthesis with the opposite failure (wide tails cause drag-glide): the proposed resolution was a NARROW uniform band (0.02, 0.04) covering exactly the real 1-2 ticks - diversity inside the measured support, no tail, no single point. s1e's root selection later leaned on the same property: its full-band latency training "预装" the delay rungs and delivered "全工况稳定裕度" that survived power derating.

  Change

  DR-on-an-axis design refined to a three-way distinction: no wide fictitious tails (drag), no single constant values (thin margins), but a narrow band spanning the measured real support; acceptance reports gained graded margin columns alongside binary gates.

  Outcome

  The tilt-margin column entered the standard report; the s1e root (band-trained) carried the C ladder while the constant-value branch was archived with its three contributions credited.

  Mechanism

  Robustness margins are shaped by the diversity of the training distribution, not just its support: a point-mass distribution lets the optimizer trade margin for on-point performance, while a band forces solutions that keep margin across the band - and binary survival metrics cannot see the difference until the margin is spent on hardware.

  Conflicts

  The narrow-band (0.02,0.04) resolution was a pending recommendation ("裁决建议(待用户)") at the time of writing; the lineage instead moved root to s1e whose full-band training predated the staged ladder - the deterministic-staging card and this card record the two failure modes the final design must avoid simultaneously.

  Applies when

  • a rung trained at a fixed plant value aces sim but wobbles on hardware
  • binary acceptance gates are all saturated across candidates
  • choosing between constant, banded, and wide DR on one axis

  “18 跑全活,时变性单独不足以击杀;但 tilt_max 裕度排序完整复现真机:s1e 7.7~9.1°(最厚)< s2_lag1 10.7~15.0 … 恒定 1 帧训练 vs s1e 的 0~3 帧全带——分布多样性逼出跨值鲁棒,恒定值允许精确特化 … 存活门二值饱和后掩盖裕度差(s2_lag1 sim 门 20/20 史上最强却真机不稳)”

• A stand gate judged by survival passes a robot that wanders a meter - judge posture insteadstand-gate-posture-not-survival

  Mechanism understoodomnireal-acceptancegate-batteryreal-acceptance

  For every gate, ask what behavior the metric is a proxy for and validate its ordering against real observations; replace metrics whose ordering disagrees with reality, and demote them explicitly rather than silently.

  Symptom

  Real robot "standing" drifted 0.5-1.4 m across the floor while the sim stand gate scored a clean 20/20 - because the gate's metric was episode survival, which wandering does not violate.

  Context

  C2's stand condition was originally written as "survival regression <=2/20". A real-robot counter-example on 2026-08-08 forced the re-judgment: wandering robots survive. Cross-checking candidate sim metrics against real-robot feel showed max-tilt median ordering agreed with hands-on ranking, while displacement ordering was actually OPPOSITE to real impressions - so displacement was demoted to a reference quantity, not a gate.

  Change

  Stand PASS criterion rewritten from survival to posture: "stand tilt max median <= root baseline +1.5 deg"; displacement kept only as reference. Applied to all subsequent rungs (C4 and redo levels inherit it).

  Outcome

  Later rungs gated stand on tilt (e.g. C4 product: 7.7 deg vs parent 7.1 deg, +0.6 deg PASS); the wandering failure mode became visible to the battery instead of hidden by survival.

  Mechanism

  A gate metric is a proxy for an intended behavior; survival is a proxy for "did not fall", not "stood still". Metric choice must be validated against ground truth (real-robot feel/measurement), and a proxy whose ordering disagrees with reality on real data is worse than no metric - it steers selection backwards.

  Applies when

  • writing PASS conditions for stand/idle/hold behaviors
  • a gate passes policies that visibly misbehave on hardware
  • choosing between candidate metrics for an acceptance battery

  “stand 必须用位姿判，不能用存活判（2026-08-08 真机反证改判）：站着乱走 0.5~1.4 m 时存活照样 20/20 —— C2 的 stand 条件原写「存活退化 ≤2/20」，选错了指标。改为: stand 倾角 max 中位 ≤ 根基线 +1.5°（倾角与真机手感排序一致；位移排序与真机相反，降为参考量）”

• Symmetrizing the config made the gait MORE asymmetric - the asymmetry lived in the policy weightsasymmetry-in-weights-not-config

  Mechanism understoodwalkattributionattributionreward-shapingcurriculum

  Localize a persistent asymmetry by intervening at the config layer first: if the symptom survives (or worsens), it is in the weights - fix it with symmetry-constrained training, not with trims or offsets.

  Symptom

  walk_v1 on hardware: straight-line command curved 149 deg in 15 s (9.9 deg/s) with 3.06 m lateral runout; turn gain +31% one way vs +129% the other (75% difference); knee asymmetry 4.4 deg in sim, 9.6 deg on the robot.

  Context

  The obvious suspect was the asymmetric default pose in the config. The decisive test: symmetrize standing_pose and run the SAME policy in sim - the asymmetry got LARGER (hip_pitch 6.8 -> 9.3 deg). Root cause therefore not in the config but baked into the policy weights: PPO without a symmetry constraint commonly converges one-sided, because splitting the work 50/50 and loading one side yield the same return, and the gradient falls randomly into one of the equivalent optima.

  Change

  Fix redirected from config trimming to retraining with mirror data augmentation (walk_v2 spec) - a weights-level fix for a weights-level disease.

  Outcome

  With augmentation (and the symmetric-default precondition), stand_v1 reached 0.0 deg asymmetry on all six joint pairs (from 4.4-7.7 deg), height fluctuation 7 mm -> 1 mm, mean |action| down 33%.

  Mechanism

  Reward-equivalent solution families (who carries the load) leave the symmetric solution unpreferred; SGD picks an arbitrary member and entrenches it. Config changes move the coordinate frame around the entrenched asymmetric function - they cannot move the function. The counterfactual test (change config, watch symptom) localizes the layer the disease lives in.

  Applies when

  • a robot veers or loads one side despite a symmetric-looking config
  • deciding between config trims and retraining for an asymmetry
  • mirrored-turn gains differ by tens of percent

  “根因不在配置里:把 standing_pose 对称化后在 sim 里跑同一策略,不对称反而变大(hip_pitch 6.8°→9.3°)—— 说明不对称烙在策略权重里。这是无对称约束的 PPO 的常见收敛结果(左右各担一半与一边多担的回报相同,梯度会随机落进其中一个)。”

• 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。”

• Training at scale 0.4 is NOT the twin of deploying 0.5 at power 0.8 - the algebra matches, the learned policy does notdeploy-scaling-not-training-equivalent

  Mechanism understoodomniattributionattributioncontract-freezesim2simcurriculum

  Never assume deploy-side scalings can be folded into training-time constants ("burning the crutch into training"): the learned optimum depends on the training-time authority, so treat such conversions as full experiments with pre-registered expectations and a sim2sim gate before any hardware.

  Symptom

  s1g (S1.6) trained from zero at action_scale 0.4 - meant as the "training twin" of the hardware-proven s1c-at-power-0.8 (0.8 x 0.5 = 0.4) - was all green in Isaac (zero falls, reward 117) yet scored 0/3 across all eight checkpoints and 0/20 at 20 seeds in the MuJoCo gate, falling forward at median 1.57 s with a 2.9x speed overshoot.

  Context

  The pre-registered expectation (survival gate should pass, since the conviction matrix showed s1c@0.8+delay2 all-survive) was cleanly falsified, and the harness was acquitted by controls: --delay 0 fell identically (not a delay fragility), check_contract all green, and s1c through the same harness survived 2/3. The verdict: "「s1c@0.8 = 0.4 训练孪生」的代数等价不成立" - a policy deployed with a derated output still LIVES in the 0.5 internal model it trained under (its value function, its expectations of its own authority), while a policy that starts training with reduced authority learns a different, clip-hugging gait with zero margin for plant differences ("部署端打折的策略活在 0.5 的内模里,训练起点收权限学出的是贴 clip 的 另一套步态,对 plant 差异零余量"). Result: the policy was withdrawn before hardware ("撤回——不上真机"), the lineage root moved back to the 0.5-contract s1c-5500, and this became the C ladder's cited fact-check ("s1g 是 0/20 证伪出局的那一代").

  Change

  The amplitude-surgery route abandoned; contract kept at scale 0.5; the deploy-side 0.8 crutch later retired on its own merits when the delay-complete s1e generation ran at full power.

  Outcome

  One training run bought a clean falsification of a plausible algebraic identity; no hardware time was spent on it because the sim2sim gate caught it.

  Mechanism

  Output scaling commutes with the network arithmetic but not with learning: the training-time scale shapes which gait solutions are reachable and how much clip headroom the optimum keeps. A derated mature policy retains the wide-authority solution executed softly; a from-zero narrow-authority policy finds a different optimum that saturates its smaller envelope - the two are not the same controller in different units.

  Applies when

  • proposing to move a deployment derating into a training constant
  • a scaled-down contract policy hugs the action clip
  • Isaac-green / cross-sim-zero results on a re-scaled lineage

  “预注册 a) 证伪——Isaac 全绿(零摔/reward 117)但 MuJoCo --delay 2 八档 checkpoint 扫描全数 0/3、iter6500 20-seed 0/20 … 「s1c@0.8 = 0.4 训练孪生」的代数等价不成立: 部署端打折的策略活在 0.5 的内模里,训练起点收权限学出的是贴 clip 的另一套步态,对 plant 差异零余量。”

• 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)”

• Brief the operator on the lineage's measured zero-command and untrained-axis behavior before handing over the joystickknow-zero-command-behavior

  Replicatedomnireal-deployreal-acceptanceprocess

  Before any teleop/demo, measure and write down the policy's zero-command behavior and per-axis competence, label untrained axes explicitly as not-bugs, and set the floor/procedure to accommodate the known drift.

  Symptom

  A teleop session was about to start on a policy that does not stand still at zero command and has never been trained on lateral commands - behaviors an unbriefed operator would report as bugs or emergencies.

  Context

  Three measured facts were written into the teleop instructions ("都有 实测依据, 不是猜"): (1) A/D (lateral) keys will get essentially no response - probe-measured sidewalk tracking ~3%, an untrained axis: "这正是 C4 要解决的事, 不是 bug"; (2) no keypress = cmd 0, and this lineage does not stand still at zero command - a three-generation lineage property: paces in place, drifts right ~5 cm/s, net rotation -30 deg/20 s; sim survival is 20/20 (it will not fall) but it walks away slowly, so leave floor margin especially on the right; (3) S (backward) WILL respond - probe-measured 20/20 survival, 67% tracking untrained, which is also why this root was chosen for the C ladder. Plus a keybinding dry-run while suspended before touching down.

  Change

  Operator briefing became part of the deployment artifact: expected response per key, expected idle behavior with magnitudes and directions, and the distinction between untrained (expected, not a bug) and abnormal.

  Outcome

  The session proceeded with correct interpretations available in advance; the known zero-command wander was handled by floor margin and start-with-command procedure rather than misdiagnosed on the spot.

  Mechanism

  A learned policy's off-nominal behaviors (idle drift, untrained axes) are lineage properties, stable and measurable in sim beforehand; operator surprise converts known properties into false incident reports and unsafe reactions. A briefing transfers the measured behavior model to the person holding the controller.

  Applies when

  • handing a learned policy to an operator or demo audience
  • the policy idles in a non-stationary way at zero command
  • some command axes are untrained in the current lineage

  “A/D 基本不会有反应 —— s1e 从未训过非零 vy, 选根探针实测侧走跟踪率 ~3% … 这正是 C4 要解决的事, 不是 bug。… 不按键 = cmd 0, 而 s1e 在零指令下不站定 —— 血统属性, 三代实录: 原地踏步 + 右漂 ~5 cm/s + 净旋 −30°/20s。”

• Narrowing the speed range to stop high-speed falls entrenched crouch-shuffling - judge gait quality at the speed that demands a gaitlow-speed-commands-reward-dragging

  Mechanism understoodwalkcurriculumcurriculumreward-shapinggate-battery

  Set command ranges to include speeds that physically demand the target behavior, and evaluate behavior-quality gates at those speeds; when a restriction is added to suppress a failure, check what new optimum it creates at the remaining commands.

  Symptom

  After the command range was narrowed to (0.15, 0.35) m/s (to treat walk_v1's 134% overspeed and 8.3 s fall at 0.5), the policy settled into crouched foot-dragging; tracking rose monotonically with speed (63% at cmd 0.2, 76% at 0.3, 87% at 0.45), showing low speeds were where the degenerate gait was optimal.

  Context

  The narrowing advice was the author's own and is retracted in the file: it treated the symptom (falls at speed) while reinforcing the root cause (at 0.15-0.35 m/s, shuffling in a crouch is globally optimal - the Froude number is so low that even humans would not lift their feet). A zero-cost experiment confirmed the flip side: at cmd 0.5 the same policy met BOTH tracking (81%) and clearance (23.0/23.2 mm) standards.

  Change

  Speed range widened back toward (0.15, 0.5) - upper bound deliberately slightly above the mechanically feasible ~0.44 m/s so the policy finds the boundary itself; acceptance re-pointed: gait-quality criteria (tracking, clearance) judged at 0.45-0.5 m/s, low speed kept only as a survival check.

  Outcome

  v4 -> v6 progression under the widened range delivered 87% tracking with 34 mm clearance; the "low command = drag" account was confirmed by the monotone tracking-vs-speed curve.

  Mechanism

  Command distribution is part of the reward: physics prices gaits per speed, and at very low speed the energetic optimum is no swing phase at all. Restricting training to that regime makes the degenerate gait the correct answer to the posed problem - and grading a gait at a speed that does not require stepping measures nothing.

  Applies when

  • a gait degenerates after a command-range restriction
  • quality metrics improve monotonically toward the range boundary
  • writing acceptance criteria for gait quality vs survival

  “现在看那个建议可能起了反作用:0.15~0.35 m/s 下蹲着蹭就是全局最优,抬腿反而亏。收窄治的是"高速摔倒"的症状,却强化了拖地的病根。… 验收标准里的 cmd 0.2 本身就是拖地速度(Froude 数极低,人在那个速度下也不抬脚)。accept_v2 应把速度跟踪与 clearance 的判定点改到 0.45~0.5 m/s”

• Verify changes in the run's resolved config (and checkpoint md5), never in the source you editedresolved-config-is-source-of-truth

  Replicatedomniprocessattributionprocesscontract-freeze

  Attribution and single-variable claims must be made on the resolved per-run config (and checkpoint hashes), not on source diffs; verify every intended variable landed before burning compute, and verify every rollback byte-level against the historical resolved config.

  Symptom

  An intended arm-B config change never reached the training run - the run was grid-identical (117/117 cells) to its C2 predecessor - and the burn was only understood afterwards.

  Context

  The repo's discipline hardened around the logged resolved config (logs/<run>/params/env.yaml) as the only source of truth: (1) the C2 root-cause analysis was performed against the checkpoint's logged env.yaml, not the code ("以真相源 23-19-25/params/env.yaml 核实"); (2) C4 added a pre-flight: grep the landed env.yaml for the new keys, and compare the first checkpoints of the two arms - identical md5 means the variable did not land, stop immediately; (3) the C4 full rollback was accepted only after starting a 1-iter run and byte-comparing its resolved env.yaml against the historical 700-era file (identical except 4 dormant schema fields, each verified to be at its no-op default).

  Change

  Standing pre-flight and post-change verification: dump/diff the resolved config that the run actually consumed; use checkpoint hash equality as a cheap "variable landed" detector between arms.

  Outcome

  Caught the not-landed variable class of failure; made the rollback provably equivalent to the historical training state rather than believed-equivalent.

  Mechanism

  Between edited source and the running experiment sit layered overrides, env-var switches, and registration logic; only the resolved, serialized config reflects their composition. Diffing at that level tests the actual experiment; diffing source tests intent.

  Applies when

  • launching an A/B pair or any single-variable rung
  • rolling back to a historical training state
  • a run behaves as if a change was never applied

  “开训前先验落盘 cfg（上一轮臂B 的改动没进 run，与 C2 逐格 117/117 相同）：grep -E "base_com|joint_friction|push_robot|track_lin_vel_y_exp" logs/<run>/params/env.yaml 另：两臂第一个 checkpoint 的 md5 若相同 = 变量没进去，立刻停。”

• A reward on a quantity the actor cannot observe teaches "produce less of it", never "correct it" - closed-loop correction needs an outer loopreward-observability-limit

  Mechanism understoodomniobservation-designobservation-honestyreward-shapingcontract-freeze

  Before adding a reward, check the actor can observe (or infer) the quantity: unobservable-error rewards buy only average suppression - route correction tasks to an outer loop whose commands stay in distribution, and do not break a frozen contract to add an observation a deploy-side loop can supply.

  Symptom

  Heading kept drifting despite world-frame yaw rewards, and a reviewer proposed heading-error rewards - raising the question of what yaw shaping can even teach this actor.

  Context

  The adopted architectural verdict: the actor's 45-dim base observation cannot see accumulated heading at all - projected_gravity is invariant to rotation about the gravity axis, and omega_z is a rate, not an angle. World-frame yaw-rate rewards are therefore privileged shaping that can only teach "少产生旋转" (generate less rotation), never "偏了以后拉回原线" (pull back to the line after drifting) - the policy cannot represent the error it would need to correct. The S1 gate (<=5 deg / 10 s) demands exactly the former, so the stack is right for its gate; active heading correction is assigned to the deployment outer loop (--heading P-loop converting heading error into in-distribution wz commands) plus small-wz training - and the 215-dim contract is explicitly NOT extended with a heading observation ("契约不加 heading 观测,冻结不动"). The reviewer's companion bias hypothesis was adjudicated with data: drift is bimodal - a basin mechanism decides whether you leave (seeds vary +/-16-46 deg vs -385 to -391 deg), and once out, rotation direction is constant (weight chirality; candidate root: the phase clock always swings left first).

  Change

  Yaw shaping kept as rate-tracking (three-layer stack); heading correction owned by the deploy outer loop; contract frozen; the "which behaviors need an outer loop" question settled by observability analysis rather than reward tuning.

  Outcome

  Stopped a contract change and a futile reward direction; drift work split correctly into rate-suppression (trainable) and error correction (outer loop), consistent with the earlier measured 10x drift reduction from the deploy-side loop.

  Mechanism

  A policy can only condition on its observation sigma-algebra; rewards on functions outside it shift the marginal action distribution (open-loop average effects) but cannot create feedback on the unobserved variable. Whether to add an observation, an outer loop, or accept average-shaping is decided by the task's gate: suppression gates need shaping, correction gates need the variable in some loop's view.

  Applies when

  • adding rewards on accumulated/世界-frame quantities (heading, position)
  • deciding between a new observation, an outer loop, and shaping
  • a drift symptom persists across reward-weight changes

  “actor 的 45 维基座观测不到累计航向(projected_gravity 对绕重力轴旋转不变,ωz 是速率不是角度)——世界系 yaw 奖励是特权塑形,只能教「少产生旋转」,不能教「偏了以后拉回原线」。… 主动纠偏闭环 = S3 把小 wz 进分布 + deploy --heading 外环 … 215 契约不加 heading 观测,冻结不动。”