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

19 cards tagged contract-freeze.

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

  Mechanism understoodrecoverytraining-runactuator-modelingcontract-freezecurriculum

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

  Symptom

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

  Context

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

  Change

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

  Outcome

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

  Mechanism

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

  Applies when

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

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

• Freeze the deployment contract, stamp every export, and let an automated checker catch wiring bugscontract-freeze-and-checker

  Replicatedomniprocesscontract-freezeprocesssim2sim

  Freeze and fingerprint the policy I/O contract; ship contract changes as new versioned profiles that leave old artifacts bit-identical; and extend the automated contract checker with every pipeline change, forcing the new path to execute in the check.

  Symptom

  Contract-level changes (observation layout, action pipeline) are where silent sim/real divergence is born; two real wiring bugs appeared the one time the action pipeline was extended.

  Context

  The 215-dim observation contract was frozen ("纪元 3，三机 digest" - an era number plus a digest agreed across three machines); proposals that would break it (e.g. a GRU memory) were rejected on contract grounds. Every exported ONNX is stamped and verified with a manifest (onnx_manifest --stamp / --verify), and deployment refuses mismatched combinations. When C4 added the lateral feed-forward, it went in as a NEW profile (omni_ff) leaving the existing omni profile's behavior bit-identical; the checker (check_contract) was extended to force the feed-forward path to actually execute (cmd_vy=0.13) and promptly caught two genuine bugs: (1) re-clamping with soft_joint_pos_limits after the feed-forward (0.23 rad deviation) instead of reusing the parent's clip; (2) indexing processed actions by asset.joint_names instead of the action term's own contract-ordered _joint_names, which landed the feed-forward on the wrong joints (l_hip_yaw / r_ankle_pitch).

  Change

  Contract discipline as implemented: frozen dims + digest; manifest stamping and refusal; contract changes only via new versioned profiles; checker updated in the same commit as any pipeline change, with inputs chosen so new code paths are exercised.

  Outcome

  Both wiring bugs caught before any training or deployment ("两个都是 check_contract 当场抓出来的 —— 这次它值回票价"); old deployments provably unaffected by the new profile.

  Mechanism

  The contract is the only interface the policy and robot share; freezing plus fingerprinting makes divergence detectable, and an executable checker turns "the contract holds" from a belief into a test - but only if its inputs actually drive the new code path.

  Applies when

  • modifying the action or observation pipeline of a deployed policy
  • exporting policies for hardware
  • proposals that would change observation dims or history structure

  “契约校验抓到的两个真错误（记账，别再犯）：1. 前馈后误用 soft_joint_pos_limits（URDF 限位 ×0.9）重钳 → 0.23 rad 偏差 … 2. 用 asset.joint_names 索引 _processed_actions → 前馈落到 l_hip_yaw/r_ankle_pitch 上 … 两个都是 check_contract 当场抓出来的 —— 这次它值回票价。”

• Slowing the gait clock at deployment is out-of-distribution and backfires - lower the commanded speed instead, or train the knobcycle-time-override-is-ood

  Mechanism understoodwalkreal-deployreal-acceptanceattributioncontract-freeze

  Any deployment override must correspond to a dimension the policy was trained to handle; to make a parameter field-adjustable, randomize it in training and observe it - otherwise use the levers inside the trained envelope (commands) and leave the knob alone.

  Symptom

  Real-robot feedback "walks very fast and unstable" suggested slowing the gait; a deploy-side --cycle-time override existed, making "just slow the clock" a one-flag temptation.

  Context

  A sim sweep of the override on walk_v6 @cmd 0.3 showed monotone degradation away from the trained 0.40 s cycle: at 0.50 s tilt jumped 7.9 -> 13.2 deg and landing force 1.52x -> 2.24x; at 0.80 s (half speed) clearance collapsed to 3 mm - dragging again - with 20 deg tilt. Meanwhile the legitimate lever, lowering the commanded speed with the clock untouched, improved everything monotonically: cmd 0.1 gave 104% tracking, 6.7 deg tilt, minimum slip - the most stable operating point. The file distinguishes the two "slows" explicitly: lower command = smaller steps at the same 2.5 Hz rhythm; a slower rhythm itself requires retraining - randomize cycle_time (e.g. 0.40-0.65 s) during training and expose it as an observation, and only then does --cycle-time become a field-adjustable knob.

  Change

  Deployment guidance: never ship a cycle-time override the policy was not trained under; respond to "too fast/unstable" with lower commands; schedule clock variability as a training-time (contract-level) change if a field knob is wanted.

  Outcome

  The sweep quantified the trap before hardware paid for it (dragging and 2.2x landing force at slowed clocks); cmd 0.1 documented as the stable demo point.

  Mechanism

  The policy is a function fitted around the training distribution; a deploy-side override moves an input (phase rate) to values never seen, so behavior degrades unpredictably - the knob LOOKS like a capability because it exists in the code, but capability lives in the training distribution, not the interface.

  Applies when

  • a deploy tool exposes overrides (clock, scale, gains) beyond the training distribution
  • hardware feels "too fast/aggressive" and a quick knob exists
  • deciding between a deploy-side tweak and a retrain

  “0.80s | 1.25Hz | 0.165 | 3mm(拖地) | 20.0° … 慢一半直接崩 … 策略按 0.40 训练, 别的周期属分布外。… 降指令速度才是有效杠杆 … cmd 0.1 是最稳的工作点。… 要节奏本身变慢必须重训 —— 训练期把 cycle_time 随机化(如 0.40~0.65s)并作为观测的一维, 部署时 --cycle-time 就成了现场可调的旋钮。”

• 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。”

• Changing the gait clock silently flipped a hardwired threshold's meaning - write derived constants as expressionsderived-constants-must-track-their-base

  Mechanism understoodwalkprocessprocessreward-shapingcontract-freeze

  Before changing any base parameter (clock, control rate, scale), enumerate every constant derived from it and every constant that must NOT change; convert derived literals into expressions of the base so the next change cannot silently flip a term's meaning.

  Symptom

  Slowing the clock 0.40 -> 0.50 s would have silently inverted the feet_air_time threshold's semantics: the 0.25 s threshold was hardwired, so at ct 0.40 the swing window (~0.20 s) sat below it (constant pressure to lengthen strides), while at ct 0.50 the window (~0.25 s) equals it - the term's meaning flips from "push longer" to "neutral" with no code error anywhere.

  Context

  The clock change audit walked every dependent quantity: most followed automatically (joint_pos_ref / clearance / contact_number cycle_time params, gait_phase observation, deploy/sim2sim/policy_io, export) - wiring confirmed, zero hand edits; the air_time threshold was the one hardwired constant, fixed by preserving the RATIO: 0.25 -> 0.3125 = 0.625 x ct, with the recommendation to commit it as the expression 0.625*ct "一劳永逸" (solved once and forever). The same audit also listed what must NOT follow the clock (50 Hz control rate, physics dt/decimation, 47-dim contract, action_latency absolute seconds, PD/torque limits) - the change's blast radius stated in both directions.

  Change

  feet_air_time threshold re-expressed as a fraction of cycle_time; auto-following vs must-not-change lists written into the spec for the clock migration.

  Outcome

  The clock migration (v10, repeated in v11) carried no silent semantic flips; the expression form removed the trap for every future clock change.

  Mechanism

  Constants derived from a base parameter encode a ratio at their birth; storing the evaluated number severs the dependency, so changing the base leaves stale semantics with no failing test. Expressions preserve the intent; and an explicit both-directions dependency list (follows / must-not-follow) is what makes a base-parameter change reviewable.

  Applies when

  • changing gait clock, control frequency, or units
  • a reward threshold interacts with a phase/window duration
  • config audit finds literals that encode ratios

  “feet_air_time 阈值 0.25 是写死的,不跟 ct 走——0.40 时摆动窗 ~0.20s<0.25(恒拉长压力),0.50 时摆动窗 ~0.25s≈阈值(语义翻转)。按比例保原压力:0.25 → 0.3125(=0.625×ct;建议直接写成 0.625 * ct 表达式,一劳永逸)。”

• PPO's Gaussian noise cannot compose phase-locked oscillations - deliver them as feed-forward and let the policy learn the residualfeedforward-for-phase-locked-skills

  Mechanism understoodomnicurriculumcurriculumreward-shapingcontract-freeze

  If a skill needs a temporally coherent (phase-locked) action component, do not expect step-wise exploration to find it: inject a verified feed-forward and train the policy as a residual stabilizer, keeping the feed-forward inside the deployment contract.

  Symptom

  Four different reward arrangements (no reference / wrong-sign reference / correct-sign reference / cage released) all failed to elicit sidewalk, while open-loop probes proved the behavior existed and was safe on the same platform with the same policy as base.

  Context

  Producing lateral velocity requires a phase-locked hip_roll oscillation synchronized to the gait clock. PPO's exploration is per-step, zero-mean, uncorrelated Gaussian noise - it can never compose a sustained phase-locked component, so the behavior is unreachable by exploration regardless of how it is rewarded. The fix changed the delivery channel: target = default + scale*action + lat_ff(cmd_vy, phi). The policy's action becomes a residual on top of the feed-forward, retaining full balance authority (it can even cancel the feed-forward); the feed-forward supplies exactly the component exploration cannot. This mirrors why the sagittal joint_pos_ref worked (it also delivered phase structure), just via a different channel.

  Change

  Contract-level change, done cleanly: new profile omni_ff (= omni + lat_ff_gain -0.5), existing omni profile bit-identical; feed-forward applied after the action delay stage; missing cmd/phase raises instead of silently dropping; deployment must use the same phi as build_obs (recomputing gives a one-tick phase misalignment).

  Outcome

  From C2-700, +100 iterations sufficed: product omni_c4_ff800 scored vy +120%/+125% (from +4%/-1%), 260/260 cells at 20/20 survival, zero old-skill regression, left/right gap 5 pp - the entire C4 saga resolved by changing the delivery mechanism, not the reward.

  Mechanism

  Exploration noise spans only the subspace its correlation structure can express; skills requiring coherent oscillation lie outside the span of i.i.d. per-step noise. Feed-forward moves the required structure into the action pipeline where it needs zero probability mass to appear, reducing the learning problem to stabilizing around a demonstrated behavior - which PPO does well.

  Applies when

  • a periodic/oscillatory skill trains flat under every reward variant
  • open-loop injection of the behavior already works
  • considering GRU/curriculum/exploration tricks for a rhythmic skill

  “病因不在奖励，在探索形式：产生侧向速度需要相位锁定的 hip_roll 振荡，PPO 的逐步高斯噪声零均值无相关，合不出相位锁定分量。… target = default + scale·a + lat_ff(cmd_vy, φ)。策略动作因此是前馈之上的残差，保留全部平衡权限”

• After seven patch-generations, freeze the lineage as a regression baseline, fix the structural debts, and retrain from zerofreeze-lineage-fix-structure-restart

  Observed oncewalkprocessprocesscontract-freezecurriculum

  When successive rungs keep trading one symptom for another, ask whether the remaining problems are structural (contracts, latency, sampling, reward-table architecture); if so, freeze the lineage as regression baselines, pay the structural debts, and restart minimal - carrying forward laws and instruments, not weights and weights' patches.

  Symptom

  The v5-v11 walk lineage had accumulated interacting patches (reward terms, gates, clamps, per-joint scales) faster than it converged on the user's goal; v12's spec itself was superseded before training by an external review's verdict that the remaining problems were structural, not parametric.

  Context

  The 2026-08-05 status banner records the pivot: the walk profile was rolled back wholesale to v10b parameters, the v5-v11 lineage frozen "只作回归对照" (kept only as regression baselines), and four structural debts were named as prerequisites for a from-zero straight-walk baseline: the action-latency FIFO (fixed with its own test), the ONNX manifest contract, discrete command sampling, and a minimal reward table. The v12 spec - fully designed, partially implemented - was suspended: "本规格挂起,不再按此开训".

  Change

  Strategy switched from "one more patch generation" to freeze-fix-restart: lineage checkpoints retained as comparison anchors, infrastructure hardened first, then a clean retrain with a minimal reward table (this restart produced the s* generation that later became the real-robot SOTA line).

  Outcome

  A designed-and-ready training generation was deliberately not run - the review's structural findings outranked sunk design cost; the restart line inherited seven generations of laws (calibrations, gate batteries, falsified fixes) without inheriting their entangled reward table.

  Mechanism

  Patch lineages accumulate coupled terms whose interactions eventually cost more to reason about than a restart costs to train; the knowledge worth keeping is the laws and instruments (measured plant values, calibrated gates, falsified directions), not the entangled weights. A restart on hardened structure converts the lineage's lessons into a clean initial design instead of another delta.

  Applies when

  • repeated rungs shuffle symptoms without net progress
  • an external review flags infrastructure/contract debts
  • deciding between another patch generation and a clean retrain

  “同日外部评审定调换路线:冻结 v5~v11 血统(只作回归对照),修结构性问题(latency FIFO 已修 tests/test_action_latency.py、ONNX manifest 契约、离散命令采样、最小奖励表)后从零训直行基线。本规格挂起,不再按此开训。”

• A policy's gain profile is part of its contract - the one-leg policy needs per-joint gains the default profile lacks, and the manifest refused an evaluation under the default once; the recovery contract's beta was never stamped, a known gap not to repeatgain-profile-belongs-in-the-stamp

  Observed onceonelegreal-deploycontract-freezeactuator-modeling

  Stamp everything that defines the closed loop a policy was trained in - gains included - into its manifest, and make every consumer refuse a mismatch; a profile field that is not in the stamp is a silent misconfiguration waiting for an operator to forget a flag.

  Symptom

  A policy trained with hip_roll kp 80 and ankle_roll kp 60 behaves differently, or falls, under the default kp 20/12 profile - and the gain profile is a command-line flag an operator can forget.

  Context

  The one-leg line added a gain_profile field to the contract so the stamped manifest carries it; the spec's deployment note says the manifest guard blocks rl_default and that it had already bitten once in simulation (an evaluation run without the one-leg profile). The same spec states the general rule - any new profile field must be synced into the manifest builder - and names the counter-example: the recovery line's beta was never put into the manifest. The recovery line itself had decided that its anchored authority is computed from the base rl gains and written into the contract so it cannot drift with the gain flag, and that the older kp x 0.9 profile chosen in the V0 era does not match the beta contract and must not be used.

  Change

  Gain profile as a contract field checked at load; per-contract gain choices written into the run sheets.

  Outcome

  Evaluations and hardware runs of the one-leg policy run under rl_oneleg or are refused; the recovery beta gap stayed recorded as known.

  Mechanism

  A policy is trained against a closed loop whose gains are part of the plant; running it under other gains is an out-of-distribution plant, exactly like a wrong observation scale.

  Applies when

  • a skill introduces per-joint or skill-specific gains
  • deployment gains are chosen by a command-line flag
  • adding any new field to a policy profile

  “增益档 `--profile rl_oneleg` 必须给 —— manifest 防线会拦 `rl_default`（sim 已咬合一次） … （recovery 的 β 未进 manifest 是已知缺口，不再复制）”

• When a contract default changes, old policies must run under a pinned legacy profile - a silent clock swap is out-of-distribution on hardwarelegacy-profile-pinning

  Mechanism understoodwalkreal-deploycontract-freezereal-acceptanceprocess

  Treat every trained policy as bound to the contract values of its training era: version the deployment profiles, pin old policies to their era's profile in every command, and never let a changed default silently apply to an old artifact.

  Symptom

  The walk profile's gait clock moved from 0.40 s to 0.50 s for new training, but versions v5-v9 were all trained at 0.40 s - running them under the updated default would silently feed a 25% slower phase clock to policies that never saw one.

  Context

  The re-test runbook hard-codes --policy-profile legacy_walk_040 into every command for the old versions, with the warning not to omit the flag: the mismatch is invisible (no error, no crash) but puts the policy out of distribution on hardware, where the same file had already documented that off-clock operation collapses gait quality.

  Change

  Deployment profiles versioned per training era; historical policies permanently associated with their era's profile; runbooks write the profile flag explicitly rather than relying on defaults.

  Outcome

  Old policies stayed runnable and comparable after the contract moved on; the silent-mismatch failure mode was closed by convention.

  Mechanism

  Changing a shared default rebinds every old artifact to a contract it was not trained under; unlike a schema break, a value change produces no error - only degraded, unexplainable behavior. Version-pinned profiles make the binding explicit and permanent.

  Applies when

  • changing any default in the deployment contract (clock, scales, gains) while old policies remain in use
  • writing runbooks that mix policy generations
  • a re-tested old policy behaves worse than its era's records

  “2026-08-02 起 walk profile 的时钟改为 0.50(WALK_V10_SPEC §3)。v5~v9 全是 0.40 训的,本文件所有命令已改带 --policy-profile legacy_walk_040 ——不要省掉这个 flag,否则是拿慢 25% 的相位时钟静默喂旧策略(分布外,真机危险)。”

• The restart reward table lists a reason for every term AND a lesson for every exclusion - absent terms are removed, not zero-weightedminimal-reward-table-with-provenance

  Mechanism understoodomnireward-shapingreward-shapingprocesscontract-freeze

  Maintain the reward table as an evidence ledger: every term cites the episode that justifies it, every excluded term cites the episode that convicted it (including the development stage it is valid at), and retired terms are deleted from the config, never left at weight zero.

  Symptom

  Seven walk generations had accumulated an entangled reward table where nobody could say which term earned its place; the restart needed a table that could be audited line by line.

  Context

  The minimal table v2 was built under three written principles: "一项管 一件事、结构性反抬脚的项一个不留、塑形只留一套相位逻辑;不在表里的一律 不加" (one term per job; zero structurally-anti-lift terms; exactly one phase-shaping logic; nothing outside the table gets added). Every row carries its provenance (e.g. base_height target = standing height cites the v4 crouch lesson; split x/y tracking cites the merged-exp gradient hole; world-frame yaw cites the v1/v3 body-frame lesson). Every EXCLUSION carries its same-type precedent: feet_landing_vel out because it is poison while the gait is unformed (drag pays 0, lifting pays - the v4-clearance / v8a-B "reverse threshold" family) though it was fine in v7 when the gait already existed - term validity depends on developmental stage; feet_air_time out with its measured non-lever evidence (v6 had it at 2.0 and still lifted 4 mm); and replaced terms are REMOVED from the config ("置 None,不是权重 0 挂着") so audits see truth, not dormant weight.

  Change

  Reward table rebuilt as ~20 rows each with weight + provenance column; exclusion list maintained alongside with the falsifying episode for each; dormant terms deleted rather than zeroed.

  Outcome

  Later revisions (S1.2/S1.3) modified the table by citing and updating specific rows' evidence rather than re-arguing the whole design; the table doubled as the lineage's reward-lesson index.

  Mechanism

  A reward table is a set of standing hypotheses; attaching each row's evidence makes revisions targeted and reversible, and recording why a term is absent prevents the cycle of re-adding known poisons. Deleting vs zero-weighting matters because config audits and DR interactions see the term either way - a zero-weight term is dormant complexity waiting to be flipped on wrongly.

  Applies when

  • designing a reward table for a restart or new task
  • someone proposes re-adding a previously removed term
  • auditing which reward rows still earn their place

  “原则:一项管一件事、结构性反抬脚的项一个不留、塑形只留一套相位逻辑;不在表里的一律不加 … feet_landing_vel(评审 #4):拖地时代价恒 0、抬脚才收费——与 v4-clearance/v8a-B 同属「反抬脚门槛」家族,步态未成形时是毒;v7④ 加它时步态已存在。… 已从 cfg 移除(置 None),不是权重 0 挂着。”

• No parameter tuning on the floor - a failing config retries once, then it is out; anomalies go back to simno-field-tuning-protocol

  Replicatedomnireal-deployreal-acceptanceprocesscontract-freeze

  Hardware time is for executing and measuring the pre-registered matrix, never for tuning: failing configs get one retry then elimination, anomalies get recorded and reproduced in sim, and contract-check bypass flags stay unused.

  Symptom

  Hardware sessions create pressure to fix problems live - nudge a gain, tweak a scale - which destroys attribution and risks the robot.

  Context

  The anomaly-handling section of the acceptance sheet is three fixed plays: (1) falls at start -> retry once at the same settings; falls again -> that configuration is eliminated, "不现场调参" (no on-site parameter tuning); (2) limit cycle or motor screech -> stop immediately, record the gain level and the joint, reproduce in sim before any discussion; (3) systematic disagreement with sim -> record it as a finding (hardware outranks sim) rather than adjusting anything to force agreement. Related guardrails elsewhere in the sheet: never pass --allow-unstamped / --allow-plant-drift to bypass manifest checks - if it errors, something real is wrong, stop and look.

  Change

  Field sessions restricted to executing the pre-written matrix; every fix path routed through sim reproduction and the normal config/rung process.

  Outcome

  Sessions stayed interpretable (each run matched a documented config) and safety overrides never became habit; anomalies arrived back in sim as reproducible cases instead of half-remembered floor stories.

  Mechanism

  Field-tuned values are measured under adrenaline on one floor with no logging or baselines - they contaminate the config lineage and are unattributable afterwards; and every bypass flag that skips a contract check converts a designed safety property into an operator promise.

  Applies when

  • a config fails or oscillates during a hardware session
  • someone reaches for a live gain tweak or a bypass flag
  • writing the anomaly-handling section of a deployment runbook

  “起步即摔 → 换档重试一次, 仍摔则该档出局, 不现场调参。出现极限环/啸叫 → 立刻停, 记录档位与关节, 回 sim 复现再议。… 不要给 --allow-unstamped / --allow-plant-drift —— 三枚 ONNX 都已盖章 … 真要报错说明有别的问题, 停下来看。”

• Size each joint's action authority to its measured working range - lock a channel to zero only when its job is provably elsewhereper-joint-action-scale-lockdown

  Observed oncewalkobservation-designobservation-honestyreward-shapingcontract-freeze

  Set per-joint action scales from measured target ranges and momentum decompositions: full authority for working channels, working-range authority for balance channels, zero for channels whose contribution is measured negligible - and prefer this structural quieting over perpetual reward penalties, keeping the contract dimensions intact.

  Symptom

  "Walks crooked" - roll and yaw channels wandered; hip_yaw peak-to-peak reached 22.3 deg in v11 while contributing essentially nothing to locomotion; a uniform action scale of 0.5 gave every joint the same authority regardless of its actual job.

  Context

  The v12 design replaced the scalar action scale with per-joint scales justified by measurements: pitch-class 0.5 (the gait's entire working channel - untouched); hip_yaw 0.0 - lossless because it carries only 1.4% of yaw momentum (v6 decomposition) and straight-line targets use merely +/-0.006-0.03 ("乱动纯属浪费"); roll 0.2 - NOT zero, because lateral balance and weight transfer are roll's unique job (locking it would degenerate into foot-edge rocking, "比现在更歪"), and 0.2 covers the measured working range +/-0.06-0.19 while "0.5 的另一半全是 '歪歪扭扭'的来源". Costs were accepted consciously: turning demoted to an observation row with a fallback (yaw 0 -> 0.1 in v13). Contract preserved: the 12-dim action interface unchanged, yaw values simply neutralized. The structural lockdown also RETIRED the reward-side hip_yaw_quiet penalty - "A 的 yaw=0 结构性取代,不再付奖励塑形成本".

  Change

  action_scale_joint pitch 0.5 / roll 0.2 / yaw 0.0 wired through robot.yaml -> policy_io (verified: all-ones action gives hip_yaw target exactly 0) -> Isaac action term, guarded by the contract checker ("它就是抓这种双侧不一致的").

  Outcome

  Designed and verified on the shared side before the lineage freeze; stands as the pattern for authority sizing: structure replaces reward shaping wherever a channel should simply not act.

  Mechanism

  Action scale is a per-channel authority budget; uniform budgets give noise channels the same voice as working channels, and reward-side quieting then pays a permanent shaping tax for what a zero scale provides for free. But zeroing is only lossless when decomposition proves the channel's contribution negligible AND no unique function (balance) lives there.

  Conflicts

  Wired and verified on the config/deploy side but never trained - the 2026-08-05 reset suspended v12 before the Isaac-side run.

  Applies when

  • some joints wander without contributing to the task
  • a quieting penalty (deviation/L1) taxes every step forever
  • deciding action-space authority for a new task or robot

  “yaw=0 是无损的:实测它只贡献 1.4% 偏航动量、直行目标只 ±0.006~0.03,乱动纯属浪费。… roll 不能为 0:横向平衡/重心换脚是它的独有职责,锁死会退化成脚缘摇摆(比现在更歪)。0.2 的依据:各代实测 roll 目标只用 ±0.06~0.19,0.5 的另一半全是"歪歪扭扭"的来源。”

• The walking lines' safety setting, power-scale 0.8, broke the recovery policy's full-range contract - it cut the ends of the joint travel (4/50 could not get up) and left the torque spikes untouched; a kp x 0.9 gain profile inside the trained kp band did the jobpower-derating-cuts-full-range-contract

  Observed oncerecoveryreal-deployreal-acceptanceactuator-modelingcontract-freeze

  A deployment derating knob means something only relative to the action contract: before reusing a line's "safe setting" on a new skill, check what it does to that skill's reachable range and to the term that makes the spikes, prefer a gain change inside the band the policy was randomized over, verify it in simulation, and re-decide when the contract changes.

  Symptom

  After the violent first real get-up (2026-08-09), the recovery policy needed a gentler setting for its next hardware test, and the walking and omni lines' standard derating - deploying at power-scale 0.8 - was the obvious candidate.

  Context

  The V0 recovery contract maps actions to absolute targets over the full joint range: a = +/-1 lands exactly on the URDF limits, and standing puts the knee at the clip. Candidates were compared on R3.1 in MuJoCo (5 categories x 10 seeds) on 2026-08-10 before any hardware time was spent.

  Change

  A new gain profile, rl_kp090 (kp x 0.9, kd unchanged), recorded in robot.yaml as the recovery hardware-test setting, with power-scale 0.8 explicitly banned for recovery.

  Outcome

  kp x 0.9: 48/50 got up; median torque demand on hip_pitch/knee fell from 120-125% to 100-104% of the deployment limit; leg-leg contact frames 2,152 -> 1,095; the change sits inside the +/-10% kp randomization the policy trained with. power-scale 0.8: 4/50 could not get up, because under the full-range contract it removes the ends of the travel (the deep squat's tucked legs, the straight standing knee), and the torque spikes (kp x error) did not fall at all. When the line moved to the beta-anchored contract, rl_kp090 was declared a V0-era choice that does not fit (beta is calibrated at kp 30) and deployment returned to rl_default; the deploy switch applies power scaling to the walking side only.

  Mechanism

  A power scale multiplies the action, which under an absolute full-range mapping shrinks the reachable workspace instead of softening the actuator; the spikes come from the proportional term on large errors, which only a gain change reduces - and a gain change inside the trained randomization band stays in distribution.

  Conflicts

  The undated operator runbook still carries an R3.1 "B comparison" command at power-scale 0.8 beside the rl_default baseline; the sources do not say whether it was written before the ban or was ever run.

  Applies when

  • reusing a power, torque or action scale from one skill on another
  • a policy whose actions map to absolute targets over the full joint range
  • choosing a gentler setting for a first or second hardware trial

  “kp×0.9 / kd 不动 —— recovery_r3_1 成功 48/50, τ 需求中位 hip_pitch/knee 120~125% -> 100~104% 部署限, 腿-腿接触 2152 -> 1095 帧; ±10% 在训练 kp DR 带内. ⚠️ power-scale 0.8 对 recovery **禁用**: 全 ROM 契约下 0.8 砍的是行程 端点 (深蹲收腿/站直够不到), 实测 4/50 起不来, 且尖峰 (kp·err) 一点不降 —— 它是 walk/omni 的安全档, 不是 recovery 的.”

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

  Observed oncerecoveryprocessprocesscontract-freezereal-acceptance

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

  Symptom

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

  Context

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

  Change

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

  Outcome

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

  Mechanism

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

  Applies when

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

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

• 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。”

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

  Mechanism understoodrecoverytraining-runactuator-modelingcontract-freezeattribution

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

  Symptom

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

  Context

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

  Change

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

  Outcome

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

  Mechanism

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

  Applies when

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

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

• Two machines, one configuration - every gain, offset and torque limit changes only in robot.yaml, whoever edits pushes at once, both checkouts show the same commit before the robot moves, and a pulled policy file is size-checked and synced before power-offtwo-machine-config-discipline

  Observed onceinfrareal-deployprocesshardwarecontract-freeze

  Treat the robot's configuration as a versioned artifact with one source of truth, push every change immediately, verify identical commits on every machine before a hardware session, keep hardware limits in the repo and the firmware in sync in both directions, and verify transferred model files (size, digest) before running them.

  Symptom

  The robot's onboard computer runs the bridge and deploy scripts from its own checkout while training and analysis happen on other machines; a hot fix left on one side, or a half-written file, silently makes the robot run something other than what was evaluated.

  Context

  The operator runbook's "wall version" of the two-machine discipline: configuration changes only in robot.yaml (calibration offset/sign, gains, torque limits), committed and pushed from the Mac, pulled on the robot, bridge restarted; code is not edited on the robot, and if it is, it is committed and pushed on the spot - nothing unpushed overnight; 30 seconds before every real-robot session both checkouts must show a clean status and the same last commit hash; changing tau_max requires writing the motor's limit_torque too (and the reverse); re-zeroed motors require re-measuring offsets. The recovery line added: after pulling on the robot, check the ONNX is not zero bytes (a lesson from a corruption incident on 08-12) and sync before cutting power; the recovery and main lines are separate worktrees, each pulled with --ff-only.

  Change

  Operating rules, pinned on the wall and repeated in the hanging checklists ("git pull, both machines on the same commit").

  Outcome

  The sources record the rules and the incident that produced the size check; they do not record a count of sessions the rules caught.

  Mechanism

  A policy is evaluated against one configuration; any divergence between the machines, or a truncated file, turns a hardware result into a result about an unknown configuration.

  Applies when

  • a robot's onboard computer and a workstation both hold the configuration
  • someone hot-fixes code or gains on the robot
  • model files are copied or pulled to the robot before a session

  “改配置只改 robot.yaml(标定 offset/sign、增益、限扭全在里面)→ Mac git commit + push → NX git pull → 重启桥。 … 谁改完谁立刻推,永远不留未推送的改动过夜。 … 每次上真机前 30 秒检查:两边 git status 干净、git log -1 哈希一致。 … 铁律不变:改 tau_max 必须同步写电机 limit_torque(反之亦然);电机重新标零后 offset 必须重测回填 yaml。”

• The deploy-side walk/recovery switch - into recovery at tilt > 65 deg held 0.3 s, back at tilt < 15 deg with angular rate < 1 rad/s and straight knees held 1 s, a 15 s timeout, last action cleared both ways - and the handoff steps the design required are only partly implementedwalk-recovery-fsm-handoff

  Observed oncerecoveryreal-deployreal-acceptancecontract-freezeprocess

  Specify a deploy-time controller switch as hysteretic, time-filtered predicates the robot can measure (proxy what it cannot, e.g. straight knees for height), a timeout that ends in a safe stop, and a complete handoff (history, clock, last action, command ramp) - then test that the code performs every handoff step, because the design document is not the implementation.

  Symptom

  With a recovery policy and a locomotion policy as separate networks, the robot needs a switch: when is it "fallen", when is it "up", and what state must be reset so the next policy does not act on the previous one's history.

  Context

  The 08-09 design: enter recovery when fallen (tilt > 55 deg or height < 0.60 x 0.384 m) for 150 ms, leave for a stand-hold when upright (tilt < 12 deg, height > 0.85 x 0.384 m, feet steady, |omega| < 0.8) for 400 ms - wide entry, strict exit, hysteresis - then a mandatory handoff trio before walking resumes (reset the walking policy's observation history, restart its phase clock at 0, clear its previous action and latency buffer) and a command ramp instead of a jump. The 08-14 implementation in deploy_policy (--recovery-policy): each policy under its own manifest contract (walk: nominal + scale; recovery: beta-anchored), the same rl_default gains, the tilt cutoff disabled; RECOVERY at tilt > 65 deg for 0.3 s; LOCO again at tilt < 15 deg and |omega| < 1 and knees straight (< 0.35 rad) for 1 s - the robot's computer has no height estimate, and straight knees stand in for height so a V3.0-style upright kneel cannot pass as standing; RECOVERY longer than 15 s ends in a safe stop; last_action cleared on both switches; command forced to 0 during RECOVERY; power scaling applies to LOCO only.

  Change

  An open account was written down with it: the recovery end state (0.355 m stance, hip yaw -/+27 deg) is outside the walking policy's training start distribution, so the first test must use stand / zero command as LOCO, and the long-term fix is to widen the walking policy's initial states rather than bend recovery's stance to suit walking.

  Outcome

  The spec records only a successful compile (py_compile); the hang test was left to be done on site. The operator runbook carries the three-step procedure (hang with stand as LOCO, mat and push, then omni walk as LOCO) but no outcome.

  Mechanism

  Two policies trained separately each assume their own history, clock and last action; a switch that carries any of them across feeds the next policy a state it never saw - the design called this "the walking policy seeing a ghost history".

  Conflicts

  The 08-09 design requires resetting history, clock and previous action plus a command ramp; the 08-14 implementation records last_action clearing and a zero command during recovery; the one-leg spec of 2026-09-14 lists the handoff hygiene as specified but not implemented - reset_history() is called by nothing (a 215-dim policy would carry four frames of pre-fall history), the phase clock is not zeroed, and there is no command ramp back to LOCO. No hardware run of the FSM is recorded in either source.

  Applies when

  • switching between separately trained policies on hardware
  • a policy with history or phase observations is re-enabled mid-run
  • the robot lacks a sensor the switching criterion was designed around

  “**判据**:进 RECOVERY = 倾角 >65°(`--fall-tilt-deg`)持续 0.3 s;回 LOCO = §5 真机可测子集:倾角 <15° ∧ |ω|<1 ∧ **膝直 <0.35 rad(NX 无高度观测, 高度门用膝直代理 —— 防 V3.0 型"跪坐但直立"误判)** 持续 1 s (`--recover-hold`);RECOVERY 单次 >15 s(`--recovery-max-time`)安全停。 … **切换卫生**:两向切换 last_action 清零;RECOVERY 态 cmd 强制 0”