Training Coach
Doctrine
A report may cite any of these as doctrine-N.
doctrine-1Contract freeze and fingerprint disciplineThe 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.
doctrine-2Attribution by resolved training params - never eval-override knobsCapability 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.
doctrine-3PASS gates become constraints; FAIL gates become objectivesOnce 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".
doctrine-4One variable per ladder rung - counted against what the checkpoint sawA 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.
doctrine-5Pre-register risks, readings, and stop criteria before the ladderBefore 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.
doctrine-6Plant parameters are measured, never inventedEvery 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.
doctrine-7Sim2sim gate before sim2real - under deployment conditionsEvery 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.
doctrine-8Observation honesty - the actor's inputs are a hardware contractThe 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.
doctrine-9Reward economics are audited in realized currencyReward 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.
doctrine-10The zero-cost option must be the desired behaviorFor 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.
doctrine-11Measurement discipline: independent referees, signs, distributionsA 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.
doctrine-12The deployment pipeline is plantIrreducible 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.
doctrine-13DR budget is finite; its distribution is the measured supportRobustness 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.
doctrine-14Gates measure what hardware feels: posture, margins, stripped assistsAcceptance 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.
doctrine-15Fork and root selection: recoverability, maturity, frozen rewardsChoose 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.
doctrine-16Curricula: verified engagement, lineage counters, disease-phase gatingAutomatic 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.
doctrine-17Probe before training: feasibility first, hypotheses in tablesAfter 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`).
doctrine-18External advice is recomputed locally; values transfer as ratiosEvery 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.
doctrine-19Hardware sessions are scripted experiments, not tuning sessionsReal-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.
doctrine-20Close questions in writing; restart when the debt is structuralAudited 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.
doctrine-21Name the quantity in the space it lives inA 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.
doctrine-22Continuation needs a live gradient; a release is chosen by a scanContinue 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.
Experience cards
165 cards matching “pipeline-latency-is-plant-not-dr”.
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 PPO
recovery-two-policies-and-a-state-machineDefine 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)”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §0 目标口径与架构判决(用户 2026-08-09 定) Under continuous 3-axis uniform sampling, pure straight-line walking is a zero-measure event the policy never trained
zero-measure-commands-need-mode-samplingEnumerate the exact command points users will actually issue (straight, stop, in-place turn) and give each explicit probability mass via mode sampling with off-axes pinned to zero - never assume a continuous sampler covers its measure-zero subsets.
Symptom
"The robot drifts even in sim when told to walk straight" persisted across reward tunings - because with commands drawn as vx in [0.15,0.5] x vy ~ U(+/-0.2) x wz ~ U(+/-0.6), the event vy=0 AND wz=0 has probability zero: pure straight-line walking was never sampled even once.
Context
Restart evidence item #3: "纯直行是零测度点 … 'sim 里直行就漂'是分布的 必然,不是 reward 没调好" - the drift metric was legitimately drowned by commanded turning (v11's own comment self-documented this). The structural fix is discrete mode sampling: a custom ModeVelocityCommand that first draws a mode by share (stand/forward/back/turn/side/mixed), then draws values only on that mode's axes with all others pinned to exact zero - which is also what preserves single-variable discipline in the C ladder (native 3-axis uniform "采不出'离散模式桶' … 把 C1~C4 的单变量纪律直接毁掉"). The mixed mode later got an ellipsoid constraint rather than a cube for the same reason in reverse - corner combinations of a cube are unrepresentative extremes.
Change
Command generation moved from independent per-axis uniforms to mode-bucket sampling with pinned-zero off-axes (plus 20% rel_standing); acceptance likewise evaluates per mode.
Outcome
Straight-line behavior became a trained, testable mode instead of a measure-zero hope; the C ladder could add one mode per rung with provable isolation.
Mechanism
A policy optimizes expected reward under the command distribution; events of probability zero contribute nothing to the objective, so exact-zero-command behaviors (straight walk, stand, in-place turn) are only learned if the sampler gives them mass. Product-of-uniforms distributions concentrate mass on mixtures and give none to the pure behaviors users actually command.
Applies when
- a "simple" command (straight, stop) underperforms mixtures in sim
- designing command distributions for velocity-tracking tasks
- a ladder needs per-mode isolation for attribution
“纯直行是零测度点:最终 command 为 vx∈[0.15,0.5] × vy∈U(±0.2) × wz∈U(±0.6) 连续均匀,vy=0∧wz=0 从未被专门采样 —— "sim 里直行就漂"是分布的必然,不是 reward 没调好 … Isaac 原生 UniformVelocityCommand 是三轴各自 uniform,采不出"离散模式桶"”
train/OMNI_V0_SPEC.md § 0. 为什么从零 (3) / 三件前置 (1) Curriculum-gate a penalty to the phase where its disease occurs - early on it only taxes exploration
gate-penalties-to-the-disease-phaseFor penalties aimed at late-stage pathologies (freezing, saturation, degenerate attractors), ramp the weight in only after exploration noise has decayed; anchor the terminal weight to measured healthy-vs-sick raw values, and shift all related tripwires to after the ramp completes.
Symptom
The action_saturation penalty, applied from iteration 0 in v8a, taxed exploration itself: with init_noise_std 1.2 the sampled actions paid ~-2.45/step before any policy had formed - while the disease it targets (clamp freezing) is a LATE pathology (v9 froze at iteration ~2624).
Context
v10 re-introduced the same penalty behind a curriculum gate: weight 0 until iter 1000, ramping linearly to -1.0 by iter 2000 - present only when the disease can occur, absent while exploration noise dominates. The trust argument was evidence, not hope: in v8a the term, while active, had pulled joint_pos_ref from 0.041 up to 0.155 and climbing - proof it can extract a policy from the frozen pit. Weight magnitudes were anchored to measured raw values (healthy v5 0.310 / v6 0.106 vs frozen v7 1.145 / v9 1.22 per step: at -1.0 healthy pays 6-18% of tracking, frozen pays 60%+, standing ~0). v10c then isolated the gated term as THE anti-freeze mechanism by single variable, upgraded to untouchable status in v11: "S 的门控机制(v10c 单变量铁案:任何情况下 不许撤,只许调终值)" - and v11 dared to relax other penalties only because S stood guard.
Change
action_saturation gated 0 -> -1.0 over iters 1000-2000 (later terminal value tuned -1.0 -> -0.5 with the gate mechanism itself frozen); tripwires adjusted to respect the gate's timing (freeze check moved to iter 2500-3000 to give the ramped term its effect window).
Outcome
Freezing stopped recurring while early training kept full exploration; the mechanism graduated from experiment to invariant within two versions.
Mechanism
A penalty's incidence depends on who occupies its support: early in training that is exploration noise (whose suppression starves learning), late it is the converged pathology. Time-gating aligns the penalty's presence with its target's presence, buying the constraint without the exploration tax - and tripwire timing must then be computed from the gate schedule, not from ungated precedents.
Applies when
- a structural penalty punishes exploration in early training
- a late-onset pathology (freeze/saturation) needs a standing guard
- deciding when a curriculum ramp should engage
“v8a 实锤它的病根是"罚在采样动作上"——init_noise_std 1.2 的早期等于罚探索(~−2.45/步);而冻结是晚期病(v9 速率 2624 才死平)… 门控让它只在病发期在场。… v8a 里它在场时 joint_pos_ref 从 0.041 爬到 0.155 且仍在升——有从低谷爬出的实证。”
train/WALK_V10_SPEC.md § 2. S 保险 —— action_saturation 课程门控 Torque caps cannot soften footfalls - impact is falling-mass momentum, only the reward can treat it
landing-impact-not-fixed-by-torque-capsClassify each hardware symptom by the physics that sets it: quantities fixed by ballistic momentum at contact must be treated through the policy's trajectory (reward terms on approach velocity/force), never through actuator caps - and size such penalty weights against your own tracking reward, not a lighter robot's.
Symptom
Footfalls slammed at 1.78x body weight in sim baseline (human walking: 1.2-1.5x); the tempting hardware-side fix was cutting actuator torque limits.
Context
Measured directly: scaling torque limits from x1.0 down to x0.4 left peak landing force essentially unchanged (1.75 -> 1.78x body weight) - the impact force comes from the momentum of the falling mass at touchdown, not from motor effort. The fix has to change the trajectory, i.e. the policy, i.e. the reward: feet_contact_forces penalty above a threshold of 113 N (= 1.2x the 9.58 kg robot's weight), clipped, weight -0.005. The weight was sized locally, not copied: the reference robot's -0.001 would amount to 0.9% of tracking reward on this robot ("策略不会理它" - the policy would ignore it); -0.005 gives 4.4%.
Change
Added threshold-type contact-force penalty (-0.005, threshold 1.2x body weight) as one of v6-minimal's three changes; hardware torque cuts explicitly rejected as a footfall treatment.
Outcome
Landing force 1.72x -> 1.55x by v6 (target <1.5x, missed by 3% - progress booked honestly); the torque-cap dead end was documented so it would not be retried.
Mechanism
At touchdown the ground stops a ballistic mass; the impulse is set by approach velocity and effective inertia, which motors can no longer influence in the final instant. Only earlier trajectory choices (approach velocity, timing) reduce it - and those are selected by the reward, not by actuator limits.
Applies when
- footfall impact or landing noise on hardware
- proposals to derate torque as a softness fix
- importing contact-force penalty weights from another robot
“⚠️ 硬件限扭降不了落脚力 —— 砸地力来自下落质量的动量: 实测 tau ×1.0→×0.4, 落脚力 1.75→1.78× 体重纹丝不动。只有这条奖励能治。… ⚠️ 权重不能用 Pi 的 −0.001 —— 实测在我们身上只占跟踪奖励的 0.9%, 策略不会理它 (Pi 6.94 kg 更轻)。−0.005 给到 4.4%。”
train/WALK_V6_MINIMAL.md § ③ 新增 feet_contact_forces Three times a joint-angle stand-in for a foot-level quantity was gamed or lied - the absolute ankle roll sold stance width to buy flat feet, a hip-roll criterion was confounded by 50 deg of yaw, and the joint table said 0.271 m where the feet were 0.159 m apart
joint-space-proxy-for-task-space-quantityExpress foot-level (task-space) goals and acceptance criteria in task space - link attitude and lateral spacing from world poses - never through joint angles that assume other joints are at zero, and measure the task-space value before trusting a joint-space estimate of it.
Symptom
The line's first real-robot get-up (v2_6, 2026-08-11) was "fairly stable", but after standing the feet were too close and the robot slid into the splits and fell several times; later reports added that it also got up through a split posture.
Context
(1) flat_feet penalized sum |q_ankle_roll|, but a flat foot is ankle roll compensating hip roll; the proxy taxed the compensated wide solution, and the untaxed combination was hips straight plus ankles at zero - flat and narrow. On hardware the lateral support shrank to hip width plus centimetres, lateral balance rested on 11 N*m ankle motors, and the feet slid apart. (2) The next rung's acceptance criterion "hip roll >= 20 deg" assumed zero hip yaw; at 50 deg of yaw the lateral contribution is x cos 50 ~ 0.64 - the same substitution again, inside a criterion. (3) A new task-space diagnostic reading the foot links' world poses measured v2_6c's stance at 0.159 m where the kinematic audit from joint angles had said 0.271 m (0.271 x cos 47 ~ 0.17).
Change
Rule written into the spec: task-space quantities are never expressed through joint-space proxies. V3.1's stance terms were all task-space: flat_feet_task from the foot links' world orientation, lateral foot spacing in metres, stand_pose stripped of both roll joints.
Outcome
From scratch with task-space terms (V3.1 P1c): lateral stance 0.355 m, foot residual tilt median 0 deg / P75 2.0 deg, all six criteria passing, mu 1.0-0.4 all 100%.
Mechanism
A joint proxy bundles the goal with everything else those joints do; the optimizer finds the combination the proxy does not tax, and a joint-based criterion silently assumes the other joints sit at their nominal.
Applies when
- rewarding flat feet, stance width, foot placement or end-effector pose
- an acceptance criterion is written in joint angles for a geometric goal
- joints with large yaw or coupled axes are involved
“**病根 = 关节空间代理**:`flat_feet` 罚 Σ|q_ankle_roll|(§41 取的简易口径)。 "脚掌平"的运动学正解是 **踝滚补偿髋滚**(q_ankle_roll ≈ −q_hip_roll) … 代理把"脚平"和"站距"绑死在一起卖了。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §43 真机首试入账(2026-08-11)判读:flat_feet 的代理口径错误 Write each config's expected hardware signature before the session - and if reality disagrees, change the books, not the conclusion
preregistered-real-expectationsBefore hardware runs, write per-config expected signatures and the disagreement rule (hardware outranks sim; discrepancies get recorded, not reconciled); validate the harness by checking it reproduces at least one known real behavior.
Symptom
Hardware impressions are easily narrated after the fact; without written expectations, any real-robot outcome can be made to "match" the sim story.
Context
The S2 acceptance sheet carried a section titled "sim 侧预注册预期 (事后核对, 不许事后改)" - per-configuration behavioral signatures written before the session: s1e@0.8 the disturbance king (push 159/160, zero chirality, all-mu 20/20) at the cost of speed gates 0/20 and zero-command wander ~0.98 m with -29.5 deg/20 s rotation; fric-3000 "walks accurately but is easier to push over"; fric-2400 neither. Credibility check included: the sim harness had reproduced the already-recorded real behavior (pace in place + right drift + net rotation -30 deg/20 s), which "提高本单全部预期的可信度". The anomaly clause fixed the epistemics in advance: if results systematically disagree with sim, "不改结论改账" - don't massage the conclusion, write the discrepancy into the books, and per the earlier zero-warning lesson, hardware wins.
Change
Every hardware session ships with a pre-registered expectation table (signature per config), a baseline-match credibility check, and a written precedence rule for disagreement.
Outcome
The A/B session became falsifiable: agreement confirms the proxy, disagreement is booked as a proxy-bias finding rather than argued away.
Mechanism
Pre-registration converts qualitative hardware sessions into tests of the sim-to-real mapping itself; a reproduced known behavior calibrates trust in the remaining predictions; and fixing "who wins on disagreement" beforehand prevents authority from drifting to whichever source flatters the plan.
Applies when
- planning any hardware acceptance or A/B session
- the sim harness's credibility in this regime is unestablished
- post-session write-ups tempt narrative fitting
“⚠️ sim 复现了真机已记录的「原地踏步 + 右漂 + 净旋 −30°/20s」—— harness 与真机行为对得上, 提高本单全部预期的可信度。… 结果与 sim 系统性不符 → 不改结论改账: 写进 README 该节, 按 「Isaac 指标三次零预警」的教训, 以真机为准。”
train/REAL_RUN_S2.md § 2. sim 侧预注册预期 (事后核对, 不许事后改) / 4. 异常处置 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 hardware
beta-anchored-action-targetFor 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)”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §33 V2.0 预注册(2026-08-10,用户点头开工):β 锚定动作空间,从零训 Exponential tracking kernels go flat exactly when the error is largest - pair them with an L2 term for the far field
exp-kernel-needs-l2-far-fieldNever let an exp/Gaussian kernel be the only tracking pressure on a quantity that can drift far from target: pair it with an unbounded (L2) term sized as the "don't diverge" floor, and check which frame the kernel reads.
Symptom
With only an exp-type yaw tracking term (exp(-err/std^2), std 0.25), a robot whose heading had drifted badly received almost no corrective gradient: at error 0.6 rad/s the term evaluates to exp(-0.36/0.0625) = 0.003 - near zero AND flat.
Context
The exp kernel is excellent for fine tracking near zero error but its gradient vanishes at large error - precisely when correction matters most. Fix: add track_ang_vel_z_err_l2 (-0.5), a plain quadratic on the same quantity: "exp 管精细跟踪、L2 管'别发散', 互补". Both terms deliberately read WORLD-frame wz (matching the exp term's source), because this torso sways enough that body-frame wz means are systematically off (measured -0.039 while actually turning +0.152). The same far-field-gradient argument reappears in the v8 risk list: frozen joints could not climb back because their huge error put them on the exp plateau ("远端梯度消失是冻结自锁的帮凶").
Change
Added the L2 companion term at -0.5 alongside the existing exp term (a term that had been in an earlier draft and was lost in a rewrite - itself worth noticing).
Outcome
Corrective pressure restored across the whole error range; the exp+L2 pairing became the house pattern for tracking terms.
Mechanism
d/de[exp(-e^2/s^2)] -> 0 as e grows: the kernel saturates and cannot distinguish bad from terrible. A quadratic's gradient grows with error, covering the far field; summing the two yields monotone corrective pressure with fine shaping near the target.
Applies when
- tracking rewards use exp/Gaussian kernels alone
- a drifted or frozen state fails to recover during training
- designing tracking terms for quantities with large transient errors
“exp 在误差大时梯度趋零, 恰好在最需要纠正的时候失灵。… 误差 0.6 → exp(-0.36/0.0625) = 0.003, 接近零且平坦。… exp 管精细跟踪、L2 管"别发散", 互补。”
train/WALK_V7_SPEC.md § ② track_ang_vel_z_err_l2 −0.5 —— 补 exp 的梯度洞 When the training reset distribution changes, freeze the acceptance distribution separately and pin its seed - the same checkpoint measured twice differed by 3.8 and 10.2 points
frozen-acceptance-distribution-and-pinned-seedAcceptance distributions are frozen artifacts, decoupled from whatever the training distribution becomes and evaluated with a pinned seed, and every gate row carries its sample size so that a small-n row is never read as a regression.
Symptom
R0.3 added easier roll-arc start states to the training resets, and the acceptance script shared the training category table; separately, the same checkpoint scored side 3.8 points and mid 10.2 points differently on two runs of the same script.
Context
Acceptance ran 512 parallel envs drawn from the fall categories. Had it kept following the training table, 15% of acceptance samples would have landed on states easier than prone - inflated scores and generations that could not be compared. The two same-checkpoint runs had identical per-item height medians, so the ruler had not changed; the spread was reset resampling noise (side n ~ 169, sigma 2.3%; mid n ~ 35, sigma 8.3%). The spec's "20 seeds" had always meant controlled seeds.
Change
accept_recovery.ACCEPT_CATEGORIES pinned to the four R0-R0.2 categories and decoupled from the training FALL_CATEGORIES; --seed 20260809 pinned, after which two consecutive runs were bit-identical. The mid row (n ~ 35) was labelled the bluntest gate.
Outcome
Every later generation (R0.3 through V3.1) was scored on the frozen distribution and seed, which is what let R0.3's intermediate state be read as "no measurable gain" (62.7 -> 62.1%) and R0.2's mid drop be booked as noise rather than a regression.
Mechanism
An acceptance set that follows the training distribution measures a moving target, and an unpinned reset draw adds sampling noise that small-n rows cannot absorb.
Applies when
- the training reset or command distribution changes between generations
- repeated evaluations of one checkpoint disagree
- a small category drives a pass/fail decision
“**分布冻结**:`accept_recovery.ACCEPT_CATEGORIES` 钉死 §5 四类 … 与训练侧 `FALL_CATEGORIES` **解耦**。 … **side 差 3.8 点、mid 差 10.2 点**(h 中位逐项一致,证明不是尺子变了)—— 纯 reset 重采样噪声 … 钉死后两次连跑逐位相同。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §14 验收尺子的两处加固(R0.3 起生效,向后兼容) When a contract default changes, old policies must run under a pinned legacy profile - a silent clock swap is out-of-distribution on hardware
legacy-profile-pinningTreat 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)。”
train/REAL_SWEEP_V5_V8.md § 1. 预检 ⚠️ 时钟改为 0.50 Every new penalty ships with a pre-registered withdrawal clause - if healthy gait must pay above the cap, the term stands down
calibration-threshold-with-withdrawal-clauseIntroduce every new penalty with: the zero-cost-option audit, a replay-calibrated weight formula (healthy pays a fixed small fraction of tracking), and a pre-registered withdrawal condition - and let the clause fire without argument when the calibration says the term cannot be afforded.
Symptom
Three same-shaped crashes had established a failure archetype: v4's clearance, v8a's landing window (weight off by 58x uncalibrated), and v6a's bare hip_yaw suppression all combined a zero-cost "don't move" option with a fee on any motion - a reverse barrier that pushes policies toward standing still.
Context
The v11 landing-window penalty was therefore introduced under a calibration-threshold protocol: (1) shape chosen with the window tightened (h_gate 0.03 -> 0.02, because 0.03 equaled the clearance target and priced the entire descent); (2) weight from a FORMULA, not judgment: measure the term's raw value on healthy replays (v5/v10b), set w = -(0.10-0.15 x tracking reward) / raw_healthy; (3) withdrawal clause pre-registered: if healthy gait must pay >15% of tracking no matter the tuning, the term is withdrawn to the next version rather than forced in - "不硬上". The companion hip_yaw quieting term ran the same protocol (calibrate on replays, healthy pays <=5%) and was later retired entirely when a structural fix (zero action scale) made its shaping tax unnecessary.
Change
Penalty introduction protocol: shape audit (what is the zero-cost option?), replay-based weight formula, healthy-pay cap with a written stand-down condition - all before training.
Outcome
The landing term was in fact withdrawn under its clause (v12 records "P5 落地窗口罚 已撤 … 维持撤下"), demonstrating the protocol firing as designed instead of the fourth same-type crash.
Mechanism
A penalty's damage mode is mispricing healthy behavior; since the healthy price is measurable in advance on replays, both the weight and the go/no-go decision can be computed rather than discovered by a ruined training run. The withdrawal clause converts "make it work" pressure into a clean deferral.
Applies when
- adding any motion-taxing penalty to a working gait
- a proposed term's weight has no measurement behind it
- a previous same-shaped term crashed training
“权重公式而非拍脑袋:先在 v5/v10b 回放上量 h_gate=0.02 的原始值,w = −(0.10~0.15 × 跟踪奖励) / raw_健康;标定门槛:若健康步态无论如何要付 >15% 跟踪,本项撤下留 v12,不硬上 (v4 clearance/v8a-B/v6a 三次同型翻车的教训:代价为零的"不动"选项 + 一动就收费 = 反向壁垒)。”
train/WALK_V11_SPEC.md § 6. P5 —— 落地窗口罚(三代欠账,标定门槛制) Compare the achieved reward to the computed ignore-floor to tell "never learned" from "learned but unprofitable"
ignore-floor-diagnosisFor any skill that trains flat, compute the reward the null policy would earn on that term; achieved==floor means the behavior never paid out (find why: exploration, reward observability, or feasibility) - do not tune weights first.
Symptom
C4 sidewalk failed on both arms; the question was whether the policy had found sidewalk and rejected it as unprofitable, or never found it at all - two diagnoses with opposite fixes.
Context
The theoretical value of the sidewalk tracking term for a policy that completely ignores the command was computable from the command distribution: 0.189. Trained final values landed at 0.187 (arm A) and 0.204 (arm B) - sitting exactly on the ignore-floor - while the honest balance at the optimum actually favored sidewalking (net +0.88/step inside the side bucket). Later the same arithmetic closed the whole saga: for the true reward landscape, doing real sidewalk scored 0.153 vs 0.944 for ignoring - the policy's refusal "是理性最优，不是探索失败" (rational optimum, not exploration failure) under one hypothesis, and under the final measurement-corrected account the policy had "每一次都在 做理性选择" (made the rational choice every time).
Change
Diagnostic rule adopted: compute the ignore-floor for the new term; if the achieved value sits on it, the behavior was never expressed in useful volume (or the reward cannot distinguish it - check both); if the achieved value is above floor but the behavior is absent at deployment, the policy sampled it and priced it out - then the reward balance, not exploration, is the lever.
Outcome
Correctly identified that PPO had not merely under-valued sidewalk; each subsequent hypothesis (waveform sign, regularization cage, exploration form, reward kernel) was tested against this floor arithmetic, which kept the search honest through three reversals.
Mechanism
Every reward term has a computable value under the null behavior; the achieved-vs-floor gap is a one-number audit of whether the optimizer ever monetized the target behavior. It converts "training failed" into one of two mechanistically distinct states with different fixes.
Applies when
- a new skill's tracking reward plateaus early
- deciding between exploration fixes and reward-weight fixes
- post-mortem of a failed curriculum rung
“track_lin_vel_y_exp 训练终值恰好坐在「完全无视指令」的底分上（A 0.187 / B 0.204，理论值 0.189），而终点 balance 明明有利（side 桶内净 +0.88/步）。不是学会了不划算，是根本没学到。”
train/C_LADDER_RUN.md § 3e. 为什么首战 FAIL A binary reward band on the swing knee had zero gradient everywhere below it, so the one-leg policy parked in an unloaded "fake touchdown" that Isaac's 5 N threshold scored as success and MuJoCo showed as real pressing - a capped constant-gradient ramp, retrained from scratch, passed 40/40
binary-band-reward-fake-touchdownShape approach-to-target rewards as capped ramps with gradient from the starting posture, never as bands or indicators; and compare contact-based terms across simulators, because a policy riding just under a force threshold looks perfect in one and wrong in the other.
Symptom
At iteration 1,000 of the first one-leg run the swing foot never lifted: the policy stood with the "raised" foot resting lightly on the ground. In Isaac the contact-match term paid 96% of full marks; the same policy in MuJoCo pressed that foot on the ground for 450 frames.
Context
The swing-leg goal was "shank folded fully back" (knee 1.5-1.95 rad), rewarded as a binary band: +0.8 inside [1.5, 1.95], zero elsewhere. From knee 0.05 to 1.5 rad the term was flat. Contact is judged at a 5 N force threshold, so a foot carrying less than 5 N counts as lifted. The walk line had hit the same disease with a binary indicator (v4) and fixed it with a capped ramp (knee_swing_amplitude).
Change
swing_knee_fold changed from the binary band to a ramp clamp(|q|/1.5, 0, 1) - a constant gradient capped near 86 deg - and the policy was retrained from scratch (V0r1). After the first real-robot try showed the fold still too low, its weight went 0.8 -> 2.0 (V0.1).
Outcome
V0r1 model_2300 passed the full acceptance 40/40 (swing knee 1.72 rad, about 98.5 deg) and was stamped as oneleg_v0.onnx; the cross-simulator disagreement is recorded as the thing that caught the cheat.
Mechanism
A reward that is flat until the target is reached gives no gradient to approach it, so the policy settles for the nearest state other terms reward - here, a foot that satisfies the contact threshold without lifting; a second simulator with different contact force resolution exposes such threshold-riding.
Applies when
- rewarding a posture target with an in-band / out-of-band indicator
- a contact threshold decides whether a foot counts as lifted
- trainer-side contact terms are near full marks while the video looks wrong
“初版二值带 [1.5,1.95] 在膝 0.05→1.5 全程零梯度，策略停在"卸力虚点地"（Isaac 5N 阈下 contact_match 96% 满分 / MuJoCo 同策略 450 帧实压——跨仿真器互证抓作弊）；v4 二值指示同型病，按 knee_swing_amplitude 判例改常数梯度封顶 ramp，从零重训 … **oneleg_v0.onnx = V0r1 model_2300, 40/40 PASS**”
git:Lucen V2@origin/oneleg-line:train/ONELEG_V0_SPEC.md § §4 奖励表 swing_knee_fold 行 / §8 核查单 5 Symmetrizing the config made the gait MORE asymmetric - the asymmetry lived in the policy weights
asymmetry-in-weights-not-configLocalize a persistent asymmetry by intervening at the config layer first: if the symptom survives (or worsens), it is in the weights - fix it with symmetry-constrained training, not with trims or offsets.
Symptom
walk_v1 on hardware: straight-line command curved 149 deg in 15 s (9.9 deg/s) with 3.06 m lateral runout; turn gain +31% one way vs +129% the other (75% difference); knee asymmetry 4.4 deg in sim, 9.6 deg on the robot.
Context
The obvious suspect was the asymmetric default pose in the config. The decisive test: symmetrize standing_pose and run the SAME policy in sim - the asymmetry got LARGER (hip_pitch 6.8 -> 9.3 deg). Root cause therefore not in the config but baked into the policy weights: PPO without a symmetry constraint commonly converges one-sided, because splitting the work 50/50 and loading one side yield the same return, and the gradient falls randomly into one of the equivalent optima.
Change
Fix redirected from config trimming to retraining with mirror data augmentation (walk_v2 spec) - a weights-level fix for a weights-level disease.
Outcome
With augmentation (and the symmetric-default precondition), stand_v1 reached 0.0 deg asymmetry on all six joint pairs (from 4.4-7.7 deg), height fluctuation 7 mm -> 1 mm, mean |action| down 33%.
Mechanism
Reward-equivalent solution families (who carries the load) leave the symmetric solution unpreferred; SGD picks an arbitrary member and entrenches it. Config changes move the coordinate frame around the entrenched asymmetric function - they cannot move the function. The counterfactual test (change config, watch symptom) localizes the layer the disease lives in.
Applies when
- a robot veers or loads one side despite a symmetric-looking config
- deciding between config trims and retraining for an asymmetry
- mirrored-turn gains differ by tens of percent
“根因不在配置里:把 standing_pose 对称化后在 sim 里跑同一策略,不对称反而变大(hip_pitch 6.8°→9.3°)—— 说明不对称烙在策略权重里。这是无对称约束的 PPO 的常见收敛结果(左右各担一半与一边多担的回报相同,梯度会随机落进其中一个)。”
train/RETRAIN_v2.md § 1. 为什么是对称增强(证据) Mirror augmentation over an asymmetric default injects systematic error - symmetrize the default first and verify the transform bit-exact
mirror-augmentation-needs-symmetric-defaultBefore enabling any symmetry augmentation, make every constant inside the observation encoding exactly symmetric, and validate the mirror transform against forward kinematics to machine precision - an unverified augmentation is a new error source, not a regularizer.
Symptom
Mirror data augmentation was about to be added while both default poses (standing_pose, walk nominal_pose) were asymmetric - stale hand-tuned compensations from before a ground re-calibration, with hip_yaw differing 2.40 deg between sides and the foot soles actually tilted (pitch 2.88/1.35 deg, roll -2.47/+0.25 deg).
Context
The observation encodes joint_pos_rel = q - default. Under mirroring q_l -> -q_r, the relation (q-default)_l -> -(q-default)_r holds only if default_l = -default_r; with an asymmetric default, augmentation produces observation pairs that are NOT mirror images, i.e. "default 不对称时做镜像增强会引入系统性错误,比不做还糟" (worse than not doing it). The fix: adopt model geometric zero as standing default (MuJoCo FK verified: sole pitch/roll exactly 0, asymmetry 0.00 deg) and a symmetric crouch for walk (hip -0.25/knee -0.5/ankle -0.25 satisfying hip - knee + ankle = 0 to keep soles flat). The mirror transform itself was verified bit-exact before use: pseudovector vs polar-vector sign patterns (ang vel [-1,1,-1], gravity [1,-1,1], cmd [1,-1,-1]), joint swap-and-negate; FK check that left-foot pose under q equals the mirror of right-foot pose under mirror(q), measured error 0.00e+00.
Change
Defaults symmetrized first (with init heights recomputed by FK), stand policy retrained on the new default so both policies share one default; augmentation enabled only after the FK mirror test passed.
Outcome
stand_v1 achieved exact left/right pairing (l_knee -0.1013 / r_knee +0.1013), six-pair asymmetry 0.0 deg, height fluctuation 7 -> 1 mm, 33% less mean |action|.
Mechanism
Augmentation asserts an equivariance of the observation encoding; any asymmetric constant inside the encoding (the default) breaks the asserted symmetry, so the augmented data teaches a false invariance. Verifying the transform against FK geometry tests the assertion end to end, independent of the training stack.
Applies when
- adding mirror/symmetry augmentation to locomotion training
- defaults or trims were hand-tuned per side at any point
- observations are expressed relative to a default pose
“观测里 joint_pos_rel = q − default。镜像下 q_l → −q_r,要让 (q−default)_l → −(q−default)_r 成立,必须 default_l = −default_r。default 不对称时做镜像增强会引入系统性错误,比不做还糟。… 位置误差与姿态矩阵误差实测均为 0.00e+00。”
train/RETRAIN_v2.md § 2. 前提:default 姿态必须先对称化(不是可选项) / 3. 镜像变换 Verify changes in the run's resolved config (and checkpoint md5), never in the source you edited
resolved-config-is-source-of-truthAttribution 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。”
train/C_LADDER_RUN.md § 3d. ⚠️ 开训前先验落盘 cfg / 3l. 回退清单（验证） Real-robot trials of a new skill were staged by risk - a hanging dry run with the robot posed by hand, then one short try per category on a mat with the hardest last, then the composed behaviour (switch + walking) last - with the user present and a log every time
staged-hang-mat-floor-for-get-upStage a new skill's hardware trials by risk - hanging dry run posed by hand, one short try on a mat per start category with the hardest last, the composed behaviour last - with an operator ready to cut enable and a log for every try; relax a safety ban only for short, attended runs and say so in writing.
Symptom
A get-up policy acts violently near the ground by design, and the first unstaged real run of the line was stopped as dangerous.
Context
The hanging checklist written with the first stamped recovery product (v2_5, 2026-08-11), to be ticked item by item with the user present: both machines on the same commit and firmware torque limits checked; the robot hung from a single point about 0.1 m off the ground; a dry run with the robot posed by hand into supine and prone to watch that the target stream is gentle (the beta contract keeps targets within +/-0.25 rad of the measured pose, so enabling causes no homing fling); the first floor try is supine only, on a mat, once, with torque and joint logs; categories are added one at a time, prone last; any kicking or oscillation cuts enable immediately. For the switch (08-14) the runbook orders: hang with the standing policy as the locomotion side, then on a mat push the robot over and let it recover, and only last swap in the walking policy. An exemption was also written: edge-standing policies stay banned from long or unattended runs, but a short single A/B with the user present, hung or on a mat, is allowed. The one-leg line reused the same order (hang, then floor with a spotter, 60 s segments with a temperature check).
Change
Real trials as a checklist of stages, each gated on the previous one, with the composed behaviour last.
Outcome
The line's first real get-up (v2_6, 08-11) came through this protocol and was reported "fairly stable"; no further hardware outcomes of the switch are recorded in the spec.
Mechanism
Each stage exposes one new risk (commanded targets without contact, a single category with contact, harder categories, then the interaction of two policies), so a failure is attributable and cheap.
Applies when
- first hardware trial of a recovery, jumping or other high-impact skill
- switching between two policies on hardware for the first time
- a policy with a known posture defect needs a comparison run
“吊挂空跑: 手动摆到 supine/prone 姿态, 看目标流是否温和 (β 帽 7.5 N·m, 目标永远贴着当前 q ±0.25 rad —— 使能瞬间无归位甩动, 这是 β 契约附带保证) … 落地首试: supine 一类, 垫子, 单次; τ/q --log 全程记录 … 逐类别扩展 (prone 最后), 每类先单次”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §40 吊挂执行单(真机首试;需用户在场,逐项打勾) Model CAN polling skew - joint observations are 6-9 ms stale by read order
can-timing-skew-modelingIf joints are read sequentially over a shared bus, reproduce the per-group observation staleness in sim (or randomize it over the measured range) - synchronous observations are a privileged fiction.
Symptom
Policies trained with synchronous joint observations degrade on hardware where motors are polled sequentially over CAN - hip data is already 6-9 ms old by the time ankle data arrives.
Context
Menlo's core sim2real finding on a leg platform of almost identical mass to Lucen's. CAN is a sequential bus: one poll cycle reads motors in a fixed order, so the observation vector mixes timestamps. Lucen runs 6:6 motors on a dual CAN split, so the problem transfers one-to-one.
Change
Explicitly model CAN timing skew in sim: give the joint groups different observation delays matching physical read order. Menlo went further - running real firmware in the loop with a motor simulator between MuJoCo and firmware injecting 0.4-2 ms uniformly distributed delay.
Outcome
Reported by the reference team as their core sim2real enabler on a same-scale platform; recorded in Lucen's experience log as directly applicable ("这个问题一模一样").
Mechanism
A policy exploits any cross-joint temporal coherence present in training observations; when hardware breaks that coherence per bus position, the learned feedback acts on inconsistent state estimates, injecting phase error exactly at the control bandwidth.
Conflicts
Second-hand episode: outcome numbers are the reference team's report, not a Lucen-run experiment; Lucen adopted the requirement but the corpus has no Lucen A/B of skew-on vs skew-off.
Applies when
- robot polls actuators sequentially over CAN/RS485 or similar shared bus
- sim2real degradation appears as jitter or oscillation not seen in sim
- designing the observation/delay model before a training run
“电机走 CAN 是顺序轮询的，髋部电机的数据到踝部电机上报时已经陈旧了 6-9 ms，他们直接在仿真里显式建模了 CAN 时序偏斜，按读取顺序给三组关节不同的观测延迟。… 注入 0.4–2 ms 的均匀分布延迟。你们是 6:6 双 CAN 分总线，这个问题一模一样”
Experience.md § 执行器 + 时序建模 (line 6) The 1.5 Hz step-frequency gate was retracted - the author had misread his own actuator data, and the limit fought pendulum dynamics
gate-threshold-retracted-frequencyEvery gate threshold must cite its measurement and survive a first-principles sanity check; when a gate keeps failing otherwise healthy behavior, re-derive the threshold from the raw data before enforcing it again - and retract wrong gates in writing.
Symptom
An acceptance criterion "step frequency <= 1.5 Hz" kept failing healthy policies (walk_v4 at 2.33 Hz), and an earlier attempt to force slower stepping (v3) had killed stepping altogether.
Context
The threshold had been derived from the author's own actuator frequency-response measurements - but re-reading the raw table showed the misread: amplitude ratio at 2.0 Hz is 0.88 (knee) / 0.83 (ankle), acceptable; the genuinely bad point was walk_v2's 3.8 Hz at 0.58. Mechanism check agreed: the leg as a compound pendulum (L ~ 0.30 m) has a natural frequency ~1.1 Hz, swing half-period 0.45 s - the observed 2.1-2.4 Hz sits near where the leg wants to swing, and forcing 1.5 Hz "是跟摆动动力学对着干" (fights the swing dynamics). The frequency definition itself was pinned by two independent methods (contact-event counting 2.39 Hz vs FFT 2.33 Hz, agreeing): reported numbers are cycle frequency = steps per leg per second.
Change
Gate retracted in writing: "步频 ≤1.5 Hz 应删除或放宽到 ≤2.5 Hz"; frequency definition standardized before entering any config.
Outcome
walk_v4/v6's 2.1-2.4 Hz reclassified from disease to normal; the v3 failure got its probable explanation (suppressing stepping to meet a wrong gate).
Mechanism
A gate is only as good as the measurement and the reading behind it; thresholds inherited from a misread plot become invisible design constraints that later training obeys at real cost. Cross-checking a threshold against first-principles dynamics (pendulum frequency) is a cheap way to catch such misreads.
Applies when
- an acceptance threshold repeatedly fails policies that look healthy
- thresholds were set from a single person's reading of raw data
- a forced compliance with a gate degrades the behavior it guards
“我当初依据自己测的执行器频响定的,但看错了区间。… 2.0~2.4 Hz 的幅值比 0.83~0.88 是可接受的;真正不行的是 walk_v2 的 3.8 Hz。… 腿按复摆算(L≈0.30 m)自然频率约 1.1 Hz … 把它压到 1.5 Hz 是跟摆动动力学对着干(walk_v3 把迈步压没了,可能正是这个原因)。”
train/WALK_DIAGNOSIS.md § ② 撤回"步频 ≤1.5 Hz"这条验收标准 —— 是我定错了 A single run's drift direction may be a limit cycle, not a policy bias - check the sign distribution across seeds
multiseed-sign-test-for-driftDistinguish "bias" from "broken symmetry limit cycle" by the sign distribution over many seeds; report drift as (mean, sign split), and never compare single-run drift magnitudes across versions.
Symptom
Net yaw over 15 s appeared to worsen from -41 deg (v2) to -84 deg (v4), inviting the conclusion that the new version drifted more.
Context
The Isaac-side view across 32 environments told a different story: per-env yaw was mixed-sign (20 negative / 12 positive) with mean ~0 - the drift is a limit cycle whose direction depends on initial conditions, not a systematic policy bias. The single MuJoCo run had sampled one draw from that distribution, so its magnitude could not be compared across versions as if it were a property.
Change
Evaluation rule: before classifying drift as systematic, run multiple seeds and examine the sign distribution; single-trajectory drift magnitudes are samples, not properties.
Outcome
The v2-vs-v4 drift "regression" was reclassified as not-established; later drift work (hip_roll l+r bias) used cross-policy, cross-seed evidence instead.
Mechanism
Symmetric dynamical systems can settle into either of two mirrored limit cycles; the selected cycle is decided by noise and initial state. A statistic whose sign is initial-condition-dependent has no meaning as a single sample - only its distribution does.
Applies when
- comparing heading drift or lateral drift across policy versions
- a symmetric-looking behavior shows a consistent direction in one run
- deciding whether to fix "drift" in reward or calibration
“偏航反而变差(−41° → −84°):注意 Isaac 侧 32 env 的逐 env 偏航是正负混合(20/12)、均值 ≈0,说明这是极限环性质(方向随初值)而非策略偏置 —— MuJoCo 单次跑测到的是分布里的一个样本,不能当作系统性偏差。要判断需多种子统计。”
train/WALK_DIAGNOSIS.md § walk_v4 独立验收 读法 (偏航) A proposal in the runbook - torque and action limits as versioned safety tiers (classroom / research / expert) written to motor RAM and read back, separate from the reward's effort penalty - recorded as a proposal, its implementation unrecorded
safety-limits-are-a-layer-not-a-rewardKeep hardware limits as an explicit, versioned safety layer (tiers written and read back at start, the persisted default the safest one) and the effort penalty as a behaviour layer; when a skill needs more torque, change tier deliberately rather than trading one layer against the other.
Symptom
Running and jumping need more torque than the deployed limits allow, and the temptation is to trade the training-side effort penalty against the hardware limit, or to hand a new user a robot "tuned however the last person left it".
Context
A message pasted into the operator runbook (undated, citing Berkeley's practice of storing the full motor configuration as JSON with write and read-back scripts) proposes: configuration is a versioned artifact, not a verbal agreement; three safety tiers in robot.yaml beside the gain and policy profiles - classroom (RS06 limited to 10 N*m, lateral joints clamped: "however bad the policy, it only moves awkwardly"), research (14 N*m, clamps at twice the measured need, the default) and expert (the 36 N*m rating, joint limits only, requiring an explicit flag); deploy writes the tier to motor RAM at start and reads it back, while the stored copy stays classroom so a power cut returns to the safest state. It frames limits as the safety layer and the effort penalty as the behaviour layer - more torque for running means switching tier, not weakening the penalty.
Change
None recorded: the message ends by asking which to do first, a rollback or the tiers.
Outcome
The sources do not record the tiers being implemented; the deployed limits stayed at 12/17/11 N*m through the recovery and one-leg lines (the one-leg spec treats raising the RS06 limit as a separate, unapproved hardware decision). Related and recorded elsewhere: torque limits were written to RAM only in a scripted, self-reversing field experiment.
Mechanism
Hardware limits bound the damage any policy can do; reward terms shape what a policy prefers. Mixing them either weakens safety to buy behaviour or distorts behaviour to buy safety.
Applies when
- a new skill needs more torque than the deployed limits
- robots are handed to students or new users
- motor configuration lives in people's heads or in the firmware only
“配置是版本化的产物,不是口头约定。 … deploy_policy 启动时按档写进电机 RAM 并读回校验(落盘的那份永远保持 classroom,断电自动回到最安全状态)。 … 限幅是安全层,dof_torques_l2 是行为塑造层,它们在不同的层,不冲突。跑步要更大力矩就换档,而不是去动训练里的省力惩罚。”
RL系统/FOLLOW THIS copy 2.md § 面向 developer / 教育机构该怎么做 (pasted proposal, undated) A sim veto needs real confirmation too - the worst sim cell was scheduled as the most informative hardware run
sim-veto-needs-real-confirmationNever let sim alone both condemn a purpose-built configuration and escape audit: spend one cheap, safeguarded hardware run on the condemned cell, pre-registering what agreement and disagreement would each imply about the proxy.
Symptom
The fric-2400@kd1.0 combination was sim's worst cell across the board (survival 17/20 - the only miss, mu0.4 1/20, push 103/160, zero-cmd 2/20), yet it was the only product specifically trained for the kd1.0 deployment gain - discarding it on sim evidence alone would leave the sim's own validity untested exactly where it mattered.
Context
The team had been burned in the other direction before ("Isaac 指标三次 零预警" - training-side metrics gave zero warning three times), so the symmetric rule was written: sim's rejection also needs hardware confirmation ("sim 判被支配 ≠ 真机被支配 … sim 的否决也要真机确认"). The run was pre-registered with a dual reading: real matches sim -> the S2f ladder closes and the fork root is settled; real clearly better than sim -> the MuJoCo proxy has a systematic bias in the kd1.0/low-margin region, "那比选型本身重要得多" - and every S2f sim acceptance would need re-scoring.
Change
The condemned configuration was kept on the hardware roster (last, spotted, minimal exposure) explicitly as a proxy-validation probe, not as a deployment candidate.
Outcome
Session design captured either result as progress: selection confirmed, or a proxy bias discovered that would re-price the whole ladder's verdicts.
Mechanism
Every sim verdict is a joint statement about the policy AND the proxy; cells where a policy was purpose-trained for the exact condition sim condemns are where proxy error is most likely and most costly. Testing the veto converts a selection decision into a calibration measurement of the evaluator itself.
Applies when
- sim rejects the configuration that targets the actual deployment condition
- the eval proxy's calibration has never been checked in that regime
- deciding which hardware runs are worth their risk
“但它也是唯一为 kd1.0 部署档专门训的产物 —— sim 判被支配 ≠ 真机被支配, 「Isaac 指标三次零预警」的教训反过来同样成立: sim 的否决也要真机确认。… 若真机明显好于 sim → MuJoCo 代理在 kd1.0/低裕度区有系统性偏差, 那比选型本身重要得多。”
train/REAL_RUN_S2.md § 上机名单 note / 2. sim 侧预注册预期 ⑤ A 10 s acceptance episode left 6-7 s of standing to observe - a narrow stance held for that window and split on hardware; a gate cannot see instability slower than its own horizon
episode-length-bounds-what-a-gate-seesSize the standing phase of an acceptance episode, and its disturbances and floor friction, to what deployment will impose; a pass on a short static window certifies only that window.
Symptom
v2_6 passed every simulation gate (success 99.6%, re-falls 0-1%) and then, on the real robot, stood up and slid into the splits several times; prone starts stood and then fell backwards.
Context
Acceptance ran 10 s episodes; a ~1-2 s get-up left roughly 6-7 s of static standing on a nominal floor with no disturbance. The narrow stance's lateral margin and the straight-knee stance's lack of any flex buffer are both failure modes that need time, disturbance or lower friction to show.
Change
The gap was booked as a known blind spot of the gate ("long-duration standing stability") alongside the task-space stance criterion; later rungs added MuJoCo friction sweeps at mu 0.4 to every checkpoint scan.
Outcome
The spec through §50 records the blind spot but no longer standing window or disturbance row in the recovery acceptance itself.
Mechanism
An acceptance episode observes only the dynamics that unfold within its horizon under its conditions; slow drifts and disturbance-triggered failures are outside it by construction.
Applies when
- a policy passes sim gates and fails on hardware after a delay
- acceptance episodes are short relative to deployment use
- stability is judged without pushes or friction variation
“**sim 门为什么没逮住**:10 s episode 起身后只站 ~6-7 s,静态窗口内窄站距 撑得住;真机站立时长/扰动谱在门口径之外 —— 长时站立稳定性记为口径缺口。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §43 判读:sim 门为什么没逮住 Diagnose a behavior failure by enumerating hypotheses and auditing each against the actual config, cheapest first
hypothesis-table-code-auditBefore changing anything, write the full hypothesis list for the symptom and audit each against the resolved config and measured magnitudes, cheapest check first; train only on the survivors.
Symptom
Real robot leaned forward "wanting to walk" but dragged its feet instead of lifting them - a symptom with many plausible causes and no obvious single fix.
Context
Seven hypotheses were listed and each checked against the actual training config files (velocity_env_cfg.py, isaac_values.py), ordered by check cost: missing foot clearance term (CONFIRMED, primary - feet_air_time existed but no swing-height term at all); energy penalties dominating (REJECTED - energy terms total -0.19 vs tracking +1.2, 16%); command range too narrow (CONFIRMED - (0.15,0.35)); nominal pose too crouched / action scale too small (HALF - knee 0.5 rad = 28.6 deg deep, scale fine); mixed PD across motor types (REJECTED - already grouped); missing base-height reward (REJECTED - present at -5.0); height-drop termination (REJECTED - none exists, which itself became finding #4 of the fix list).
Change
The audit produced a ranked fix list (add clearance penalty; widen speed range; reduce nominal crouch) with each rejected hypothesis documented so it would not be re-litigated.
Outcome
Three confirmed causes fixed over v5/v6: swing height went 22-23 mm -> 34 mm, tracking 81% -> 87%; the rejected hypotheses stayed rejected (no wasted rungs on energy weights or PD grouping).
Mechanism
Multi-cause symptoms invite guess-and-train loops; a written hypothesis table forces each candidate to be confirmed or rejected against actual values (not impressions), and cost-ordering the checks means most hypotheses die for the price of reading a config.
Applies when
- a real or sim behavior failure has multiple plausible causes
- the team is about to "try a fix" without an audit
- post-mortems keep re-proposing already-rejected causes
“真机现象:躯干前倾像要走,脚抬不起来(拖着蹭)。按成本从低到高逐条核查 … | 1 | 缺 foot clearance | ✅ 成立,首要 | 有 feet_air_time,无任何摆动足高度项 | | 2 | 能量惩罚压过跟踪 | ❌ 不成立 | 能量类合计 −0.19,跟踪 +1.2,只占 16% |”
train/WALK_DIAGNOSIS.md § walk 拖地问题 — 七条假设的代码核查结果 Gate a new reward term by its command so all old modes score pointwise identical
gate-new-reward-terms-by-commandWhen a reward term must be added mid-lineage, gate it on the condition that defines the new task so every pre-existing situation scores exactly as before - and still watch for value-rescale pathologies inside the new mode.
Symptom
Adding a lateral tracking reward (track_lin_vel_y_exp) ungated would have paid 0-2.0 per step even in modes with cmd_vy = 0 (healthy gait sway of vy ~0.1 already earns 1.28), shifting the whole reward table by a large bias and rescaling the value function - no longer "just adding one mode".
Context
C4 was the C ladder's only true reward surgery. Single-variable discipline required that the change be invisible to every existing mode. The chosen construction: gate_by_cmd=True - the term pays only when |cmd_vy| > 0.02, so for all modes with cmd_vy == 0 the term is pointwise zero, i.e. the reward is pointwise identical to before the change. The same trick appeared earlier in C1: replacing the vy L2 tax with a command-error version that is "对 cmd_vy≡0 逐点同值" (pointwise equal when cmd_vy is 0), explicitly classified as not-a-reward-change.
Change
track_lin_vel_y_exp added with gate_by_cmd=True (weight +2.0, std 0.15); the residual acknowledged honestly - inside the side bucket the values DO change, so the rung still watched the known reward-reshuffle pathology signature (s1c B-arm: scatter -> half-recover -> collapse) as a stop criterion.
Outcome
Old modes provably unaffected (pointwise-equal argument); attribution for any change in old-skill metrics stayed clean through the C4 redo series.
Mechanism
PPO's critic normalizes to the reward scale it sees; an ungated additive term shifts returns in every state and re-scales advantages globally, entangling the new skill with all old ones. Command-gating confines the new term's support to the new mode's state distribution, making "pointwise identical elsewhere" a provable property rather than a hope.
Applies when
- adding a tracking/shaping term for a new command or skill to a lineage that must not regress
- reward change proposed while other skills are still being gated
- reviewing whether a config diff counts as a reward change
“只在 |cmd_vy| > 0.02 时付。不门控的话它对 cmd_vy≡0 的老模式也给 0~2.0 分（健康摇摆 vy≈0.1 → 1.28），等于给整张奖励表加一个大偏置、值函数尺度全变 … 门控后老模式逐点得 0 = 与加项前逐点同值，单变量纪律成立。… 但 side 桶内的值确实变了 —— 这仍是奖励表改版，开级盯 s1c B 臂签名”
train/C_LADDER_RUN.md § 3d. gate_by_cmd=True（重要） A quadratic clearance reward stalled for 3500 iters near target - switch to an indicator on accumulated height
indicator-reward-avoids-gradient-decayWhen a shaped term plateaus near its target, check the gradient profile: replace vanishing-gradient forms with threshold/indicator forms for the final approach, and prefer delta-accumulation over absolute positions to immunize against frame offsets.
Symptom
The quadratic-error clearance term froze at -0.002 from iteration 2000 to 5500 - thousands of iterations with no progress on foot lift.
Context
Diagnosis: a quadratic penalty's gradient vanishes as the error approaches target, so exactly where the last millimeters must be earned the incentive fades to nothing. The replacement (Humanoid-Gym form): accumulate the swing-phase height climb per foot, reward a BINARY indicator |accumulated - target| < 0.01 masked to the planned swing window, reset on contact, weight +1.6 as a positive reward. Two properties: the indicator's incentive is constant until the threshold is crossed (no decay zone), and accumulating height DELTAS makes any constant sole-frame offset cancel automatically - which structurally sidesteps the earlier 0.0585 m zero-point bug ("顺带绕开我先前那个'忘了减 0.0585 导致惩罚恒为 0'的坑").
Change
Clearance reformulated from quadratic penalty on instantaneous height to indicator on per-swing accumulated climb (target 0.03 m by leg-length scaling, weight +1.6).
Outcome
Part of the v5 package under which lift finally moved (v5 29 mm, v6 34 mm vs the stalled 18-24 mm era); the offset-cancellation property removed one whole bug class from the term.
Mechanism
Policy-gradient learning follows the reward's local slope; quadratic shaping concentrates slope far from target and starves it near target, so convergence stalls precisely at the finish line. An indicator pays a constant bounty until the goal is met; formulating on deltas rather than absolutes removes sensitivity to reference- frame constants.
Applies when
- a reward term's value freezes short of target for thousands of iters
- designing clearance/height/precision terms
- reward code depends on absolute link positions
“现行二次型在接近 target 时梯度趋零 —— 这正是 clearance 从 iter 2000 到 5500 卡在 −0.002 不动的原因。… 二值指示在跨过阈值前梯度恒定,没有衰减区 … 累积 delta 让 SOLE_OFFSET 自动抵消”
train/WALK_V5_SPEC.md § 3. clearance 改峰值型(去掉二次型的梯度衰减) The smoke watcher panicked at the wrong operating point and its score goes blind when gates saturate - treat it as a survival sentinel, not a judge
smoke-watcher-operating-pointConfigure every automated evaluator at the lineage's declared deployment operating point, give it graded metrics that cannot saturate, and until then scope its authority to catastrophe-detection - never let a mis-configured watcher stop or rank a rung on its own.
Symptom
Two watcher misfires in one ladder: (1) during the friction rung the watcher (evaluating at kd 1.0) reported panic-level 1/3 survival from iter 3300 - falsified by the official kd 1.2 scan, because the lineage's design operating point was kd 1.2 and the watcher lacked the --kd-scale passthrough; (2) during the PD rung the watcher's early-stop score froze at iter 1050 despite ongoing drift improvements, because with all eight gates passing (constant 0/3 failures) the score has no gradient left - "八门全过恒 0/3 时 score 对漂移改善盲, s1f 课文三现".
Context
Both are the same category: the in-training smoke loop is an instrument with its own configuration (operating point, score design), and its verdicts are only as aligned as that configuration. The booked doctrine: "冒烟只当存活哨兵" - until the watcher evaluates at the deployment operating point with graded metrics, its role is detecting catastrophes, not ranking checkpoints; ranking belongs to the full battery at the design operating point (and the watcher's scoring was separately patched to weight survival 3x so recoveries during hard phases are not early-stopped away).
Change
Watcher debt booked (--kd-scale passthrough); score saturation acknowledged with graded columns planned; selection authority kept with 20-seed batteries at the declared operating point.
Outcome
A false panic did not abort a rung that was actually passing at its design point; a frozen score did not hide real drift gains; the instrument's authority was scoped to what its configuration can actually see.
Mechanism
An evaluator is itself configured (gain profile, delay, metrics); evaluating a policy away from its design operating point measures a counterfactual robot, and bounded scores saturate once binary gates pass, losing all sensitivity. Instruments need the same operating-point discipline as deployments and graded outputs to retain gradient.
Applies when
- an automated smoke loop contradicts the official battery
- early-stop scores freeze while graded metrics still improve
- lineages with non-default deployment gain/delay profiles
“watcher (kd1.0 口径) 3300 起 1/3 恐慌被 kd1.2 正式扫描证伪为考纲外假象 —— 工作点评测口径教训: watch_ckpt 缺 --kd-scale 透传 (待补), 冒烟只当存活哨兵。… watcher score 饱和误停 @1050(八门全过恒 0/3 时 score 对漂移改善盲, s1f 课文三现)”
train/README.md § omni_s2e_fric (watcher 恐慌被证伪) / omni_s2e_pd (500 臂) The median of a bimodal metric lands in the empty gap - check the distribution, and never judge swing on 3 seeds
median-hides-bimodal-distributionBefore quoting a median or mean, look at the distribution; report suspected-bimodal metrics as mode share plus per-mode ranges, use small-seed smoke runs only to screen trends, and size the seed count for decisions by the share resolution you need (here: 20).
Symptom
Years of "high swing variance" and undecidable 3-seed swing readings turned out to be one fact: the metric was bimodal all along - "历代 swing 高方差与 3-seed 不可判由此定性:一直在测双稳态系统" - and every median reported from it (e.g. 12.1 mm) described a value no seed ever produced.
Context
Concrete instances: s2e_pd-1400's 20 seeds split 2.6-4.9 mm vs 19.3-24.0 mm with zero seeds between; s1e-500 read 3.9 mm on seeds 0-2 but 23.1 mm median over 20 seeds; a 3-seed reading of 19.3 was logged as "double-peak optimism, lesson recurrence #4". The selection re-audit codified the sampling rule: "3-seed 的 swing 读数不可判点, 只能筛带,选点必须 20-seed" - 3 seeds may screen a band, only 20 seeds may pick a point.
Change
Swing (and any suspected multi-modal metric) reported as mode shares plus per-mode ranges instead of a bare median; 3-seed smoke numbers demoted to band-screening; all shipping/selection decisions moved to 20-seed batteries.
Outcome
The "swing debt" bookkeeping was reinterpreted as basin probability (see swing-bistability-damping-switch), and checkpoint selection stopped being whipsawed by which basin the first three seeds happened to fall into.
Mechanism
Central-tendency statistics presuppose unimodality; on a bimodal distribution the median tracks the mode SHARE, not any achievable behavior, and small samples alias the share entirely. Mode-aware reporting (share + per-mode stats) is the only faithful summary, and the needed sample size is set by the share resolution required.
Applies when
- a quality metric shows chronic high variance across seeds
- 3-seed smoke readings contradict 20-seed batteries
- reporting swing height, clearance, or any basin-prone metric
“中位数落在空档里,「swing 债 −11mm」实为「50% 概率掉进拖地吸引子」。历代 swing 高方差与 3-seed 不可判由此定性:一直在测双稳态系统。… swing 跨 seed 双峰 (500 在 seed0~2 只读 3.9mm, 20-seed 中位 23.1) —— 3-seed 的 swing 读数不可判点, 只能筛带, 选点必须 20-seed。”
train/README.md § swing 双稳态定性 / s1e 选点重审 The walk phase machine structurally cannot express flight - rebuild the representation for duty < 0.5, teach flight with a mask tax, never a cliff bounty
phase-machine-structural-limitsWhen a new gait changes the contact pattern's structure, audit whether the phase/mask representation can express it and rebuild the representation if not; teach the new contact pattern with graded mask-mismatch pressure and count it in acceptance with artifact-proof definitions (minimum segment length), never with cliff bounties.
Symptom
Running requires both feet airborne, but the walk-era phase machine switches legs by the sign of sin(phase) - with duty < 0.5 the two swing windows must OVERLAP during flight, which a sign-switching representation cannot express at all.
Context
The run phase machine was re-architected rather than patched: per-leg phases (left = phi, right = phi+0.5 mod 1) with leg_phase < duty defining stance, aligned to the walk sin convention at duty=0.5 so the machines agree where their domains overlap. Flight is taught by the SAME mechanism that once cured foot-dragging, direction reversed: in the two planned flight windows the contact mask is (0,0) and feet_contact_number_duty charges -0.3 per foot still on the ground - a mild ~0.16/step tax, deliberately NOT a cliff: "悬崖式腾空奖励诱发跳跃 hack,v4-clearance 家族老课文". The reference shape (half-sine bump over swing progress) is zero at window boundaries by construction, eliminating the clearing-window that the C4 probe measured to cost 13-19% on non-sinusoidal references. The shape self-check ("零代价选项是什么") was run on three behaviors: standing pays ref everywhere (known cmd=0 stepping risk, booked), walking pays only the flight-window tax, proper running collects full marks.
Change
New rewards.py run section (leg_phase_duty / stance_mask_duty / ref_run / clearance_run / contact_duty) with the walk versions untouched byte-for-byte; flight acceptance metric defined with a segment-length floor (>=40 ms to count) so numeric contact flicker cannot fake flight.
Outcome
Flight became expressible and taught by a calibrated mild pressure; the walk lineage's phase code stayed frozen as its own contract.
Mechanism
A phase representation defines which contact patterns exist in the reward's vocabulary; duty cycling below 0.5 introduces states (double-flight) outside a half-period sign convention's language, so no weight tuning can teach them. And rare desirable events taught by cliff-shaped bounties invite hacks (jumping in place); the graded mask tax prices the planned pattern without creating a jackpot.
Applies when
- extending a walking stack to running/jumping (duty < 0.5)
- a desired contact pattern never appears despite reward increases
- defining flight/contact acceptance metrics
“duty<0.5 时摆动窗 (1−duty)T > T/2,两腿摆动窗在腾空段重叠 —— sin 符号切腿的机制结构上表达不了"双脚同时在空中"。… feet_contact_number_duty 对"还踩着地"持续 −0.3/脚 —— 与 walk 治拖地同一机制,方向相反。不腾空的税 ~0.16/步 … 梯度温和不构成悬崖(悬崖式腾空奖励诱发跳跃 hack,v4-clearance 家族老课文)。”
train/RUN_V0_SPEC.md § 5. 相位机设计 Calibrate a wall penalty by measuring healthy and sick policies - healthy pays ~0, the disease pays a wall
calibrate-threshold-between-healthy-and-sickCalibrate every threshold penalty by evaluating its exact formula on replays of at least one healthy and one sick policy: place the threshold between their distributions, size the weight so the sick policy pays a decisive fraction of tracking while the healthy one pays ~0, and pre-compute neighboring thresholds for cheap adjustment.
Symptom
Real walk_v7 occasionally clipped its own legs (stance narrowed to 133 mm mean vs nominal 214.5); a foot-distance penalty was needed, but an uncalibrated threshold/weight risked either doing nothing or becoming a reverse barrier.
Context
The term (relu(d_min - lateral foot distance), measured in the base yaw frame because world-frame y is meaningless after turning) was calibrated before training by replaying three known policies through the exact reward formula at the acceptance operating point: healthy v5 (183 mm) pays 0.6% of tracking - effectively free; narrowed v7 (133 mm) pays 22% - effective widening pressure; collapsed v8 (93 mm) pays 55% - a wall. d_min 0.16 was placed deliberately between healthy and sick, with alternative thresholds (0.14/0.18) pre-computed in the tool output for later adjustment. The shape self-check was named as a standing question: "先问'零代价的选项是什么'" - the zero-cost region must be exactly the desired behavior.
Change
feet_lateral_distance added at d_min 0.16 / weight -10, with telemetry expectation pre-registered (should decay toward 0 as stance learns >160 mm; if bow-legged over-widening >214 appears, only then discuss an upper bound).
Outcome
v9_probe on hardware: no leg contact ("没碰腿(N2 兑现)"), stance min 145/126 mm green; the term's zero-cost design left healthy gait untaxed.
Mechanism
A relu threshold penalty defines a free region and a priced region; its correctness is entirely in where the boundary sits relative to the healthy and pathological distributions. Replaying known-good and known-bad policies through the exact formula measures both distributions in the term's own currency, making the threshold and weight a placement decision instead of a guess.
Applies when
- adding any relu/threshold-style wall penalty
- a safety margin (foot distance, joint limit, clearance) needs enforcement without taxing normal behavior
- choosing between candidate thresholds for a new term
“形状自检 (v6 横向组/v8-B 的教训 —— 先问"零代价的选项是什么"): 标称站距付 0, 健康步态付 ~0, 收窄才付费 … v5(健康) 183 mm … 0.6% ≈ 免费 | v7(收窄) 133 mm … 22% —— 有效推宽 | v8(塌陷) 93 mm … 55% —— 墙 … d_min=0.16 恰在 v5(183)与 v7(133)之间”
train/WALK_V9_SPEC.md § 2. N2 —— 脚距惩罚(已完成权重预标定) An outer heading P-loop at deploy cut drift 10x because its output stays inside the trained command band - then training was aligned to it
deploy-heading-loop-and-align-trainingFix drift-class problems first with an outer loop whose output provably stays inside the trained command band; when adopting it permanently, align the training command generator to the deployment's actual command mixture (feedback-driven AND constant), matching law, gain, and clip exactly.
Symptom
Persistent heading drift on straight-line walking (v6 net yaw 60.3 deg over 15 s) that reward-side fixes had only partially tamed.
Context
The deploy stack added --heading: an external P loop wz = clip(0.5 * wrap_to_pi(theta0 - theta), +/-0.6), recomputed each frame and fed into the policy's ordinary wz command slot. Measured: net yaw walk_v6 60.3 -> 5.8 deg, walk_v5 17.4 -> 4.2 deg. A run-level audit later corrected the mechanism story: training had heading_command=False since v1 - the policy had NEVER seen heading-error feedback, so the loop works purely because its output lands inside the trained command distribution wz ~ U(+/-0.6): "收益真实,当时的机理解释写错了" (the benefit is real; the mechanism explanation had been wrong). v8 then closed the loop properly: training-side heading command enabled with rel_heading_envs=0.5 - half the envs get heading-error-driven wz, half get explicit constant wz, because deployment feeds wz BOTH ways (straight-line = heading feedback, turning = constant command) and rel=1.0 would have made constant-wz turning out-of-distribution. The law, gain, and clip were aligned item-by-item between trainer and deploy tool.
Change
Deploy-side outer loop first (no retrain needed); then v8-D enabled the matching training-side heading command at rel=0.5 with identical gain (0.5) and clip (+/-0.6), contract unchanged (wz slot carries the computed value).
Outcome
Drift handled at deploy (5.8 deg) generations before training caught up; the alignment removed the residual train/deploy distribution mismatch, with the accepted cost booked (open-loop straight walking becomes more OOD for heading-envs - irrelevant since acceptance and deployment always run the loop).
Mechanism
A learned velocity-tracking policy is a valid inner loop for any outer controller whose commands stay within the trained command distribution - the policy needs no knowledge of the outer objective. Full alignment then requires training on the same mixture of command sources the deployment actually uses, in the observed proportions.
Applies when
- heading/position drift on a velocity-tracking policy
- designing outer loops over learned locomotion controllers
- training command distribution differs from how deployment feeds commands
“审计更正(2026-08-02,run 级 env.yaml):训练侧自 v1 复盘起就是 heading_command=False … 策略从未见过航向误差反馈。--heading 是评估/部署侧外加的航向 P 环(wz=clip(0.5·err,±0.6), 落在训练分布 wz~U(±0.6) 内)。实测净偏航 walk_v6 60.3° → 5.8° … 收益真实,当时的机理解释写错了”
train/WALK_V7_SPEC.md § 0. 本轮之前已经改掉 (航向闭环, 含审计更正) Teleop fed the sidewalk axis a command beyond its training band - feet clipped; give each axis its own speed setting
teleop-command-band-per-axisGive every command axis its own teleop scale, clamped to that axis's training band, and reproduce any hardware incident in sim with the exact deployed command values before touching training.
Symptom
Robot stepped on its own foot when sidewalking left under teleop - and only when going left.
Context
The teleop tool used one speed setting for all axes: --teleop-speed 0.20 applied to A/D sent cmd_vy = 0.20, above the training band's top (0.08-0.18) where foot-spacing margin is thinnest. Sim reproduction of the incident (product policy, pw0.8, 5 seeds x 20 s, true collision threshold = single foot width 104 mm): at vy 0.20 the minimum foot distance was 111-115 mm - 7-11 mm from self-collision - vs 147 mm at vy 0.10. Left was 4x more dangerous than right (25% vs 6% of time inside the 160 mm soft wall at vy 0.10), matching the left-only symptom; the margin did not degrade over time (pressing more just lengthened exposure).
Change
deploy_policy gained --teleop-side (default 0.10), separating the lateral speed from the forward speed so each axis's teleop command sits inside its own trained band.
Outcome
Command now inside the band with 43 mm margin at default; the incident became a quantified, reproduced, closed account rather than a mystery.
Mechanism
The policy's competence envelope is the training command distribution per axis; teleop mappings that share one scalar across axes silently command out-of-band inputs on the weakest axis. Asymmetric risk (left vs right) came from the policy's own chirality bias, so a symmetric command produced an asymmetric hazard.
Applies when
- wiring a joystick/teleop layer over a learned policy
- a hardware incident occurs on one command direction only
- training bands differ across command axes
“A/D 一直与 W/S 共用速度档，所以按 A 下发的是 vy = 0.20 —— 既超训练带（0.08~0.18）上沿 … 0.20（遥控实际值）| 111~115 mm | 7~11 mm … 且左比右危险 4 倍 … 处置：deploy_policy 新增 --teleop-side（默认 0.10），侧移与前进档分开。”
train/C_LADDER_RUN.md § 3p. 一 向左走踩到自己 → --teleop-speed 0.20 同时喂给了 vy An exponential kernel on instantaneous velocity punishes gait oscillation - track the cycle average
cycle-average-tracking-for-gait-quantitiesReward velocity tracking on gait-cycle averages (or filtered values), not instantaneous samples, whenever the desired behavior oscillates at stride frequency; widening the kernel does not fix a variance penalty.
Symptom
Even while the robot genuinely sidewalked (verified after the metric fix), the Isaac-side tracking reward sat on the ignore-floor: true sidewalk scored 0.178 vs 0.189 for ignoring the command - the reward was mildly punishing the desired behavior.
Context
Sidewalking is inherently oscillatory: per-frame vy std was 0.177 while the tracking kernel width was sigma = 0.15, applied to the instantaneous value. A kernel-width scan showed widening sigma 0.15 -> 0.50 still loses (-0.14 -> -0.07): "指数核惩罚的是方差，而侧步天生带方差" (the exponential kernel penalizes variance, and side-stepping inherently carries variance). Modeling with measured parameters: replacing instantaneous vy with the mean over one gait cycle (0.5 s) flips the margin decisively (true sidewalk 1.888 vs ignore 1.281, +0.607), half-cycle is neutral (+0.006), two cycles adds nothing more. Explicitly flagged as extrapolation pending Isaac-side implementation. This also vindicated a previously dismissed external note (sigma too small) - right conclusion, different mechanism than claimed (variance, not gradient).
Change
Proposed fix recorded: change the tracked quantity from instantaneous vy to a one-gait-cycle running average; widening sigma alone rejected by the scan.
Outcome
Diagnosis complete and quantified; the C4 product shipped via feed-forward before the reward change was implemented, so the cycle-average fix remained a verified-by-model, not-yet-trained change.
Mechanism
E[exp(-(v-c)^2/sigma^2)] decreases with Var(v) even when E[v] = c exactly; a gait's phase-locked oscillation guarantees variance at the stride frequency, so instantaneous tracking rewards structurally prefer standing still at the command mean. Averaging over exactly one cycle removes stride-frequency variance while preserving command-following error.
Conflicts
The cycle-average fix itself is model-extrapolated ("⚠️ 这一条是外推，须在 Isaac 侧实装并复量后才能当结论") - the diagnosis is measured, the remedy untested in training at the time of writing.
Applies when
- tracking rewards for lateral/turn/any oscillation-carrying velocity
- a verified behavior scores below the ignore-floor
- choosing sigma for exp-kernel tracking terms
“侧走时 vy 的逐帧摆幅 std = 0.177，而 track_lin_vel_y_exp 核宽 σ = 0.15，且作用在瞬时值上 … 真侧走（均值 66%，振荡 ±0.18）0.178 | 完全无视指令 0.189 … 真侧走的得分比无视指令还低。… σ 从 0.15 放到 0.50，侧走仍然吃亏 … 把跟踪目标从瞬时 vy 换成一个步态周期（0.5 s）的平均 vy：… 1.888 vs 1.281”
train/C_LADDER_RUN.md § 3n. 二/三 Isaac 训练奖励为何一直坐在「无视底分」/ 修法不是放宽 σ Knee swing collapsed because it directly trades against the slip penalty - price the conflict explicitly and clamp what reward cannot hold
knee-swing-vs-slip-pricingWhen a behavior collapses as another metric improves, look for the term pair trading them and set their price ratio deliberately (with escalation and reverse tripwires pre-registered); where the policy actively spends action budget to undo your target, stop paying more reward and clamp the target space structurally.
Symptom
Knee peak-to-peak swing collapsed across generations - v5 33 deg, v10 26-30, v10b 7-8, v11 6.5-8.6 - and rolling the clock back did not recover it, acquitting the clock; the collapse tracked the gated slip penalty instead: "屈膝与不打滑在当前奖励里直接对抗" - v10b's excellent 93 deg slip was purchased with knee amplitude.
Context
Reward-side flexion fixes had failed three times: raising reference amplitude backfired twice (v9/v11), and v11's deep-squat default was actively fought by the policy - it spent 0.68 of action budget pulling the squat straight ("被策略花 0.68 动作拉直反杀"). v12's design accepted the conflict as real and attacked on two tracks: (1) ECONOMICS - a direct knee_swing_amplitude reward (+0.3, target 0.55 rad, capped at 0.6/step = 55% of tracking), explicitly opposed to the slip penalty by design ("显式对立——这正是设计:v12 就是这场对抗的定价实验"), with an escalation ladder (K +0.3 -> +0.5, then slip -0.5 -> -0.3, one layer at a time) and a reverse tripwire (slip telemetry back at v10 levels -> slip weight to -0.8, accept ~20 deg knee compromise); (2) STRUCTURE - knee target bounds [0.2, 0.9] rad so full straightening is physically impossible (straightest 11.5 deg) and the 0.68 fighting budget is released. A bonus falsifiable prediction was attached: phase-lock strength tracks amplitude (v9_probe 48 deg locked 2.5 Hz; v11 low-amplitude 1.36 Hz unlocked), so if K works, hardware phase-lock should return - one change, two verdicts.
Change
knee_swing_amplitude reward + knee target clamp + pre-registered escalation/reverse levers; the failed reward-side-only approach retired.
Outcome
The lineage was frozen before v12 trained (strategic reset), but the diagnosis stands as the walk line's clearest example of two reward terms trading a behavior between them, with the pricing experiment and structural clamp fully designed and calibrated.
Mechanism
When two terms price opposite aspects of one motion (swing amplitude creates yaw momentum that becomes slip), the optimizer settles wherever the price ratio puts it - patching one side moves the equilibrium, not the conflict. Explicit pricing makes the trade a designed quantity; structural clamps remove the regions where the policy spends budget fighting the designer.
Conflicts
The pricing experiment (K vs slip) was designed and calibrated but never trained - the 2026-08-05 reset suspended v12; the collapse attribution table and the 0.68-action counterattack are measured, the remedy's效果 is untested.
Applies when
- one gait quality degrades in lockstep with another's improvement
- the policy visibly fights a default pose or reference
- repeated reward-side fixes for the same behavior have failed
“膝摆塌在 v10→v10b,头号嫌疑是门控滑移罚(四代实测膝 p2p:v5 33° / v10 26~30° / v10b 7~8° / v11 6.5~8.6°;退时钟没救回 → 非时钟)——"屈膝"与"不打滑"在当前奖励里直接对抗 … 奖励侧修屈膝已三败 … v11 深蹲 default 被策略花 0.68 动作拉直反杀”
train/WALK_V12_SPEC.md § 0. 定位 / 2. K —— 膝摆经济(与滑移罚的对偶) Brief the operator on the lineage's measured zero-command and untrained-axis behavior before handing over the joystick
know-zero-command-behaviorBefore any teleop/demo, measure and write down the policy's zero-command behavior and per-axis competence, label untrained axes explicitly as not-bugs, and set the floor/procedure to accommodate the known drift.
Symptom
A teleop session was about to start on a policy that does not stand still at zero command and has never been trained on lateral commands - behaviors an unbriefed operator would report as bugs or emergencies.
Context
Three measured facts were written into the teleop instructions ("都有 实测依据, 不是猜"): (1) A/D (lateral) keys will get essentially no response - probe-measured sidewalk tracking ~3%, an untrained axis: "这正是 C4 要解决的事, 不是 bug"; (2) no keypress = cmd 0, and this lineage does not stand still at zero command - a three-generation lineage property: paces in place, drifts right ~5 cm/s, net rotation -30 deg/20 s; sim survival is 20/20 (it will not fall) but it walks away slowly, so leave floor margin especially on the right; (3) S (backward) WILL respond - probe-measured 20/20 survival, 67% tracking untrained, which is also why this root was chosen for the C ladder. Plus a keybinding dry-run while suspended before touching down.
Change
Operator briefing became part of the deployment artifact: expected response per key, expected idle behavior with magnitudes and directions, and the distinction between untrained (expected, not a bug) and abnormal.
Outcome
The session proceeded with correct interpretations available in advance; the known zero-command wander was handled by floor margin and start-with-command procedure rather than misdiagnosed on the spot.
Mechanism
A learned policy's off-nominal behaviors (idle drift, untrained axes) are lineage properties, stable and measurable in sim beforehand; operator surprise converts known properties into false incident reports and unsafe reactions. A briefing transfers the measured behavior model to the person holding the controller.
Applies when
- handing a learned policy to an operator or demo audience
- the policy idles in a non-stationary way at zero command
- some command axes are untrained in the current lineage
“A/D 基本不会有反应 —— s1e 从未训过非零 vy, 选根探针实测侧走跟踪率 ~3% … 这正是 C4 要解决的事, 不是 bug。… 不按键 = cmd 0, 而 s1e 在零指令下不站定 —— 血统属性, 三代实录: 原地踏步 + 右漂 ~5 cm/s + 净旋 −30°/20s。”
train/REAL_RUN_S2.md § 附: WSAD 遥控 上机前必须知道的三条 Borrow reward values from other robots as ratios (to tracking weight, to leg length) - never as absolute numbers
transfer-ratios-not-absolutesWhen importing any numeric from another robot's config or paper, identify its natural normalizer (tracking weight, leg length, sqrt(g*L), body mass) and transfer the dimensionless ratio; sanity-check against a same-scale robot when one exists.
Symptom
Published configs offered tempting absolute values (swing height target 0.06-0.08 m, weight -20) that would have been wrong for a robot with half the leg length and a different tracking weight.
Context
The cross-check normalized before transferring: G1's feet_swing_height weight -20 against tracking +1.0 is a 20x ratio, so with local tracking at 1.5 the equivalent is -30, not -20. G1's 0.06 m target on a ~0.70 m leg scales to ~28 mm on the local 0.325 m leg (Humanoid-Gym converts to ~23 mm), confirming the locally chosen 0.03 m and explicitly rejecting copying 0.05-0.08 absolutes. A same-class robot (Menlo's 16 kg) was used to sanity-check feet_air_time (+0.5 vs the local 2.0, flagged over-high). A nondimensional check of the same kind later validated the sidewalk speed target (v/sqrt(gL) = 0.081 vs hardware-verified 0.101 - 0.104 - inside the envelope, conservative).
Change
All borrowed values converted through ratios (weight/tracking-weight, height/leg-length, dimensionless speed) before entering the config.
Outcome
The scaled values worked (0.03 m target matched both the scaling law and measured 22-23 mm baseline); no cross-robot absolute was ever copied raw.
Mechanism
Reward economies are scale-relative (only ratios to the tracking term matter to the optimum) and kinematic quantities are morphology-relative (clearance scales with leg length, speed with sqrt(g*L)); absolutes encode the source robot's scale, ratios encode the design intent.
Applies when
- copying reward weights/targets from open-source configs or papers
- setting clearance heights, speed targets, or impact thresholds
- comparing your weights to published tables
“G1 的 feet_swing_height 是 tracking 的 20 倍(−20 vs +1.0)。我们 tracking 是 1.5,按同比例应为 −30 … G1 目标 0.06 m / 腿长 ~0.70 m,换算到我们 0.325 m 腿长约 28 mm;Humanoid-Gym 换算约 23 mm。故 target 取 0.03 m 是对的 … 不必抄 0.05~0.08 的绝对值。”
train/WALK_DIAGNOSIS.md § 修正 ②(权重放大) / 修正 ③(目标高度按腿长缩放) Score left and right separately - averages hide chirality breaking that mirror augmentation does not prevent
chirality-scored-separatelyReport every mirrored skill as two numbers with an explicit gap budget; never accept an average, and never assume augmentation guarantees symmetry - measure it per lineage and treat breakage as hard to reverse.
Symptom
Policies developed quantified left/right asymmetry (e.g. C2-700 turned right at 82% but left at 67% - a 15 pp gap; push tolerance 40/40 symmetric on the root vs 17/40 on a deep-trained descendant), and averaged metrics would have reported healthy midpoints.
Context
The repo had policy-level symmetry-breaking evidence strong enough to make separate scoring a battery rule: "左右必须分开打分 … 平均 vy 跟踪会把它掩盖". Notably, chirality broke and never recovered even though mirror augmentation (command-level mirror_prob 0.5) was on the whole time - augmentation reduced but did not prevent asymmetry, and once broken it stayed broken through subsequent rungs. PASS conditions therefore carried explicit symmetry budgets (left/right tracking gap <=10 pp), and sim's predicted asymmetry (700: right faster than left) was flagged for direct real-robot timing confirmation.
Change
Battery rule: every directional skill reports left and right (CW/CCW) as separate rows with a max-gap budget; mirror augmentation treated as mitigation, not proof of symmetry.
Outcome
The 700-vs-A800 asymmetry gap (15 pp vs 7 pp) became a first-class selection criterion; C4 product shipped with a measured 5 pp gap.
Mechanism
Averaging over mirrored conditions cancels antisymmetric error exactly where it matters; and symmetry lost during training is a lineage injury (like plasticity loss) that later rungs do not spontaneously heal, so it must be gated, not assumed.
Applies when
- evaluating turn/sidewalk/push-recovery or any mirrored skill
- relying on mirror/symmetry augmentation
- selecting between checkpoints with similar average scores
“左右必须分开打分（left/right lateral、CW/CCW turn 各自一行）—— 本仓已有 policy-level symmetry breaking 的量化证据，平均 vy 跟踪会把它掩盖。”
train/C_LADDER_RUN.md § 5. 固定验收矩阵 (左右分开打分) Sort external training advice into adopt / already-have / modify / would-trap by recomputing it on your own config
external-advice-audit-against-own-arithmeticNever apply external tuning advice directly: recompute each claim on your own reward table and probe data, classify it adopt / have / modify / trap, and record why - and verify external citations actually exist.
Symptom
External AI/literature advice for the omni ladder arrived plausible-sounding but was written without knowledge of this robot's actual reward table, contract, and history; following it blindly would have broken single-variable discipline and, in one case, made sidewalk unlearnable.
Context
Before the C ladder, every external suggestion was audited: 3 adopted (ellipsoid command sampling; staged wz bands; command-switch acceptance), 3 already present (unified reward; frame history - the frozen 168-dim 5-frame window; per-100-iter acceptance), 2 modified (stand share kept at 20% to avoid a second variable; back share NOT raised because the probe showed backward works untrained 20/20@67%, so oversampling would only crowd out forward), and 1 flagged as a trap: "start vy very small (0.06-0.15)" - on THIS reward table vy was only an L2 tax, so ignoring a vy=0.06 command costs 0.4% of the vx tracking scale, 28-180x cheaper than ignoring forward, with quadratic shrinkage making small commands weaker still. A separate retrieval-reliability note: two search agents returned fabricated verbatim quotes from arXiv PDFs (2 papers, verified fake and discarded); only HTML/abstract/source-verifiable material was used.
Change
Advice classified only after recomputing each claim with local numbers; the "start small" trap was replaced by adding a gated lateral tracking term (the ladder's only true reward surgery) instead of shrinking the command.
Outcome
The adopted items (ellipsoid modes, staged wz, transition acceptance) entered the ladder; the trap was avoided; one external factual error (calling s1g the mainline start - it was falsified 0/20) was caught. Later, one initially-dismissed item (sigma=0.15 too narrow) turned out right for a different reason than claimed - see cycle-average-tracking-for-gait-quantities.
Mechanism
External advice encodes the advisor's reward table and robot, not yours; the transfer-validity test is whether the claim survives recomputation under your own arithmetic (reward margins, probe baselines, contract freeze). Items that survive become experiments; items that don't become documented traps.
Applies when
- incorporating LLM or literature advice into a training plan
- advice conflicts with locally measured baselines
- an external claim depends on reward-table details the advisor cannot know
“其建议 C4「先很小，vy = ±0.06~0.15」—— 在我们这张奖励表下会让侧走学不起来 … 忽略侧走比忽略前进便宜 28~180 倍，且指令越小激励越弱（平方缩放）—— "先很小"在稳定性上对、在梯度上正好把信号缩没了。”
train/C_LADDER_RUN.md § 1. 外部 AI 训练建议的评估（采纳 / 已有 / 要改 / 会踩坑） One fixed acceptance matrix for every rung - new skill must PASS while every old skill stays within a regression budget
fixed-acceptance-matrix-per-rungFreeze one acceptance matrix for the whole ladder; every promotion requires the new skill's PASS plus bounded regression on every prior skill, measured against the parent's baseline under the current (bug- fixed) metric code - and include command transitions, not just steady states.
Symptom
Sequential skill training silently trades old skills for new ones (C1 trained away the root's backward ability); without a constant measurement frame, each rung's numbers are incomparable and regressions hide.
Context
The C ladder ran the same 13-cell command matrix at 20 seeds per cell at every rung (stand; vx +0.15/+0.30; vx -0.10/-0.20; vy +/-0.10; wz +/-0.20; two vx&wz combos; two vx&vy combos), with promotion requiring "新技能 PASS 且旧技能不明显退化" - old-skill regression budget <=2/20 against the parent's recorded 20-seed baseline. For the transition-rich final rung, a command-switch block was added (forward->stop, stop->backward, forward->turn, left->right, turn->forward; survival + re-track within 2 s) because "每个 steady command 都会做 ≠ 命令切换不会摔" - steady-state success does not imply switch safety, and the joystick does switches. The C4 product's gate ran 260 cells (13 x 20) all 20/20.
Change
Battery frozen once, reused verbatim per rung; baselines re-measured per parent (and re-measured again after the metric-frame fix, since old baselines were taken with the buggy coordinate reading - "旧基线是坏坐标系的， 不可引用").
Outcome
Regressions were caught at the rung that caused them (C1's backward loss, C2's vx+0.30 decay), and cross-rung comparisons stayed valid for the ladder's whole life.
Mechanism
A constant matrix makes every rung's output a point in the same metric space, so "did we lose anything" is a table diff, not a judgment call; the per-skill regression budget converts previously earned PASSes into standing constraints on all future training.
Applies when
- designing gates for sequential skill addition
- promoting a checkpoint to be the next rung's root
- after any evaluation-code fix (old baselines must be re-measured)
“新技能 PASS 且旧技能不明显退化才晋级。… C5 追加：命令切换验收（steady ≠ transition） forward→stop、stop→backward、forward→turn、left→right、turn→forward，各 20 seed，判存活 + 切换后 2 s 内是否重新跟上。”
train/C_LADDER_RUN.md § 5. 固定验收矩阵（每级跑同一张，每项 20 seed） Measure yaw rate by integrating heading, not by averaging body-frame angular velocity - the two differed 15x
heading-integral-not-body-rateFor any secular rate (turn gain, drift), integrate the world-frame angle over the window; never average instantaneous body-frame rates during oscillatory motion - and when code comments warn about a measurement, believe them before re-measuring.
Symptom
Two measurements of the same turn gain disagreed by a factor of ~15: time-averaged body-frame omega_z gave -0.05 while the sim2sim harness's heading-angle integration gave +0.473.
Context
The harness code comment had already documented and predicted the failure: during gait the torso oscillates (body-frame omega_z std up to 0.7); projecting world angular velocity onto a swaying body axis and then averaging biases the estimate systematically - "实测体系均值 −0.04 而实际在以 +0.15 转" (measured body-frame mean -0.04 while actually turning at +0.15). The author's own -0.05 measurement was declared void and the training machine's 1.58/2.45 turn gains confirmed valid.
Change
Measurement doctrine fixed: yaw rate for evaluation = net heading change by integration over the window; instantaneous body-frame rates are unusable for averaged directional statistics during legged gait.
Outcome
Subsequent friction sweeps and turn-gain accounting were all conducted in the heading-integral currency, making cross-simulator comparisons (MuJoCo vs Isaac 1.04/1.02) meaningful.
Mechanism
Averaging a vector quantity expressed in an oscillating frame couples the frame's oscillation into the mean (a rectification bias); the heading integral is computed in the world frame where the gait oscillation integrates to ~zero, leaving the secular component.
Applies when
- measuring turn gain, heading drift, or any secular angular rate
- a body-frame-averaged statistic disagrees with trajectory-level truth
- writing evaluation code for oscillating platforms
“我用体坐标系 ωz 的时间均值测,得 −0.05;sim2sim 用航向角积分,得 +0.473。差 15 倍。… 步态中躯干摇晃(体系 ωz std 可达 0.7),把世界角速度投到摇摆的体轴上再取均值会系统性偏掉 … 结论:偏航率必须用航向积分,体系瞬时角速度取均值不可用。”
train/WALK_DIAGNOSIS.md § ③ 转向增益 —— 我的测法是错的,训练机的 1.58/2.45 成立