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
64 cards matching “multiseed-sign-test-for-drift”.
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 nonzero response with the same sign for + and - commands is bias, not ability
same-sign-response-is-yaw-biasBefore crediting any directional skill, test both command signs: response must flip sign with the command; a same-signed pair is a bias to subtract, not an ability to report.
Symptom
Root-selection probe showed nonzero wz "tracking percentages" on turn commands, tempting the read that candidates could partially turn.
Context
During C-ladder root selection, s1e-500's measured yaw rate was +0.084 rad/s for cmd +0.3 and +0.093 rad/s for cmd -0.3 - same sign both ways. The same check on the C2 baseline gave wz+0.20 -> -0.13 and wz-0.20 -> +0.12 (again same sign), while the alternative root s2e_pd-1400 gave +0.16 / -0.16 - opposite signs, i.e. a genuine 16% command response.
Change
Reading corrected and written into the execution sheet: percentages on directional commands are meaningless unless the +cmd and -cmd responses have opposite signs; all three candidates were re-classified as "cannot turn, cannot sidewalk - C2/C3/C4 learn from zero". Acceptance criteria thereafter required "tracking >=50% AND left/right opposite-signed".
Outcome
Prevented crediting turn/sidewalk ability that did not exist; the antisymmetry clause became a standing part of every turn and sidewalk PASS condition (C2, C4, C4-redo levels all carry "且左右反号").
Mechanism
A constant yaw (or lateral) bias projects onto any command's sign convention and shows up as fake fractional tracking; only sign-antisymmetry under command reversal distinguishes a feedback response to the command from an open-loop offset.
Applies when
- evaluating turn/sidewalk/any signed-command tracking percentages
- a candidate shows partial tracking on an axis it was never trained on
- writing PASS criteria for a new directional skill
“C2/C3 那些非零的 wz 百分比不是转向能力 —— 转向+ 与 转向− 的实测同号（s1e：cmd +0.3 → +0.084，cmd −0.3 → +0.093 rad/s），那是恒定偏航偏置。… 三个候选都不会转、都不会侧走。”
train/C_LADDER_RUN.md § 0. 读数纠正（重要，别引错） 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) A 2-degree joint-zero calibration fix moved the whole runnable envelope - re-test old "cannot run" verdicts after recalibration
zero-offset-calibration-shifts-envelopeDate every hardware verdict with the calibration state; after any zero/mount recalibration, re-test previously condemned policy-power combinations and previously "unexplainable" posture offsets before attributing either to training or model.
Symptom
s1d was on record as "only runs at power 0.7" (kicked wildly at 0.8); after a calibration pass, the same policy ran 12 s at 0.8 with no kicking at all.
Context
The calibration had fixed a 2.08 deg zero offset on r_hip_roll - exactly the constant error source on the dominant joint of the kicking oscillation loop ("恰是乱踢振荡环主导关节的常值误差源"). The three-generation post-calibration hardware sweep also closed a second case: the robot's mysterious "backward lean" disappeared after calibration, and the sim-real posture difference collapsed from opposite-sign 5+ deg to same-sign ~2 deg ("后仰案实质了结") - the lean had been a sensing/zero artifact, not a mass-model error. Booked consequence: if the s1d recovery re-verifies, "真机可跑档整体 上移" - every policy's runnable power envelope shifts up, and downstream lineages' hardware expectations get revised.
Change
Joint-zero and mount calibration promoted from setup chore to a variable that dates hardware verdicts: verdicts about which power/scale levels a policy can run are conditioned on the calibration state they were measured under.
Outcome
One policy rehabilitated at a higher power level; one standing sim-real posture discrepancy closed without touching model or training; a pending re-verification booked rather than asserted.
Mechanism
A constant joint-zero error acts as a persistent disturbance injected at the feedback loop's most-loaded joint; near an oscillation threshold, removing a 2-degree bias is the difference between a stable and an unstable loop. Since the error is additive and machine-side, it shifts every policy's stability envelope simultaneously - which is why verdicts must carry their calibration date.
Conflicts
The s1d rehabilitation awaited one confirming re-run at the time of writing ("待复核一跑坐实") - the offset-as-cause reading is the head suspect, not a closed verdict.
Applies when
- a policy oscillates at a power level others tolerate
- sim and real disagree on a constant posture offset
- deciding whether to re-test old hardware verdicts after maintenance/calibration
“发现①:s1d@0.8 能跑了(旧账「只有 0.7 能跑」)——12s 无乱踢。头号嫌疑 = 标定修正:r_hip_roll offset 修 2.08°,恰是乱踢振荡环主导关节的常值误差源。… 发现②:「后仰」标定后消失 … sim-real 姿态差从反号 5°+ 收敛到同号 2°,后仰案实质了结。”
train/README.md § 真机 @0.8 三代横评(2026-08-07 标定后) When training fails repeatedly, inject the target behavior open-loop - stop tuning rewards for an unverified behavior
open-loop-probe-before-reward-tuningAfter two failed training attempts at a skill, stop training: demonstrate the behavior open-loop on the real plant/sim first, and only resume training once you hold a measured, safe, sign-verified target trajectory.
Symptom
Three sidewalk training rounds failed; hypotheses multiplied (exploration failure / wrong reference waveform / insufficient authority) with no way to pick between them by running more training.
Context
Instead of a fourth reward guess, the team wrote probe_side_ref.py: the candidate reference is injected open-loop on top of a frozen policy's output (bypassing PPO entirely), directly measuring "what happens if the robot literally does this waveform" - separating all three hypotheses in one experiment (5 seeds x 8 s per condition, several waveform families and gains). The probe immediately eliminated the authority hypothesis (full-amplitude execution, 5/5 survival) and localized the problem to the waveform/measurement side. The closing principle was written down after the saga: without a verified target behavior, tuning rewards is "在黑暗里试钥匙" (trying keys in the dark).
Change
Standing method: before opening another training rung on a failing skill, build an open-loop (or task-space) generator of the intended behavior, measure whether the physical system can express it and what it looks like - then train toward a verified, quantified target.
Outcome
The probe chain produced the verified waveform (reversed-sign triangle, half gain), quantified safe amplitude (tilt 8.2 deg at band top, foot distance clear of the wall), exposed the metric bug when probe and training disagreed, and ultimately supplied the feed-forward that made C4 pass in +100 iters.
Mechanism
Training couples exploration, reward design, and feasibility into one opaque outcome; open-loop injection cuts the loop and tests feasibility and waveform alone. A behavior demonstrated open-loop converts the remaining failure into a pure credit-assignment/reward question - and its measured trajectory becomes the reference itself.
Applies when
- repeated training failures on one skill with multiple live hypotheses
- uncertainty whether the platform can physically express the behavior
- a reference trajectory's shape/sign/amplitude is guessed, not measured
“三轮 FAIL 之后不再猜，写 train/probe_side_ref.py 把参考开环注入到策略输出之上（绕过 PPO），直接量「照这个波形做会怎样」，一次分开三个假说：甲 探索 / 乙 波形 / 丙 权限。”
train/C_LADDER_RUN.md § 3f. C4 真因定谳（开环探针） / 3k. 建议的下一步 The hip_roll (l+r) asymmetry scalar predicted real-robot lateral drift - promote validated sim scalars into the gate
hip-roll-sum-predicts-lateral-driftHunt for cheap sim scalars that predict real-robot behaviors, validate them on direction AND ordering across multiple policies, then promote them into the acceptance battery; treat later violations as debt to justify in writing, not noise to ignore.
Symptom
A persistent hip_roll left/right asymmetry row in the sim2sim symmetry table had been dismissed as "calibration or mechanical asymmetry" noise; meanwhile real deployments drifted sideways by policy-dependent amounts.
Context
Forward-kinematics analysis reframed the scalar: both hip_rolls move the feet in +y for positive angle, so a same-signed (l+r) sum IS a lateral translation mode - the scalar is a direct lateral-drift bias estimate. Checked against real deployments: s1e (l+r = -0.0178, smallest magnitude) was the steadiest with least drift; 700 (+0.0253) drifted mildly left; A800 (+0.0267) drifted clearly left with the largest tilt 12.9 deg. Direction correct 3/3, ordering correct 3/3 (the log's heading calls it "四枚四中", four-for-four).
Change
The scalar was promoted into the acceptance battery as a posture-class criterion alongside tilt-max median: "hip_roll 左右不对称 |l+r| 不得比父代大" - doubling as a heat proxy (error ~ torque ~ heating).
Outcome
Used at every later gate; when the C4 product exceeded it by +0.005 rad (~+0.3 deg vs parent), the criterion was not silently waived - it was booked as explicit debt with a mechanism argument (the increment is task-required, far smaller than the sidewalk amplitude +/-2.2 deg) plus a related account (stand saturation 32.4% -> 37.2%).
Mechanism
A policy's static joint-angle bias in a translation-producing mode integrates into real-world drift; sim can measure that bias precisely and cheaply. A sim scalar earns gate status exactly when its predictions are validated against hardware in both direction and ordering - and a validated gate may only be exceeded with a written mechanism-level justification, never silently.
Conflicts
The log's heading says "四枚四中" (4/4) but the evidence table lists three policies and the text says "方向 3/3、排序 3/3"; the fourth instance is not shown in this file.
Applies when
- a real robot drifts or leans in a policy-dependent way
- deciding which sim measurements deserve gate status
- a validated gate criterion is marginally exceeded by a new product
“s1e | −0.0178（绝对值最小）| 微右、最不飘 | 三者中最稳、飘最小 ✓ … A800 | +0.0267 | 左、最飘 | 明显左飘、倾角最大 12.9° ✓ 方向 3/3、排序 3/3。 → 正式纳入验收表（与「倾角 max 中位」并列为姿态类判据）。”
train/C_LADDER_RUN.md § 3e. 顺带：hip_roll 左右不对称 (l+r) 就是横移偏置 —— 四枚四中 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 Run acceptance under measured contact parameters - honest condim/torsional-friction flipped a false PASS into a real-matching FAIL
eval-plant-honesty-contact-paramsPin the evaluation plant's contact model to measured values (contact dimension, torsional/rolling friction, mu) before trusting any gate that involves slip, impact, or drift - a gate can only fail a policy for physics its simulator contains.
Symptom
walk_v5 passed the old acceptance battery yet failed on the real robot (footfall force, drift, kicking) - the evaluation plant was flattering the policy.
Context
The battery was re-run under "honest contact parameters" - condim 4 (adding torsional contact) with measured torsional friction 0.035 - and v5 then FAILED exactly the rows corresponding to its real problems: heading 185 deg (limit 30), support-foot yaw slip 284 deg (limit 80), landing force 1.72x (limit 1.5x), suspended tilt 45.9 deg (limit 10). The slip physics depends on torsional friction, which the default contact model (condim 3) does not even simulate - a slip problem is invisible to an evaluator that cannot represent yaw friction at the foot. Term-sizing measurements for the new rewards were likewise taken under the same honest parameters (cmd 0.45, skipping the 5 s start transient).
Change
Acceptance harness pinned to condim 4 / torsion 0.035 (measured); verdicts issued under defaults declared non-citable for these rows.
Outcome
Sim acceptance verdicts began agreeing with hardware ("现在失败, 与真机一致"); the v6 fixes could be developed and validated against an evaluator that could actually see the disease.
Mechanism
An evaluator is a plant model too: contact dimensionality and friction values decide which failure modes exist in the simulation at all. Evaluating under default contact parameters tests the policy in a world where its real failure is physically impossible, producing structurally false PASSes.
Applies when
- sim acceptance passes policies that fail on hardware
- slip/drift/impact gates run under default simulator contact settings
- setting up a cross-simulator evaluation harness
“accept_v2.py 已加三条判据, walk_v5 在诚实的接触参数下(--condim 4 --torsion 0.035)现在失败, 与真机一致:直行 15s 航向累计 <30° | 185° ✗ … 落脚力峰值 <1.5× 体重 | 1.72× ✗”
train/WALK_V6_MINIMAL.md § 5. 验收 Push-test protocol - positive side first, fragile side spotted, axes aligned in the log, and cross-machine push counts stay qualitative
push-test-chirality-protocolOrder disturbance tests from the robust side to the fragile side with protection scaled to sim-measured asymmetry, align and log frame conventions before testing, and treat cross-domain disturbance counts as qualitative evidence only.
Symptom
Hand-push testing on hardware risked falls on a side sim had already flagged as fragile, and push counts invited apples-to-oranges comparison with sim numbers.
Context
Sim chirality was explicit: descendants were far more fragile in -y (fric-3000@kd1.2: +6 N*s survived 15/20 vs -6 N*s only 3-9/20) while the s1e control was perfectly symmetric (40/40). The protocol therefore: push the positive direction first, keep a spotter for the negative side; before any push, record which real-robot side corresponds to sim's +y in the log ("上机前对一次坐标"); and - citing the chaos lesson ("混沌课文") - real push results are used only as qualitative corroboration, never compared numerically with sim survival counts across machines.
Change
Push testing became a scripted, chirality-aware protocol with frame alignment as a logged precondition and an explicit epistemic limit on cross-domain count comparison.
Outcome
The fragile side was tested with protection informed by sim's quantified asymmetry; logs stayed interpretable because the frame correspondence was recorded before the first push.
Mechanism
Disturbance-response chirality is a real, quantifiable lineage property, so test order should follow measured fragility; and perturbation outcomes are chaotic in the details (divergent trajectories from tiny differences), so counts do not transfer across domains even when qualitative rankings do.
Applies when
- planning push/disturbance tests on hardware
- sim shows directional asymmetry in disturbance survival
- someone proposes comparing real push counts to sim counts
“先正向后负向, 负向留人扶 —— sim 手性明确: 后代在负 y 向显著更脆 (fric-3000 @kd1.2: +6 N·s 15/20 vs −6 N·s 3~9/20), 而 s1e@0.8 两向 40/40 完全对称。上机前对一次坐标 … 跨机不做二值结论 (混沌课文): 真机推力只作定性对照, 不与 sim 计数对比。”
train/REAL_RUN_S2.md § 3. 抗推 (可选, 人手推; 做则按此协议) A suspended (no-load) test acquits or convicts the actuator before you blame authority
suspended-test-isolates-actuator-authorityBefore attributing a failure to actuator authority, measure no-load tracking error and steady-state torque fraction; blame authority only if the task fails while the error grows with demanded force - and then fix gains or targets, not training.
Symptom
hip_roll sagged 0.21 rad on the ground and saturation questions loomed over the sidewalk plan - was the roll axis physically too weak (authority), or was something else limiting it?
Context
Before C4, the roll-authority question was settled by measurement triage: suspended test (--suspend, feet off ground) showed hip_roll tracking error 0.0008 rad - actuator acquitted; the entire 0.21 rad ground sag is load-induced. Steady-state torque was 25% of limit - 75% margin remains. Since sidewalk needs lateral force, not exact angles, authority was ruled "not a hard limit", with a pre-registered criterion for when it WOULD become one: sidewalk fails to track AND roll error keeps growing - then the fix is raising hip_roll kp or lowering the vy target, not more training.
Change
Hypothesis "roll authority insufficient" demoted from blocker to a monitored branch with an explicit trigger condition; C4 proceeded.
Outcome
Later open-loop probes confirmed the actuator could produce the behavior (sidewalk feed-forward ran at full amplitude, 5/5 survival), and the eventual C4 failure causes were measurement and reward, never authority.
Mechanism
Suspended vs loaded comparison separates the actuator's closed-loop competence from the load path: tiny no-load tracking error means the motor/controller is fine and any loaded deviation is statics (gravity / stiffness budget, kp trading error for force). Torque-fraction measurement then bounds how much force headroom actually remains.
Applies when
- suspecting an axis is "too weak" for a new skill
- large position sag on a loaded joint
- deciding between hardware fix, gain change, and more training
“吊挂（--suspend）实测 hip_roll 跟踪误差 0.0008 rad → 执行器无罪，地面下垂 0.21 rad 全是负载所致；稳态占限扭 25% → 仍有 75% 扭矩余量。… 判据：若 C4 出现「侧走跟不动且 roll 误差继续变大」，那才是权限账 … 解法是提 hip_roll 的 kp 或降 vy 目标，不是硬训。”
train/C_LADDER_RUN.md § 3d. roll 权限：已部分澄清，不是硬上限 PPO's Gaussian noise cannot compose phase-locked oscillations - deliver them as feed-forward and let the policy learn the residual
feedforward-for-phase-locked-skillsIf a skill needs a temporally coherent (phase-locked) action component, do not expect step-wise exploration to find it: inject a verified feed-forward and train the policy as a residual stabilizer, keeping the feed-forward inside the deployment contract.
Symptom
Four different reward arrangements (no reference / wrong-sign reference / correct-sign reference / cage released) all failed to elicit sidewalk, while open-loop probes proved the behavior existed and was safe on the same platform with the same policy as base.
Context
Producing lateral velocity requires a phase-locked hip_roll oscillation synchronized to the gait clock. PPO's exploration is per-step, zero-mean, uncorrelated Gaussian noise - it can never compose a sustained phase-locked component, so the behavior is unreachable by exploration regardless of how it is rewarded. The fix changed the delivery channel: target = default + scale*action + lat_ff(cmd_vy, phi). The policy's action becomes a residual on top of the feed-forward, retaining full balance authority (it can even cancel the feed-forward); the feed-forward supplies exactly the component exploration cannot. This mirrors why the sagittal joint_pos_ref worked (it also delivered phase structure), just via a different channel.
Change
Contract-level change, done cleanly: new profile omni_ff (= omni + lat_ff_gain -0.5), existing omni profile bit-identical; feed-forward applied after the action delay stage; missing cmd/phase raises instead of silently dropping; deployment must use the same phi as build_obs (recomputing gives a one-tick phase misalignment).
Outcome
From C2-700, +100 iterations sufficed: product omni_c4_ff800 scored vy +120%/+125% (from +4%/-1%), 260/260 cells at 20/20 survival, zero old-skill regression, left/right gap 5 pp - the entire C4 saga resolved by changing the delivery mechanism, not the reward.
Mechanism
Exploration noise spans only the subspace its correlation structure can express; skills requiring coherent oscillation lie outside the span of i.i.d. per-step noise. Feed-forward moves the required structure into the action pipeline where it needs zero probability mass to appear, reducing the learning problem to stabilizing around a demonstrated behavior - which PPO does well.
Applies when
- a periodic/oscillatory skill trains flat under every reward variant
- open-loop injection of the behavior already works
- considering GRU/curriculum/exploration tricks for a rhythmic skill
“病因不在奖励，在探索形式：产生侧向速度需要相位锁定的 hip_roll 振荡，PPO 的逐步高斯噪声零均值无相关，合不出相位锁定分量。… target = default + scale·a + lat_ff(cmd_vy, φ)。策略动作因此是前馈之上的残差，保留全部平衡权限”
train/C_LADDER_RUN.md § 3j. C4-redo4：唯一变量 = 侧步参考改为前馈注入（契约级） A joint-velocity penalty meant to slow the get-up cut joint speed 16% and left the get-up time unchanged - the knob never moved the variable, so the idea it was meant to test stayed untested
dof-vel-penalty-is-not-a-pacing-knobBefore reading a result as a test of an idea, check that the knob actually moved the independent variable; velocity regularizers smooth a schedule they do not set, and a schedule driven by per-step task income moves only when that income's time structure does.
Symptom
The get-up took 0.6-0.9 s in Isaac with large torque demand; the user proposed getting up more slowly so less torque would be needed.
Context
The idea had support in the accounts: the acceptance bound is an upper bound of 5 s (5-8x margin), the quasi-static squat path peaks at 25% of the limits, and rolling over needs no momentum. R3.2 raised dof_vel from -1e-3 to -5e-3 as the single variable.
Change
dof_vel -1e-3 -> -5e-3 (child-run from R3.1).
Outcome
Get-up medians moved +0.02-0.04 s (noise); raw joint velocity -16%; torque demand median got worse (hip_pitch 46-48% -> 63-67%) as the new term competed with torque_headroom on the same joints; MuJoCo 98 -> 96%. Verdict FAIL on the knob, not on the idea, and the rung was not adopted. When pace was later attacked through the income's time structure (V2.5/V2.5b), the MuJoCo get-up moved into the 3.5-4.5 s design band.
Mechanism
The pace was set by base_height_progress paying for every step spent high (stand earlier, earn more); a velocity regularizer only smooths motion along the same schedule and does not change when the robot stands up.
Applies when
- trying to make a skill slower or gentler with smoothness penalties
- an experiment's primary metric did not move and a verdict is being written
- two penalties act on the same joints
“**关键判读:`dof_vel` 罚只把关节速度压了 16%,而起身用时一点没变。** 也就是说**这一级根本没有把"慢下来"这个自变量推动起来** —— 所以它**不构成对 用户假说的检验** … 起身节奏由 `base_height_progress` 的逐步计酬决定(早站起来就多 拿),速度正则只在同一条时间轨迹上把动作抹匀,不改变何时站起来。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §25 R3.2(dof_vel −1e-3→−5e-3,慢一点起身) 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 Cutting swing amplitude 40% raised yaw-momentum demand 53% - the falsified fix is recorded so nobody walks that road again
amplitude-cut-falsified-yaw-fixTest gait fixes against the quantity the ground must actually supply (torque/force rates vs friction ceilings), not against kinematic proxies; record falsified fixes with their mechanism so the search space shrinks permanently.
Symptom
Support-foot yaw slip stayed at ~223 deg (vs 225 deg) after walk_v6 cut joint swing amplitudes by ~40% (hip_pitch 20.6->10.4 deg, knee 31.3->18.8 deg) - the change built on the theory "smaller swing = less yaw momentum to dump into the ground".
Context
Direct measurement inverted the theory: yaw-momentum amplitude ROSE 16% (+/-0.1000 -> +/-0.1159) and its rate of change rose 53% (1.86 -> 2.84 N*m demanded from the ground), pinned exactly at the foot's supply ceiling (2.8-3.3 N*m at mu 0.6-0.7) - so slip could not drop. The extra demand lives in higher harmonics: v6's crisper foot placement (better clearance 34 mm, lower landing force) shortens the momentum- exchange window, concentrating the same exchange into less time. The verdict was written as a closed road: "下一轮不要再走这个方向". An honest residue was also booked: WHY amplitude down but momentum up 16% remained unresolved, with the named next step (per-rigid-body decomposition of H_z, since per-joint RMS sensitivity ignores phase correlations).
Change
The "reduce amplitude to reduce yaw momentum" lever was removed from the planning space; future yaw-slip work redirected toward the supply side (friction) and momentum-rate mechanics.
Outcome
Slip unchanged (225 -> 223 deg); the falsification and its mechanism became a permanent constraint on the fix search space.
Mechanism
Ground yaw torque demand scales with the rate of change of angular momentum, not its amplitude; kinematic amplitude cuts that also sharpen contact timing can raise dH/dt while lowering range. When demand exceeds the friction-limited supply ceiling, slip is set by the ceiling, so demand-side changes below the ceiling do nothing visible.
Applies when
- attacking foot slip or yaw drift via gait shape changes
- a fix targets an amplitude while the constraint is a rate
- documenting a failed intervention after a version comparison
“walk_v6 | ±0.1159 (+16%) | 2.50 Hz | 2.84 N·m (+53%) … 脚的供给上限 2.8~3.3 N·m(μ 0.6~0.7)—— v6 正好顶在天花板上, 所以滑移一点没降。… 结论: "减小摆动幅度以降低偏航动量"这条被 v6 证伪 —— 砍 39% 幅度, 需求反升 53%。下一轮不要再走这个方向。”
train/WALK_DIAGNOSIS.md § ② 未生效的机理: 减小摆动幅度反而让偏航需求上升 Never referee a suspect metric with another metric from the same code - they can share the disease
independent-referee-for-metric-disputesTo adjudicate a disputed measurement, compute the quantity by an independent method from raw state; never accept a sibling column from the same pipeline as the tiebreaker.
Symptom
A triple reversal on one question: sidewalk sign diagnosis (correct) was retracted using a second metric from the same script, then the retraction itself had to be retracted when that second metric turned out to be the buggy one - two opposite-direction errors on the same problem in one day, both written into the execution sheet.
Context
The probe's net-displacement metric suggested the sidewalk reference sign was inverted. Worried about yaw-drift pollution of net displacement, the author checked the same table's body-frame vy_mean column (~0.003 everywhere, 20-50x smaller) and retracted the sign diagnosis. But vy_mean came from mj_objectVelocity, which was silently reporting vertical velocity due to a frame bug - the "referee" was the diseased measurement. Re-measured with a truly independent computation (xmat.T @ qvel, world trajectory), the original diagnosis was confirmed: saw -0.5 gave vy +0.058/-0.130 (76%/106%), consistent with the net-displacement values all along (yaw pollution was real but only 10-21 deg, nowhere near reversal-sized).
Change
Lesson written twice, verbatim, as a hard rule: when questioning a measurement, the referee must be an independent algorithm (different code path, different physical derivation), e.g. rotate qvel by the body matrix directly, or inspect the raw world trajectory.
Outcome
With the independent referee in place the frame bug was confirmed, fixed, and the whole C4 line re-scored - revealing sidewalk had been working (see body-frame-velocity-api-audit).
Mechanism
Metrics sharing a code path (or an upstream API) share failure modes; agreement between them is evidence about the code, not the world. Only a measurement with an independent derivation can break the tie, because its errors are uncorrelated with the suspect's.
Applies when
- two metrics of the same quantity disagree
- about to retract a conclusion based on a second readout
- auditing evaluation code after a surprising result
“我用一个坏指标去质疑一个好指标，并把撤回写进了执行单。教训（写死）：质疑一个测量时，不能用同一份代码里的另一个测量当裁判 —— 它们可能同源同病。裁判必须是独立算法（这次的裁判应该一开始就是 xmat.T · qvel[:3]，或直接看世界轨迹）。”
train/C_LADDER_RUN.md § 3m. 二 我今天犯了两个方向相反的错 / 3n. 五 元教训 Decompose the offending quantity by channel first - then penalize the failure event, not the joints
penalize-the-slip-not-the-jointBefore penalizing motion to fix a side effect, measure which channels actually carry the offending quantity; prefer penalties conditioned on the failure event that are exactly zero for healthy behavior - and do not medicate behaviors that measurement shows are not sick.
Symptom
Heading drift with support-foot yaw slip (v5: 212-284 deg accumulated over 15 s); the previous v6 draft had attacked it by penalizing lateral joints (a roll 4.0 / yaw 2.0 "home" group) - which collapsed training into the standing basin.
Context
Before choosing the penalty target, the yaw angular momentum was decomposed by joint group with MuJoCo subtree_angmom weighted by real walking joint velocities: pitch-class joints (hip_pitch + knee) carry 95.3%, hip_roll 3.3%, hip_yaw 1.4%. The failed "home" group had been taxing 2.7/step to manage a 4.7% channel. The replacement, feet_yaw_slip (-0.2, |support-foot yaw rate| while in contact), targets the failure event itself and - decisively - costs a non-slipping gait exactly zero, which "横向回家组做不到". The same rung's do-not-do table applied the complementary principle to foot spacing: measured 196-214 mm, stable, no crossing - "没病不吃药" (no disease, no medicine).
Change
Removed joint-usage penalties for the drift problem; added the event-conditional slip penalty (-0.2, realized tax 0.141/step = 12% of tracking) alongside the existing linear-slip term.
Outcome
Turn-gain left/right difference improved 70% -> 19% and heading 185 -> 60.3 deg by v6 without a standing-basin collapse; the 2.7/step lateral tax never returned.
Mechanism
Penalizing joints taxes every use of a channel including healthy use, and if the channel carries little of the offending quantity the tax buys nothing while pushing the optimum toward immobility. An event-conditional penalty (slip while in contact) prices only the failure, leaving the healthy gait's cost surface untouched - and the channel decomposition tells you in advance whether a joint-side fix can even work.
Applies when
- choosing a penalty target for drift/slip/impact problems
- a proposed penalty taxes joints or motions rather than failure events
- a previous joint-penalty attempt collapsed the gait
“pitch 类 (hip_pitch + knee) 占偏航角动量 95.3% … hip_yaw 1.4% … 压 hip_yaw 是管 1.4% 的通道收 2.7/步 的税 —— 上一轮正是这样把策略推进了站立盆地。滑移项不惩罚走路: 不打滑的步态代价为零, 这是横向"回家"组做不到的。”
train/WALK_V6_MINIMAL.md § ① / ② 新增 feet_yaw_slip Add a single-point-suspension test to acceptance - the ground is a free stabilizer that hides divergence
suspension-probe-removes-free-stabilizerInclude at least one acceptance condition that strips the environment's free stabilization (suspension, or equivalent) - the sim-passing policy that fails on hardware is often failing a condition the battery never posed.
Symptom
walk_v5 looked healthy in every on-ground sim test yet diverged on the real robot - the acceptance battery had never measured a condition that would have revealed it.
Context
The battery gained a single-point-suspension probe (robot hung, feet free): measure torso tilt while the policy runs without ground contact. v5 scored 45.9 deg mean tilt suspended - wildly unstable - which the file calls "最灵敏的失稳探针(拿掉地面这个免费稳定器)": ground reaction forces passively stabilize a marginal policy, so on-ground metrics saturate long before the policy's internal balance is actually sound. v6 halved it (23.0 deg, target <10 deg) - progress visible on a scale where on-ground numbers showed nothing.
Change
Suspended-tilt added as a standing acceptance row; run under the honest contact parameters battery (accept_v2 with measured condim 4 / torsional friction 0.035), under which v5 correctly FAILS in agreement with the real robot.
Outcome
The sim battery's verdict on v5 flipped from pass to fail, matching hardware; suspended tilt became the discriminating metric between v5 and v6 (45.9 vs 23.0 deg) when ground metrics differed little.
Mechanism
Contact with the ground closes a stabilizing feedback loop the policy gets for free; removing it exposes the policy's own attitude control authority. A metric measured only in the assisted condition cannot rank policies by the unassisted quantity that hardware will actually demand during perturbations and flight phases.
Applies when
- sim acceptance passes but hardware diverges
- designing an acceptance battery for a legged robot
- two candidates tie on ground metrics
“单点吊那条是最灵敏的失稳探针(拿掉地面这个"免费稳定器"), v5 在地上一切正常却在真机发散, 就是因为验收从没测过这个工况。”
train/WALK_V6_MINIMAL.md § 5. 验收 The action-delay was implemented as lerp - beyond one step it extrapolated BACKWARD, so a whole lineage trained on a fictitious actuator
latency-lerp-reverse-extrapolationUnit-test plant-model code (delays, filters, randomizers) against hand-computed truth across its FULL configured range, not just the nominal case - a delay must be a queue, and any interpolation used outside [0,1] is a silent plant corruption that training will faithfully absorb.
Symptom
Every walk/stand model up to v11 had been trained on a silently wrong plant: the action-latency implementation lerp(cur, prev, lag) is only an interpolation for lag <= 1 - at lag 3 it computes 3*prev - 2*cur, a REVERSE extrapolation. With the configured (0, 0.06) s at 50 Hz (lag in [0,3]), about 2/3 of environments were adapting to actuator dynamics that do not exist.
Context
Listed as evidence item #1 for the full restart: "全部旧模型训在错误 plant 上" and the head suspect for the real robot's wild kicking. The fix replaced it with a true FIFO delay line (commit 7f13793) plus its own regression test (tests/test_action_latency.py) - but every exported ONNX predated the fix, which is part of why the lineage was frozen rather than patched.
Change
Delay implementation rewritten as an honest FIFO with unit tests; the restart baseline trained on the corrected plant from day one.
Outcome
A generation-scale training investment was revealed to have a corrupt plant underneath; the class of bug (plausible-looking math that silently changes meaning outside its valid range) got a permanent test.
Mechanism
lerp(a, b, w) leaves the segment for w > 1; used as a delay it fabricates high-gain inverted dynamics precisely in the largest-delay draws, so the policy learns compensation for an actuator that cannot exist - and DR then trains robustness to the artifact rather than to reality. No training metric can catch this: the sim is self-consistent, just wrong.
Applies when
- implementing or auditing action delay / filtering in a trainer
- a lineage behaves as if compensating dynamics nobody modeled
- deciding whether old checkpoints are salvageable after a plant bug
“动作延迟旧实现 lerp(cur, prev, lag) 在 lag>1 时是反向外插(w=3 → 3·prev−2·cur),配置 [0,0.06]s@50Hz 即 lag∈[0,3],约 2/3 env 在适应不存在的执行器动态。7f13793 已换真 FIFO,但所有 ONNX 均训于修复之前 —— 真机"乱踢"的头号嫌疑。”
train/OMNI_V0_SPEC.md § 0. 为什么从零 (1) The deploy-side walk/recovery switch - into recovery at tilt > 65 deg held 0.3 s, back at tilt < 15 deg with angular rate < 1 rad/s and straight knees held 1 s, a 15 s timeout, last action cleared both ways - and the handoff steps the design required are only partly implemented
walk-recovery-fsm-handoffSpecify a deploy-time controller switch as hysteretic, time-filtered predicates the robot can measure (proxy what it cannot, e.g. straight knees for height), a timeout that ends in a safe stop, and a complete handoff (history, clock, last action, command ramp) - then test that the code performs every handoff step, because the design document is not the implementation.
Symptom
With a recovery policy and a locomotion policy as separate networks, the robot needs a switch: when is it "fallen", when is it "up", and what state must be reset so the next policy does not act on the previous one's history.
Context
The 08-09 design: enter recovery when fallen (tilt > 55 deg or height < 0.60 x 0.384 m) for 150 ms, leave for a stand-hold when upright (tilt < 12 deg, height > 0.85 x 0.384 m, feet steady, |omega| < 0.8) for 400 ms - wide entry, strict exit, hysteresis - then a mandatory handoff trio before walking resumes (reset the walking policy's observation history, restart its phase clock at 0, clear its previous action and latency buffer) and a command ramp instead of a jump. The 08-14 implementation in deploy_policy (--recovery-policy): each policy under its own manifest contract (walk: nominal + scale; recovery: beta-anchored), the same rl_default gains, the tilt cutoff disabled; RECOVERY at tilt > 65 deg for 0.3 s; LOCO again at tilt < 15 deg and |omega| < 1 and knees straight (< 0.35 rad) for 1 s - the robot's computer has no height estimate, and straight knees stand in for height so a V3.0-style upright kneel cannot pass as standing; RECOVERY longer than 15 s ends in a safe stop; last_action cleared on both switches; command forced to 0 during RECOVERY; power scaling applies to LOCO only.
Change
An open account was written down with it: the recovery end state (0.355 m stance, hip yaw -/+27 deg) is outside the walking policy's training start distribution, so the first test must use stand / zero command as LOCO, and the long-term fix is to widen the walking policy's initial states rather than bend recovery's stance to suit walking.
Outcome
The spec records only a successful compile (py_compile); the hang test was left to be done on site. The operator runbook carries the three-step procedure (hang with stand as LOCO, mat and push, then omni walk as LOCO) but no outcome.
Mechanism
Two policies trained separately each assume their own history, clock and last action; a switch that carries any of them across feeds the next policy a state it never saw - the design called this "the walking policy seeing a ghost history".
Conflicts
The 08-09 design requires resetting history, clock and previous action plus a command ramp; the 08-14 implementation records last_action clearing and a zero command during recovery; the one-leg spec of 2026-09-14 lists the handoff hygiene as specified but not implemented - reset_history() is called by nothing (a 215-dim policy would carry four frames of pre-fall history), the phase clock is not zeroed, and there is no command ramp back to LOCO. No hardware run of the FSM is recorded in either source.
Applies when
- switching between separately trained policies on hardware
- a policy with history or phase observations is re-enabled mid-run
- the robot lacks a sensor the switching criterion was designed around
“**判据**:进 RECOVERY = 倾角 >65°(`--fall-tilt-deg`)持续 0.3 s;回 LOCO = §5 真机可测子集:倾角 <15° ∧ |ω|<1 ∧ **膝直 <0.35 rad(NX 无高度观测, 高度门用膝直代理 —— 防 V3.0 型"跪坐但直立"误判)** 持续 1 s (`--recover-hold`);RECOVERY 单次 >15 s(`--recovery-max-time`)安全停。 … **切换卫生**:两向切换 last_action 清零;RECOVERY 态 cmd 强制 0”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §50 FSM 双策略调度(2026-08-14,用户令):deploy_policy --recovery-policy A reward on a quantity the actor cannot observe teaches "produce less of it", never "correct it" - closed-loop correction needs an outer loop
reward-observability-limitBefore adding a reward, check the actor can observe (or infer) the quantity: unobservable-error rewards buy only average suppression - route correction tasks to an outer loop whose commands stay in distribution, and do not break a frozen contract to add an observation a deploy-side loop can supply.
Symptom
Heading kept drifting despite world-frame yaw rewards, and a reviewer proposed heading-error rewards - raising the question of what yaw shaping can even teach this actor.
Context
The adopted architectural verdict: the actor's 45-dim base observation cannot see accumulated heading at all - projected_gravity is invariant to rotation about the gravity axis, and omega_z is a rate, not an angle. World-frame yaw-rate rewards are therefore privileged shaping that can only teach "少产生旋转" (generate less rotation), never "偏了以后拉回原线" (pull back to the line after drifting) - the policy cannot represent the error it would need to correct. The S1 gate (<=5 deg / 10 s) demands exactly the former, so the stack is right for its gate; active heading correction is assigned to the deployment outer loop (--heading P-loop converting heading error into in-distribution wz commands) plus small-wz training - and the 215-dim contract is explicitly NOT extended with a heading observation ("契约不加 heading 观测,冻结不动"). The reviewer's companion bias hypothesis was adjudicated with data: drift is bimodal - a basin mechanism decides whether you leave (seeds vary +/-16-46 deg vs -385 to -391 deg), and once out, rotation direction is constant (weight chirality; candidate root: the phase clock always swings left first).
Change
Yaw shaping kept as rate-tracking (three-layer stack); heading correction owned by the deploy outer loop; contract frozen; the "which behaviors need an outer loop" question settled by observability analysis rather than reward tuning.
Outcome
Stopped a contract change and a futile reward direction; drift work split correctly into rate-suppression (trainable) and error correction (outer loop), consistent with the earlier measured 10x drift reduction from the deploy-side loop.
Mechanism
A policy can only condition on its observation sigma-algebra; rewards on functions outside it shift the marginal action distribution (open-loop average effects) but cannot create feedback on the unobserved variable. Whether to add an observation, an outer loop, or accept average-shaping is decided by the task's gate: suppression gates need shaping, correction gates need the variable in some loop's view.
Applies when
- adding rewards on accumulated/世界-frame quantities (heading, position)
- deciding between a new observation, an outer loop, and shaping
- a drift symptom persists across reward-weight changes
“actor 的 45 维基座观测不到累计航向(projected_gravity 对绕重力轴旋转不变,ωz 是速率不是角度)——世界系 yaw 奖励是特权塑形,只能教「少产生旋转」,不能教「偏了以后拉回原线」。… 主动纠偏闭环 = S3 把小 wz 进分布 + deploy --heading 外环 … 215 契约不加 heading 观测,冻结不动。”
train/OMNI_V0_SPEC.md § 3. 评审④判决(2026-08-06,S1.3 开训前) A real-robot verdict is (policy x deployment stack) - when the stack changes materially, old verdicts expire
stale-verdicts-under-old-stackDate every hardware verdict with the deployment-stack version it was measured under; after any material stack change, re-test before trusting old condemnations or old praises - with the interpretation of each possible result written down first.
Symptom
walk_v5 stood condemned as "kicks wildly" and walk_v6 as "cannot walk unassisted" - but those verdicts were issued under an earlier deployment stack (--heading did not exist yet, several fixes had just landed); only v7 had ever run under the current unified stack.
Context
New sim evidence sharpened the doubt: a same-harness four-version sweep showed v6 was the HEALTHIEST archive at the real operating point (cmd 0.15: dominant frequency locked at 2.50, steps 35:35 perfectly symmetric, foot distance 203/190 mm best of four, mean tilt 4.2 deg, saturation 15%). A full re-test under the unified stack was scheduled with per-version questions and a pre-filled interpretation table ("判读表(预填假设,回来对号)"): e.g. v6 walks + frequency ~2.5 -> old verdict was the stack's fault, v6 becomes the comparison champion; v6 walks but at ~1.25 -> period-doubling on hardware = confirmed plant gap, actuator fitting promoted to mainline; v5 no longer kicks -> the kicking was an old-stack artifact.
Change
All four versions re-queued on hardware under one stack (same torque limits, slew profile, heading loop, logging), with the version-specific legacy profile pinned; verdicts held provisional until re-issued.
Outcome
The re-test design separated policy properties from stack artifacts before any policy was permanently written off - and turned each outcome into a specific conclusion via the pre-filled table.
Mechanism
A deployed behavior is produced by the policy plus everything between it and the motors (heading loop, slew limits, torque caps, clock); verdicts implicitly condition on that whole stack. Fixing the stack invalidates the conditioning, so old failures may be stack artifacts and old successes may not survive either.
Applies when
- deployment tooling (limits, filters, loops) changed since a policy was last judged
- deciding which historical policy is the rightful baseline
- a sim sweep contradicts an old hardware verdict
“只有 v7 在完整的今日部署栈下上过真机 … v5"左右乱踢"、v6"未能自主"的判决全部来自更早的栈(--heading 尚不存在, 部分修复刚落地)——判决已过期。且 2026-08-02 四代同机仿真横测翻出了新证据:v6 在真机工况(cmd 0.15)下是四代里最健康的仿真档案”
train/REAL_SWEEP_V5_V8.md § 0. 为什么重测 Bracket a real-robot A/B with a repeated reference run - battery drain is the confound
battery-bracketed-real-abOrder hardware A/B sessions as A-B-A: repeat the first condition at the end, and void the comparison if the bracket runs disagree - never let battery or venue drift ride on the second condition.
Symptom
In a two-policy teleop A/B on hardware, the second policy is measured on a lower battery voltage than the first - a systematic bias that would be read as a policy difference.
Context
C2 real A/B (checkpoint 700 vs A800, same floor, same day) was scripted as 700 -> A800 -> 700-rerun, with the explicit note that a teleop session drains the pack and the trailing policy "naturally suffers".
Change
Protocol: run the reference policy first AND last; if the two reference runs differ noticeably, declare the whole session battery/floor-polluted and void the A/B ("结论作废重来"). Also log electricity per run.
Outcome
Called out as the round's only systematic confound, closed by one extra command ("这是本轮唯一的系统性混淆源，一条命令就能堵掉").
Mechanism
Battery voltage scales available torque, and torque loss hits behavior asymmetrically (see power-scale-hurts-nonforward-axes), so drain masquerades as policy regression; a head/tail reference pair converts the unobserved drift into a measured control.
Applies when
- comparing two policies or settings on hardware in one session
- any sequential hardware evaluation where the plant drifts (battery, temperature, floor wear)
“为什么要 700 复跑：一次遥控 session 下来电池会掉压，第二枚天然吃亏。头尾各跑一次 700，若两次 700 明显不同，说明这轮 A/B 被电量污染，结论作废重来。这是本轮唯一的系统性混淆源，一条命令就能堵掉。”
train/C_LADDER_RUN.md § 3c. A-3 真机 A/B（同一段地板、同一天、电量记账） 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 The get-up kept getting faster because standing earlier paid more every step - lowering torque authority barely slowed it, and only zeroing the standing income for the first 3 s moved the pace into the design band
per-step-income-drives-speed-time-gateWhen a skill is too fast, find the term that pays for finishing early and gate that income by time; keep the "get into position" term ungated so the policy does not learn to wait, use a ramp instead of a cliff, and confirm with a paired same-level experiment that the drift is motivational before changing it.
Symptom
The user judged the get-up too fast (Isaac medians about 0.7-1.6 s) and suspected path dependence: the policy seemed to get faster the longer it trained.
Context
Lowering the beta authority 0.40 -> 0.30 cut impact but moved supine only 1.70 -> 2.00 s: coordination-limited, not torque-limited. A paired experiment inside one beta level (checkpoint 15,600 vs 18,499, +2,900 iterations, same ruler) measured the drift: get-up medians -7 to -10%. The spec concluded the motive, not the path, was the cause - per-step standing income pays for every early step, and any lineage (even one from scratch) races toward the fastest solution inside its constraints.
Change
V2.5: the standing income (base_height, stand_pose, still, feet_on_ground) multiplied by w(t) = clamp(t/3 s, 0, 1); upright deliberately NOT gated, so righting and sitting up early still pay and the policy is not taught to lie flat and wait; a ramp, not a step. V2.5b: zero before t0 = 3 s, then a 1 s ramp.
Outcome
V2.5: Isaac 100%, get-up +17-43% slower, MuJoCo 100/100/98/100% (the best cross-simulator reading yet), still short of the 3.5-4.5 s design band - a linear ramp only discounts early income. V2.5b: MuJoCo supine 2.04 -> 4.10 s and prone 3.18 -> 4.04 s, inside the band; the Isaac pace barely moved (a lineage habit on a gradient-free plateau). Later the zero gate proved harmful when trained from scratch (curriculum-history-is-part-of-the-product) and in the V3.1 lineage (time-gate-vs-wide-stance-retire-the-fix).
Mechanism
Constraints on authority or velocity change how the fastest solution looks; the time structure of the task income decides how fast the fastest solution is.
Applies when
- a policy is faster or more aggressive than wanted and constraints do not slow it
- progress-style rewards pay every step spent at the goal
- performance drifts faster with more training at fixed settings
“**V2.5 机制(唯一)**:站立收入(base_height/stand_pose/still/feet_on_ground) 乘时间斜坡 w(t)=clamp(t/T_gate,0,1),T_gate=3.0 s;**upright 刻意不门控** (翻正/坐直早期照常拿钱,防"躺平等门开" … **用户假设量化 证实:逐步计酬动机在 β 包络内持续压缩时间,约束挡不住动机 —— V2.5 动机层 修法为正解。**”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §39 V2.5 预注册 / §39 补 配对实验 The recovery line's real-robot verdicts live in three places that disagree - the spec's "fairly stable, first real stand-up" for v2_6, an undated runbook note that only v3_1p1c works, and a first run whose details were never recorded
write-hardware-verdicts-backA hardware verdict is a dated entry in the authoritative ledger - policy file and stamp, gain profile, floor, battery, tries, log file, what was seen - written back the same day; a note in a command file is a pointer, not a verdict, and a newer verdict that contradicts an older one must say so.
Symptom
Asked "which recovery policy works on the real robot", the sources give different answers, and none of them carries the conditions of the test.
Context
08-09: the first real run was stopped as violent and dangerous; which ONNX, which gain profile and whether a log existed were marked "to be recorded" and never were. 08-11: v2_6 was "fairly stable", the line's first real get-up, with splits after standing; v2_5b's result and both CSVs were "to be reported". 08-14: v3_1p1c was stamped and pushed, with "the real first test still needs the user present"; the spec records no hardware result for it. The operator runbook (undated) puts above the v2_6 and v2_5b floor commands the note that none of the recovery policies below work, only recovery_v3_1p1c - a verdict never written back into the spec, with no date, floor, battery, number of tries or log attached.
Change
None recorded in the sources; this card records the gap.
Outcome
The line's authoritative record ends with v3_1p1c as the product awaiting its first real test, while the operator's note implies it is the only one that works and that v2_6 (recorded as a success) does not.
Mechanism
Verdicts given at the robot travel by word of mouth and command-file comments; without a record carrying the conditions, a later reader cannot tell a changed verdict from a changed floor, battery or stack.
Conflicts
§43 (2026-08-11) records v2_6 as the first successful real get-up ("fairly stable"); the undated runbook says every recovery policy except recovery_v3_1p1c does not work; §49 (2026-08-14) says v3_1p1c's first real test was still pending. The runbook's claim has no date and was never written back to the spec, so it cannot be ordered against §43.
Applies when
- choosing which policy to deploy from an operator's notes
- a hardware session ends without a written result
- two documents disagree about what worked on the robot
“下面的recovery都不行 只有recovery_v3_1p1c.onnx”
RL系统/FOLLOW THIS copy 2.md § #### Recovery Policy (operator runbook, undated) 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 Record-high training reward hid a fully-failing DR subgroup - aggregate metrics average over draws, gates must test per condition
aggregate-metrics-mask-subgroup-failureNever gate on metrics aggregated across DR draws: evaluate at fixed representative conditions (especially the deployment-critical stratum), and if a difficulty axis matters, ramp it on measured per-stratum success rather than sampling the full range from iteration zero.
Symptom
omni_s1e trained under constant-wide latency DR (0, 0.06 s) posted the lineage's highest-ever Isaac reward (129) - while the --delay 2 smoke evaluation showed 3/3 falls from iter 1500 onward, persisting to early stop; the usable checkpoint window shrank to iters 500-1000.
Context
Diagnosis written plainly: "聚合奖励掩盖重延迟尾部子群体失败" - the aggregated reward averages over latency draws, so the majority of light-delay environments can mask the total failure of the heavy-delay tail. The remedy for the training side was a survival-gated ratchet curriculum (survival_gated_latency): the sampling cap starts at 0.02 s and rises +0.01 only when a 4096-reset window's survival (time_out share) reaches >=90%, capped at 0.06, ratchet up-only - "增益与延迟耐受一起长,不升到策略撑不住的 地方" (gain and delay tolerance grow together; never raise past what the policy can hold). The detection side was already in place from the noise-crutch episode: the per-condition smoke curve, not the training reward, is the health readout.
Change
Latency exposure made curriculum-gated on measured subgroup survival instead of uniform-from-zero; per-condition (--delay 2) smoke evaluation kept as the authoritative curve; watcher scoring adjusted (survival weighted 3x) so recovery during hard phases is not early-stopped away.
Outcome
The failure mode was caught by the smoke curve within one generation; the follow-up redesign (deterministic staged latency) superseded the ratchet, but the aggregate-masking lesson held through both.
Mechanism
Expected-return training weights each DR draw by probability, so a subgroup can contribute bounded loss while being catastrophically failed; any scalar averaged over the randomization cannot distinguish "uniformly decent" from "great on easy draws, dead on hard ones". Only conditioning the evaluation on the stratum reveals the split, and curricula should raise difficulty on measured stratum success, not on schedule.
Applies when
- training reward hits records while a fixed-condition eval degrades
- wide DR on an axis where deployment sits at one known value
- designing curricula for difficulty axes (delay, push, terrain)
“常量 latency DR (0,0.06) 从零训被证伪——Isaac reward 129 历代最高,但 --delay 2 冒烟 iter1500 起 3/3 全摔持续到早停(聚合奖励掩盖重延迟尾部子群体失败,可用窗口只剩 500/1000)。… 采样上限 0.02 起步 … ≥90% 才 +0.01s,0.06 封顶,棘轮只升不降。”
train/OMNI_V0_SPEC.md § 3. S1.5(s1e 训练塌方复盘) Low-friction robustness traced to kd DR bandwidth, not friction training - by digging resolved params across 8 lineages, 3840 cells
kd-bandwidth-mu-law-attributionAttribute capability differences by tabulating every lineage's resolved training params and eliminating zero-variance and non-aligned columns first; never let an eval-side override knob serve as the explanation axis, and never write a mechanism into a law before it survives a targeted test.
Symptom
Lineages differed wildly in low-ground-friction survival, and the intuitive explanation - "some trained ground friction, some didn't" - was about to steer the ladder toward a ground-mu training rung.
Context
The attribution ran as a full parameter-vs-result cross: 8 lineages x 4 eval kd levels x 6 mu levels x 20 seeds = 3840 cells, with each lineage's RESOLVED training params dug out and compared item by item. First kill: all 8 lineages had ground mu pinned at (1.0,1.0) - zero variance - so low-mu differences cannot come from friction training at all. The only training parameter aligned with the mu score was kd DR bandwidth: narrow (<=0.24) lineages scored 19.9/19.5/19.5, wide (>=0.40) scored 17.1/15.2/14.6/14.2/12.8 - the two groups completely non-overlapping. Every rival was excluded item by item (kd center no; kp band no; COM small-beneficial non-driving; friction rung a clean double null 19.5->19.5 and 15.2->14.6; iteration count non-monotonic), and the one clean single-variable causal link confirmed it: the s2e-3 kd surgery (0.7,1.3)->(1.08,1.32) moved the score 17.1->19.5. Counter-proof against "each best at its own operating point": the narrow-band lineage evaluated OUT of band (18.2) still beat the wide-band lineage at its own band center (9.2). Two axes were ordered never to be conflated (the first attribution's own error): training kd bandwidth is a parameter axis / lineage property; the eval-side --kd-scale knob is a plant axis (more damping physically helps on slippery floors for ALL policies) - "plant 轴只能当部署缓解,不能当 归因". A tempting mechanism story ("drag vs step attractor") was tested and falsified, and explicitly kept OUT of the law: "机制未定, 不入定律".
Change
The planned ground-mu training rung was recommended closed ("建议 不开") in favor of a kd band-narrowing rung (0.8,1.2)->(0.9,1.1) centered on the deployed value - with a pre-registered risk that the law demands "bandwidth = measured dispersion" and the real robot's kd dispersion was not yet measured; if it exceeds +/-10%, narrowing sacrifices real coverage and the rung must yield.
Outcome
A whole training rung was deleted from the ladder by attribution alone (the second S2 pass dropped mu and push, 5 rungs -> 3); floor material became a deployment-selection input (mu <~0.6 -> deploy the kd1.2 gain profile) rather than a training target.
Mechanism
Cross-lineage performance differences must be attributed over the actual training-parameter table, not over eval knobs or plausible stories: eval knobs act on the plant for every policy (a physical effect), while lineage properties come only from training-time parameters. Zero-variance columns are free eliminations, and one clean single-variable rung is worth more than any correlation.
Applies when
- explaining why lineages differ on a robustness axis
- an eval-side knob (gain scale, power) changes results and invites misattribution
- deciding whether to open a DR rung for an axis never actually varied in training
“8 血统地面 μ 训练带全部钉 (1.0,1.0) 零方差,低 μ 差异与「训没训地面摩擦」无关,是 kd DR 带宽的副产物 … 宽 ≤0.24 → 19.9/19.5/19.5;宽 ≥0.40 → 17.1/15.2/14.6/14.2/12.8, 两组完全不重叠。… 训练 kd 带宽 = 参数轴/血统属性;评测部署 --kd-scale = plant 轴 … plant 轴只能当部署缓解, 不能当归因。… 机制未定, 不入定律。”
train/OMNI_V0_SPEC.md § 4. 地面 μ 鲁棒性 = kd DR 带宽的副产物 (2026-08-08) Two simulators disagreed 1.8x on one policy's torque demand - four explanations were eliminated with numbers, the surviving suspect was never tested, and neither reading was allowed to cancel the other
torque-disagreement-between-simulators-unresolvedWhen two simulators disagree on a safety-relevant quantity, eliminate the measurement explanations one at a time with numbers, name the survivor as a hypothesis, keep the pessimistic reading binding, and run the direct test (replay one action sequence open-loop through both plants) before the quantity is used to pass a gate for hardware.
Symptom
For R3.0, Isaac read hip_pitch torque demand at 75-80% of the limit (PASS); MuJoCo read the same policy at 148-152% (1.5x over the limit).
Context
Candidates were eliminated one by one: PD gains (identical, RS06 kp 30 / kd 1.5), the settle window (both skip the first 0.5 s), the statistic (worse-of-two-legs vs per-joint - explains ~15%), self-collision (the no-contact subset still read 152%), sampling (500 Hz peak vs 50 Hz sample - ~9%). About 1.8x remained. The prime suspect - Isaac's implicit actuator (PD solved inside the PhysX integration, chosen to mimic kHz firmware PD) against MuJoCo's 500 Hz explicit PD - was named but untested.
Change
The Isaac PASS was recorded as valid for the Isaac plant only; the prescribed decider was an open-loop replay of one action sequence through both plants, compared step by step, needing no training. It was upgraded to a precondition of R3.1.
Outcome
R3.1 ran in parallel with, not after, the replay, and the replay never appears as done. By §40 it was "the oldest open account" on the line, to be fed by real-robot logs - which the spec also never records. Meanwhile the 50 Hz sample under-read the peak by 34% as smoothing narrowed the spikes, so Isaac-side readings grew more optimistic exactly when the comparison mattered more.
Mechanism
Each measurement artifact explained a slice of the gap; what remained is a plant difference, and an untested plant hypothesis cannot license discarding the pessimistic simulator on a safety-relevant quantity.
Conflicts
The spec prescribes the open-loop replay (§23), upgrades it to an R3.1 precondition, then records that R3.1 ran in parallel without it (§24), and lists it as the oldest open account before hardware (§40); nothing through §50 (2026-08-14) records a result. Every later "torque gate PASS" on this line is an Isaac-plant reading plus a MuJoCo check, never a reconciled one.
Applies when
- Isaac and MuJoCo (or sim and hardware) report different torques, contacts or slips
- implicit vs explicit actuator models are in play
- a gate passes on one simulator only
“**同一个策略,一侧判 PASS 一侧判超限 1.5 倍。** … 口径项全部扣掉后仍剩 **~1.8×** 没有解释。 … 但**这条没有验证,不能拿它当结论去抵消 MuJoCo 的读数**。 … (做法:拿同一条 动作序列在两边开环回放,逐拍比 τ —— 不需要重训)”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §23 MuJoCo 复核门:成功率更稳,但 τ 与 Isaac 差 ~1.8× 没收敛 The walking lines' safety setting, power-scale 0.8, broke the recovery policy's full-range contract - it cut the ends of the joint travel (4/50 could not get up) and left the torque spikes untouched; a kp x 0.9 gain profile inside the trained kp band did the job
power-derating-cuts-full-range-contractA deployment derating knob means something only relative to the action contract: before reusing a line's "safe setting" on a new skill, check what it does to that skill's reachable range and to the term that makes the spikes, prefer a gain change inside the band the policy was randomized over, verify it in simulation, and re-decide when the contract changes.
Symptom
After the violent first real get-up (2026-08-09), the recovery policy needed a gentler setting for its next hardware test, and the walking and omni lines' standard derating - deploying at power-scale 0.8 - was the obvious candidate.
Context
The V0 recovery contract maps actions to absolute targets over the full joint range: a = +/-1 lands exactly on the URDF limits, and standing puts the knee at the clip. Candidates were compared on R3.1 in MuJoCo (5 categories x 10 seeds) on 2026-08-10 before any hardware time was spent.
Change
A new gain profile, rl_kp090 (kp x 0.9, kd unchanged), recorded in robot.yaml as the recovery hardware-test setting, with power-scale 0.8 explicitly banned for recovery.
Outcome
kp x 0.9: 48/50 got up; median torque demand on hip_pitch/knee fell from 120-125% to 100-104% of the deployment limit; leg-leg contact frames 2,152 -> 1,095; the change sits inside the +/-10% kp randomization the policy trained with. power-scale 0.8: 4/50 could not get up, because under the full-range contract it removes the ends of the travel (the deep squat's tucked legs, the straight standing knee), and the torque spikes (kp x error) did not fall at all. When the line moved to the beta-anchored contract, rl_kp090 was declared a V0-era choice that does not fit (beta is calibrated at kp 30) and deployment returned to rl_default; the deploy switch applies power scaling to the walking side only.
Mechanism
A power scale multiplies the action, which under an absolute full-range mapping shrinks the reachable workspace instead of softening the actuator; the spikes come from the proportional term on large errors, which only a gain change reduces - and a gain change inside the trained randomization band stays in distribution.
Conflicts
The undated operator runbook still carries an R3.1 "B comparison" command at power-scale 0.8 beside the rl_default baseline; the sources do not say whether it was written before the ban or was ever run.
Applies when
- reusing a power, torque or action scale from one skill on another
- a policy whose actions map to absolute targets over the full joint range
- choosing a gentler setting for a first or second hardware trial
“kp×0.9 / kd 不动 —— recovery_r3_1 成功 48/50, τ 需求中位 hip_pitch/knee 120~125% -> 100~104% 部署限, 腿-腿接触 2152 -> 1095 帧; ±10% 在训练 kp DR 带内. ⚠️ power-scale 0.8 对 recovery **禁用**: 全 ROM 契约下 0.8 砍的是行程 端点 (深蹲收腿/站直够不到), 实测 4/50 起不来, 且尖峰 (kp·err) 一点不降 —— 它是 walk/omni 的安全档, 不是 recovery 的.”
git:Lucen-recovery@origin/recovery:robot.yaml § gain_profiles 注释: recovery 真机测试安全档 (2026-08-10) / rl_kp090 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) Oversized lateral COM randomization (+/-5 cm) deliberately forces leg spread
com-randomization-forces-leg-spreadDR ranges can be behavior-shaping tools, not just robustness padding: oversize a randomization axis to force a strategy the reward struggles to express - and expect a compensating behavior to appear as the cost.
Symptom
Feet drift toward the centerline and even collide; policy has no incentive to keep a lateral support base.
Context
COM randomization ranges were chosen asymmetrically by axis: lateral +/-5 cm ("比常规大，故意的" - larger than usual, on purpose), fore-aft +/-2 cm, vertical +/-2 cm. The oversized lateral range is not robustness padding but a behavioral forcing function. Lucen logged it as directly relevant to its own roll-channel / sideways leg-kick symptom.
Change
Set COM randomization to lateral +/-5 cm, fore-aft +/-2 cm, vertical +/-2 cm, with the lateral band intentionally oversized to make narrow stances fail during training.
Outcome
Effective at separating the feet on the reference robot; side effect - the base began swaying left-right, which then required a foot-centerline distance penalty (see reward-chain-foot-height-landing-spacing).
Mechanism
Randomizing COM laterally makes narrow-stance policies fall for some draws, so PPO discovers wide stances as the only strategy robust across the band - DR used as an implicit reward. The sway side effect appears because the policy hedges against unknown COM by active lateral correction.
Applies when
- feet too close / self-collision in a learned gait
- roll-axis instability suspected to come from narrow stance
- choosing COM or mass-offset DR ranges
“两脚太近甚至互撞 → 先试质心横向随机化 ±5 cm，逼迫策略把脚分开；有效但引发新问题——基座开始左右摇摆 … 横向 ±5 cm（比常规大，故意的，用来逼出分腿）/ 前后 ±2 cm / 垂直 ±2 cm”
Experience.md § 质心随机化范围 (lines 75, 84-86) A field experiment is allowed when it is pre-scripted, single-variable, and self-reversing (RAM-only writes)
reversible-single-variable-field-experimentsPermit hardware-side experiments only when scripted in advance with one variable, a log, and automatic reversion (volatile writes, git restore); keep every config mirror (yaml vs firmware) changed and restored as a unit.
Symptom
A hypothesis needed a hardware test - v5's "wild kicking" might trace to the RS00 torque cap being deployed at 11 N*m vs its trained 14 (-21%), on exactly the ankle-roll/hip-yaw joints doing lateral-yaw work - but changing limits in the field is the classic way to lose track of robot state.
Context
The experiment was written to be safe by construction: change exactly one number (robot.yaml RS00 tau_limit 11.0 -> 14.0), write to firmware RAM only (--write without --save, so a power cycle automatically rolls back to the saved 12/17/11), run the single logged trial, then restore both yaml (git checkout) and RAM immediately. The consistency requirement is explicit: the deploy tool's torque self-check compares against robot.yaml, so yaml and firmware must change and restore together; and the hypothesis scoping is itself single-variable - RS06's 67% cut was measured irrelevant (gait uses only 16% of rated) and hip torque was left alone for safety.
Change
Field experimentation policy refined: not "never touch hardware settings" but "only pre-scripted, one-variable, logged, auto-reverting changes with config/firmware kept consistent".
Outcome
The sub-experiment could answer the torque-cap hypothesis without any risk of the robot persisting in an undocumented state - forgetting to restore costs nothing but a git checkout.
Mechanism
The danger of field changes is state divergence (robot config drifting from the repo's record), not the change itself; volatile (RAM-only) writes bound the divergence lifetime to one power cycle, and single-variable scoping preserves attributability even in a field setting.
Applies when
- a hypothesis requires changing firmware limits or gains on the robot
- field debugging tempts persistent config writes
- designing safe escape hatches for deployment tooling
“程序(--write 不带 --save = 只写 RAM,断电自动回滚)… deploy 的限扭自检是对 robot.yaml 比对的,所以 yaml 和固件必须同改同还原;忘了还原也没事,断电重启即回 12/17/11(上次 --save 的值),但 yaml 要 git checkout。”
train/REAL_SWEEP_V5_V8.md § 4. 限扭子实验(可选二期,只对 v5,单变量) 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 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) The best checkpoint to SHIP is not the best checkpoint to CONTINUE FROM - maturity is capital against adaptation shock
root-maturity-vs-product-qualityDecide shipping points and fork roots separately: gates rank products, but a root candidate must prove itself by surviving a continuation under the next rung's shift (dual-arm if in doubt) - and prefer the more-trained point as root when product metrics conflict with maturity.
Symptom
A band re-audit found s1e-300 beat the incumbent root s1e-500 on nearly every quality gate (stepping 19/20 vs 13/20 with historically-best 26.9 mm swing, speed gate 14/20 vs 2/20, heading 26 vs 54 deg/20 s) - suggesting the root had been mis-picked and the younger point should take over.
Context
The dual-arm control settled it the other way: continuing the S2 PD rung from s1e-500 adapted smoothly (3/3 smoke throughout), while the b300 control arm (same config, from s1e-300) fell into a survival valley under the PD shock (+100 iters: 1/3 -> 0/3), never climbed out within budget, and its 800-iter product scored 13/20 survival - eliminated. Verdict: "幼年点自身指标再好也扛不住新 DR 适应冲击, 成熟度是本钱,s1e-500 根被数据背书" - a young point's own metrics, however good, do not survive new-DR adaptation shock; maturity is capital. The audit still yielded value: the band scan (200-1000, per-100) mapped the lineage's arc (200 dragging -> 300 peak -> 400+ decay -> 900+ drift blowout), and 300 remains the better PRODUCT answer for shipping-as-is questions.
Change
Selection doctrine split into two questions with different answers: best-product point (quality gates at the point itself) vs best-root point (survives adaptation shocks; more training age = more capital), each decided by its own evidence - and root claims settled by a dual-arm continuation test, not by point metrics.
Outcome
s1e-500 kept the root role with data behind it; the S2e ladder built on it passed rung after rung, while the b300 line was closed at the cost of one control arm.
Mechanism
Early checkpoints sit near sharp optima with less accumulated robustness structure; their headline metrics reflect the narrow training distribution, not resilience to distribution shifts. A continuation rung is itself a distribution shift, so the root property being selected for is shock tolerance - observable only by actually continuing, never by static gates.
Applies when
- a younger checkpoint outscores the current root on quality gates
- choosing the base for a robustification or command ladder
- a continuation run stalls in an early survival valley
“b300 对照臂 … PD 冲击下存活谷(+100 起 1/3→0/3),预算尽未爬出,800 档 20-seed 存活 13/20 出局——幼年点自身指标再好也扛不住新 DR 适应冲击,成熟度是本钱,s1e-500 根被数据背书”
train/README.md § omni_s2e_pd (b300 对照臂) / s1e 选点重审 When the eval proxy has a known systematic bias, gate on within-proxy differences, not absolutes
relative-metrics-survive-proxy-biasWhere the evaluator is known-biased, design gates as within-evaluator contrasts (left vs right, A vs B, pre vs post) that cancel common-mode error; reserve absolute thresholds for quantities whose proxy calibration has been checked.
Symptom
MuJoCo systematically overestimated yaw turn gain (1.58/2.45 where Isaac measured 1.04/1.02), and friction alignment recovered only part of the gap (mu 1.0 -> 0.6 pulled it to 1.43/2.27) - so any absolute turn-gain acceptance threshold in MuJoCo would be judging the proxy's bias, not the policy.
Context
The acceptance criterion was rewritten to use only the left/right difference of the turn gain (standard: <20%): both directions pass through the same biased proxy, so the bias largely cancels in the difference while the policy's chirality - the thing being gated - survives. The absolute-gain row was dropped: "转向只判左右差,不判绝对值". A parallel task was still opened to align MuJoCo contact parameters to Isaac's (mu, restitution), since drift proved highly friction-sensitive (straight-line drift -65 deg at mu 1.0 vs +1 deg at mu 0.4) - bias reduction and bias-robust metrics proceeded together.
Change
Gate metric changed from absolute turn gain to left-right gain difference; proxy-alignment work scheduled separately rather than blocking acceptance.
Outcome
Turn acceptance became meaningful across proxy versions (v5 70% -> v6 19% difference measured the real improvement) while the absolute bias question was pursued without holding the ladder hostage.
Mechanism
A systematic multiplicative or additive proxy bias applies to both arms of a mirrored measurement; differencing (or ratioing) mirrored conditions cancels the common-mode bias to first order, leaving the asymmetry signal. Metrics built this way remain valid while the proxy is imperfect - which it always is somewhere.
Applies when
- a sim proxy disagrees with the trainer or hardware in absolute terms
- writing acceptance thresholds for direction-paired skills
- proxy calibration work would otherwise block a ladder
“转向只判左右差,不判绝对值 —— MuJoCo 的偏航增益系统性高估(实测 1.58/2.45 vs Isaac 1.04/1.02),摩擦只能解释一部分(μ 1.0→0.6 仅拉到 1.43/2.27)。差值是相对量。”
train/WALK_V5_SPEC.md § 6. 验收 Ungated phase shaping made standing 42x more expensive than stepping - and the stepping was cooking the hip motors
moving-gate-42x-stand-taxGate every phase/clock-driven shaping term on the command that justifies motion, price the cmd=0 case explicitly during design - and when a reward flaw is sim-only, book it with trigger conditions instead of operating immediately on a working lineage.
Symptom
At cmd = 0 the sim policy never stood still - it stepped in place and crept 0.98 m per 20 s; on the real robot the same lineage's stepping made hip_roll motors run 20 degC hotter than every other joint (43-48 degC vs 25-28).
Context
The arithmetic closed it: the three phase-shaping terms (joint_pos_ref 1.6, feet_contact_number 1.2, feet_clearance_swing 1.6) are driven by the gait clock with NO command gating, so standing at cmd=0 forfeits 3.32/step of shaping while honest standing earns only 0.078 of tracking - stepping wins 42x. The heat chain: perpetual stepping = perpetual single support = one hip_roll stalled at ~4.3 N*m (25% of torque limit) carrying the torso's frontal-plane moment - two hip_rolls = 90% of whole-machine steady-state I2R; measured stand-vs-step comparison: total heat -71% when actually standing. The suspended test acquitted the actuator (0.21 rad sag -> 0.0008 rad in air) and mechanics vetoed the easy fix ("降 kp 救不了热" - equilibrium torque equals the external load regardless of kp). The fix (moving_gate: hard-gate the three shaping terms on |cmd| > eps) was designed - then DEFERRED by the user because the real robot at the time stood fine: "真机不表现该问题, 为真机不存在的病改奖励表不划算", with written trigger conditions (C-ladder stand row persistently failing, or real robot starting to step/drift at cmd=0) and the known hazard tag (this is exactly the reward-change class that triggers the B-arm signature). When the real robot later DID step and cook, the booked trigger fired and moving_gate moved from debt to to-do with its benefit re-priced: "停止空烧 hip_roll,稳态发热降 ~71%".
Change
moving_gate designed with the gate_by_cmd convention; deferral, triggers, and expected heat recovery all pre-registered instead of patching the reward for a then-sim-only symptom.
Outcome
The cmd=0 stepping went from mystery to closed arithmetic; the thermal measurement (hip_roll +20 degC) quantitatively confirmed the 90%-of-heat prediction; the reward change waited for real-world justification instead of spending a risky revision early.
Mechanism
Clock-driven shaping terms define a perpetual-motion bounty unless gated by command; the resulting idle gait is not a training bug but the table's optimum. Its cost surfaces on hardware as stall-torque heating set by statics (mass x lateral offset), which no gain change can remove - only removing the motion (gating) or widening the stance can.
Applies when
- the policy steps in place or creeps at zero command
- specific joints run hot in idle behaviors
- deciding when a known reward flaw justifies a risky mid-lineage fix
“塑形合计 −3.32/步 … 净: 站定亏 42 倍 … 两颗 hip_roll 4.29 / 4.09 N·m(各占限扭 25%),占全机稳态 I²R 的 90% … 吊挂实测 hip_roll 跟踪误差 0.21 rad → 0.0008 rad … 降 kp 救不了热 … 真机不表现该问题, 为真机不存在的病改奖励表不划算”
train/README.md § C1 FAIL 节 (cmd=0 的 sim/真机分歧记账) / C2 真机 A/B 三b 发热定性 Foot dragging is an attractor, not a low amplitude - and joint damping is the mode switch, adjustable at deploy time
swing-bistability-damping-switchWhen a quality metric is bimodal, stop treating it as an amplitude to be trained up: map the modes against initial conditions and plant parameters, find the parameter that switches basins, apply it first as a deployment lever, and only then bake it into the training distribution (as a plant-family shift, never as an execution-mapping change).
Symptom
s2e_pd-1400's swing height "median 12.1 mm" hid a perfect bimodal distribution: 20 seeds split into a drag mode (2.6-4.9 mm) and a step mode (19.3-24.0 mm) with NOT ONE seed in between - the median sat in the empty gap, and "swing debt -11 mm" really meant "50% probability of falling into the drag attractor".
Context
Two designed experiments closed the mechanism. Test A (nominal plant, 40 seeds): step 42% / drag 58% / middle 0 - at nominal gains, initial conditions alone pick the mode, both modes 100% survivable. Test B (fixed init, kp x kd grid): kd is the mode SWITCH - at kd 1.3 all surviving cells step (13-22 mm), at kd 0.7 nearly all drag (2.7-4.3), only at kd 1.0 does init get a vote; kp >= 1.2 is dangerous (5/6 falls). Global verification at kd x1.3 (20-seed, delay 2): survival 20/20 at ZERO cost, step share 42 -> 80%, swing median 12.1 -> 18.4 mm, slip record low 334, thicker tilt margin - costs: vx 85 -> 78%, saturation +5 pp. A Pareto sweep then priced the knob: step share 42/72/75/88/82/90 across kd 1.00-1.30 with a linear vx tax of -2.3 pp per 0.1 kd - the basin gain is fully collected at kd 1.20 ("1.30 是 over-damping 纯多付税"). Mechanism: low damping leaves a landing micro-oscillation / ground-slide channel the policy can exploit to drag; damping plugs the channel.
Change
Deployment lever adopted: kd-scale 1.20 (conservative 1.15) as the legitimate successor to the power-0.8 crutch ("前者削幅度保稳,后者堵 拖地通道换步态,且不牺牲存活"); training-side prescription: move the DR band to nominal-1.2 x (0.9,1.1) = [1.08,1.32], deleting the [0.7,1.0) drag-teaching zone - a contract-level change requiring digest re-baselining, gated on measuring the real robot's actual kd dispersion first.
Outcome
The kd surgery rung (s2e_kd) delivered basin 8 -> 11/20, slip 405 -> 331, vx 81 -> 85% with no out-of-band fragility (below-band check 20/20) - "拐杖烧进分布的正确姿势", explicitly contrasted with the failed s1g amplitude version: this one changes the plant family the policy has seen, that one changed the execution mapping the policy would have to relearn.
Mechanism
The gait's swing behavior is a bistable dynamical system whose basin boundaries are set by plant parameters; a policy trained across a kd band that includes the drag basin has learned to inhabit it. Shifting the deployed (and then trained) damping moves the system into the step basin without touching the policy - a plant-side fix for what looked like a training deficiency.
Applies when
- a gait quality metric splits into distinct modes across seeds
- deciding between more training and a gain/damping change
- converting a deployment crutch into a training-distribution change
“20-seed 里拖地模式 2.6~4.9mm 与迈步模式 19.3~24.0mm 各半,中间一个不落 … kd 是模式开关——kd1.3 下 6/6 存活格全迈步 … kd0.7 下几乎全拖地 … swing 债的解(至少大半)在部署端阻尼档,不在训练端 … 机理:低阻尼下落脚微振荡/贴地滑给了策略顺势拖行的通道,加阻尼堵之。”
train/README.md § swing 双稳态定性 + kd 部署杠杆 (2026-08-07, 用户设计 Test A/B) Privileged signals (true velocity, foot force, foot height) go to the critic only
observation-honesty-critic-onlyTreat the actor observation vector as a hardware contract: every element must exist on the real robot with realistic noise; privileged simulator truths belong in the critic only.
Symptom
Policies trained on ground-truth base linear velocity work in sim and fail on hardware, where only a drifting IMU and encoders exist - the policy has learned to depend on a signal that does not survive deployment.
Context
Many open-source locomotion stacks feed simulator ground-truth linear velocity to the actor. The reference team refused: the real robot has no ground-truth velocity. Asymmetric actor-critic keeps the training benefit of privileged information without deploying the dependency.
Change
Route ground-truth velocity, foot contact forces, and foot heights to the critic only; the actor observes exclusively signals that exist on hardware (IMU-derived quantities, encoders, commands, previous actions).
Outcome
Recorded as adopted doctrine in Lucen's experience log; the trained actor's input contract matches what the real robot can actually produce.
Mechanism
The critic is discarded at deployment, so it may consume any privileged state to reduce value-estimation variance; the actor's observation set is a deployment contract - anything in it that hardware cannot supply (or supplies with different noise/drift) becomes a train/deploy distribution shift the policy was never trained to handle.
Applies when
- designing actor/critic observation spaces
- reviewing a config where the actor sees base_lin_vel or contact forces
- sim policy is strong but real robot drifts, oscillates, or falls without obvious actuator cause
“很多开源代码库把真值线速度喂给策略，Asimov 团队没有，因为真机上没有真值速度，只有会漂的 IMU 和编码器；用完美速度训练出来的策略会依赖它，然后在硬件上失效。真值速度、足底力、足高统统只给 critic”
Experience.md § 观测空间的诚实性 (line 7)