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
171 cards matching “curriculum-history-is-part-of-the-product”.
Tightening the bridge's rate limiter under an unchanged policy cut torque peaks 30-50% and made other things worse - the policy cannot see the limiter, keeps commanding and winds up; a deploy-side limiter is a safety net, not a cure
deploy-rate-limiter-windupA rate or torque limiter added at deployment lowers peaks but the policy still commands as if unconstrained (saturation, windup, new contacts); use it as a safety net mirrored in evaluation, and put the constraint where the policy can learn around it.
Symptom
After the violent first real-robot get-up, the cheapest candidate fix was to tighten the bridge's slew (rate) limit for the recovery policy without retraining.
Context
Probe on R3.1 in MuJoCo (5 categories x 3 seeds, mu 1.0), monkeypatching the limiter with no repository change: TIGHT = RS06 4.0 / RS02 3.0 / RS00 2.0 rad/s (about 0.08/0.06/0.04 rad per policy step) against the current vel_limit setting.
Change
The probe decided the role of the limiter rather than a deployment.
Outcome
Success 14/15 -> 12/15; get-up median 2.35 -> 3.53 s (max 9.30); torque demand peak median hip_pitch 164% -> 111%, knee 166% -> 86%; action saturation still 100%; leg-leg contact 558 -> 860 frames. The limiter was kept only as a real-robot safety net (mirrored into sim2sim evaluation); the cure moved into training - where the next lesson was that a limiter anchored on the last command is itself an integrator (slew-anchor-is-an-integrator).
Mechanism
A policy that never trained with the limiter keeps issuing the targets it learned; the limiter clips them, the target window runs ahead (windup), and the robot follows a trajectory the policy never evaluated.
Applies when
- a trained policy is too violent on hardware and a quick deploy-side fix is tempting
- adding slew, torque or velocity limits in a bridge or firmware
- evaluation and deployment use different limiter settings
“判读:**链路侧收紧立等可取地把 τ 峰值砍 30~50%,但成功率掉、饱和率仍 100%、 腿-腿接触反升** —— 策略感知不到限速器,目标窗口继续狂奔。⇒ 收紧 slew 只配当 **真机侧安全网**(必须同步进 sim2sim 口径,基础设施现成),**不配当治法; 治法必须进训练**。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §26 探针:收紧桥层 slew,r3_1 不重训直接测 Diagnose a behavior failure by enumerating hypotheses and auditing each against the actual config, cheapest first
hypothesis-table-code-auditBefore changing anything, write the full hypothesis list for the symptom and audit each against the resolved config and measured magnitudes, cheapest check first; train only on the survivors.
Symptom
Real robot leaned forward "wanting to walk" but dragged its feet instead of lifting them - a symptom with many plausible causes and no obvious single fix.
Context
Seven hypotheses were listed and each checked against the actual training config files (velocity_env_cfg.py, isaac_values.py), ordered by check cost: missing foot clearance term (CONFIRMED, primary - feet_air_time existed but no swing-height term at all); energy penalties dominating (REJECTED - energy terms total -0.19 vs tracking +1.2, 16%); command range too narrow (CONFIRMED - (0.15,0.35)); nominal pose too crouched / action scale too small (HALF - knee 0.5 rad = 28.6 deg deep, scale fine); mixed PD across motor types (REJECTED - already grouped); missing base-height reward (REJECTED - present at -5.0); height-drop termination (REJECTED - none exists, which itself became finding #4 of the fix list).
Change
The audit produced a ranked fix list (add clearance penalty; widen speed range; reduce nominal crouch) with each rejected hypothesis documented so it would not be re-litigated.
Outcome
Three confirmed causes fixed over v5/v6: swing height went 22-23 mm -> 34 mm, tracking 81% -> 87%; the rejected hypotheses stayed rejected (no wasted rungs on energy weights or PD grouping).
Mechanism
Multi-cause symptoms invite guess-and-train loops; a written hypothesis table forces each candidate to be confirmed or rejected against actual values (not impressions), and cost-ordering the checks means most hypotheses die for the price of reading a config.
Applies when
- a real or sim behavior failure has multiple plausible causes
- the team is about to "try a fix" without an audit
- post-mortems keep re-proposing already-rejected causes
“真机现象:躯干前倾像要走,脚抬不起来(拖着蹭)。按成本从低到高逐条核查 … | 1 | 缺 foot clearance | ✅ 成立,首要 | 有 feet_air_time,无任何摆动足高度项 | | 2 | 能量惩罚压过跟踪 | ❌ 不成立 | 能量类合计 −0.19,跟踪 +1.2,只占 16% |”
train/WALK_DIAGNOSIS.md § walk 拖地问题 — 七条假设的代码核查结果 Late training leaned on sampling noise as a stability crutch - deterministic play collapsed while training metrics stayed green
noise-crutch-deterministic-collapseEvaluate the deterministic policy in an external harness on a fixed cadence during training (not just at the end), select checkpoints on that curve, and treat a collapsing noise_std with rising training reward as a warning that noise is load-bearing.
Symptom
omni_s1's final checkpoint (model_5999) fell at 4 s even in Isaac's OWN deterministic play, while checkpoints from iter 1700-4000 were fine - and no training metric flagged anything. Policy noise_std had collapsed to 0.045 by iter ~990 (final 0.033).
Context
Diagnosis: the policy had learned to use its exploration noise as a dither/stabilizer - "策略把采样噪声当稳定拐杆,训练指标看不见" (the training metrics cannot see it, because training always runs with noise on). Countermeasures: entropy_coef 0.005 -> 0.01 to slow the std collapse, and - the structural fix - an in-training smoke loop (watch_ckpt.py): every 500 iters, export ONNX directly, run 3-seed MuJoCo evaluation, log CSV/TensorBoard curves plus three-view videos. The doctrine line was written in bold: "训练指标全绿不再是发育健康的 证据,冒烟曲线才是" - green training metrics are no longer evidence of healthy development; the smoke curve is. The follow-up run s1b showed the drift metric follow a U-shape (73 -> 8.6 at iter 3500 -> 76), making checkpoint selection BY the smoke curve (early stop at 3500) the shipping mechanism, with terminal re-degradation booked as known and unresolved.
Change
entropy floor raised; watch_ckpt smoke loop instituted as standing infrastructure; checkpoint selection moved from "last iteration" to "best point on the deterministic smoke curve".
Outcome
s1b shipped from iter 3500 (the U-bottom) instead of a degraded terminus; every later lineage (s1c/s1e, the C ladder's --every 100 loops) inherited the watcher as the standard guardrail.
Mechanism
PPO evaluates and improves the stochastic policy; if noise itself stabilizes the gait (dither smoothing a marginal limit cycle), the deterministic mean policy is a different, worse controller that training never measures. External deterministic evaluation on an independent simulator is the only readout of what will actually be deployed.
Applies when
- final checkpoints underperform mid-training ones
- noise_std collapses early while training reward climbs
- deciding which checkpoint to export and ship
“训练后期确定性脆化——noise_std iter~990 收到 0.045(终 0.033),model_5999 连 Isaac 确定性 play 都 4 s 摔(1700~4000 正常):策略把采样噪声当稳定拐杖,训练指标看不见。对策:entropy_coef 0.005→0.01 + train/watch_ckpt.py 训练中冒烟曲线 … 训练指标全绿不再是发育健康的证据,冒烟曲线才是。”
train/OMNI_V0_SPEC.md § 3. S1.1 修订记录 ② 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 Prove a new penalty actually fires - two ways a clearance term silently did nothing
inert-reward-term-auditBefore training with a new reward term, log its realized per-step value under the current policy and confirm it is nonzero where intended - check coordinate zero-points against FK and check who occupies the term's gate; and never weaken the term that creates the states your new term needs.
Symptom
A newly designed swing-height clearance penalty could have trained as a no-op twice over, and the companion advice to lower feet_air_time actively backfired when tried.
Context
Instance 1 (zero-point offset): the proposed code used body_pos_w of the foot link, but that is the ankle_roll_link frame origin, which sits 0.0585 m above the ground even with the foot flat on it - so (0.03 - 0.0585) is always negative and the penalty is永远 0; the 0.0585 offset must be subtracted (verified identical in MuJoCo FK and Isaac). Instance 2 (gate occupancy): the clearance penalty fires only in swing phase; a dragging policy keeps both feet in contact, so the penalty is constantly 0 for exactly the policy it was meant to fix - and worse, any slight lift immediately incurs it, a reverse threshold. Lowering feet_air_time to 0.5 on that advice measurably collapsed air time to 0.0002 (below v3). Corrected understanding: "clearance 是把已有的摆动相抬高, 造出摆动相仍要靠 air_time" - air_time creates the swing phase, clearance raises it.
Change
Fixed the height zero-point; kept feet_air_time as the swing-phase creator with clearance layered on top; both errors documented as corrections to the team's own earlier advice.
Outcome
With both fixed, swing height rose from 22-23 mm (v2) to 29 mm (v5) to 34 mm (v6); the inert-term failure class entered the standing checklist.
Mechanism
A penalty's gradient exists only where its gate is occupied and its argument crosses its threshold; frame offsets shift the threshold out of reach, and phase gates can have zero occupancy under exactly the policy being treated. Terms interact as an ecology - one term must create the states in which another can act.
Applies when
- adding any gated or thresholded penalty (clearance, impact, slip)
- a new term produces no behavioral change at any weight
- body-frame positions are used in reward code
“body_pos_w 是 ankle_roll_link 坐标系原点,平放触地时仍高出地面 0.0585 m。… (0.03 − 0.0585) 恒为负 → 惩罚永远是 0 … clearance 惩罚只在摆动相生效,拖地时两脚始终触地 → 惩罚恒 0;而一旦轻微抬脚就立刻扣分,对正在拖地的策略是反向门槛。… 正确认识:clearance 是"把已有的摆动相抬高",造出摆动相仍要靠 air_time。”
train/WALK_DIAGNOSIS.md § walk_v4 独立验收 — 本文档给的两处代码/建议是错的 Torque caps cannot soften footfalls - impact is falling-mass momentum, only the reward can treat it
landing-impact-not-fixed-by-torque-capsClassify each hardware symptom by the physics that sets it: quantities fixed by ballistic momentum at contact must be treated through the policy's trajectory (reward terms on approach velocity/force), never through actuator caps - and size such penalty weights against your own tracking reward, not a lighter robot's.
Symptom
Footfalls slammed at 1.78x body weight in sim baseline (human walking: 1.2-1.5x); the tempting hardware-side fix was cutting actuator torque limits.
Context
Measured directly: scaling torque limits from x1.0 down to x0.4 left peak landing force essentially unchanged (1.75 -> 1.78x body weight) - the impact force comes from the momentum of the falling mass at touchdown, not from motor effort. The fix has to change the trajectory, i.e. the policy, i.e. the reward: feet_contact_forces penalty above a threshold of 113 N (= 1.2x the 9.58 kg robot's weight), clipped, weight -0.005. The weight was sized locally, not copied: the reference robot's -0.001 would amount to 0.9% of tracking reward on this robot ("策略不会理它" - the policy would ignore it); -0.005 gives 4.4%.
Change
Added threshold-type contact-force penalty (-0.005, threshold 1.2x body weight) as one of v6-minimal's three changes; hardware torque cuts explicitly rejected as a footfall treatment.
Outcome
Landing force 1.72x -> 1.55x by v6 (target <1.5x, missed by 3% - progress booked honestly); the torque-cap dead end was documented so it would not be retried.
Mechanism
At touchdown the ground stops a ballistic mass; the impulse is set by approach velocity and effective inertia, which motors can no longer influence in the final instant. Only earlier trajectory choices (approach velocity, timing) reduce it - and those are selected by the reward, not by actuator limits.
Applies when
- footfall impact or landing noise on hardware
- proposals to derate torque as a softness fix
- importing contact-force penalty weights from another robot
“⚠️ 硬件限扭降不了落脚力 —— 砸地力来自下落质量的动量: 实测 tau ×1.0→×0.4, 落脚力 1.75→1.78× 体重纹丝不动。只有这条奖励能治。… ⚠️ 权重不能用 Pi 的 −0.001 —— 实测在我们身上只占跟踪奖励的 0.9%, 策略不会理它 (Pi 6.94 kg 更轻)。−0.005 给到 4.4%。”
train/WALK_V6_MINIMAL.md § ③ 新增 feet_contact_forces Changing the gait clock silently flipped a hardwired threshold's meaning - write derived constants as expressions
derived-constants-must-track-their-baseBefore changing any base parameter (clock, control rate, scale), enumerate every constant derived from it and every constant that must NOT change; convert derived literals into expressions of the base so the next change cannot silently flip a term's meaning.
Symptom
Slowing the clock 0.40 -> 0.50 s would have silently inverted the feet_air_time threshold's semantics: the 0.25 s threshold was hardwired, so at ct 0.40 the swing window (~0.20 s) sat below it (constant pressure to lengthen strides), while at ct 0.50 the window (~0.25 s) equals it - the term's meaning flips from "push longer" to "neutral" with no code error anywhere.
Context
The clock change audit walked every dependent quantity: most followed automatically (joint_pos_ref / clearance / contact_number cycle_time params, gait_phase observation, deploy/sim2sim/policy_io, export) - wiring confirmed, zero hand edits; the air_time threshold was the one hardwired constant, fixed by preserving the RATIO: 0.25 -> 0.3125 = 0.625 x ct, with the recommendation to commit it as the expression 0.625*ct "一劳永逸" (solved once and forever). The same audit also listed what must NOT follow the clock (50 Hz control rate, physics dt/decimation, 47-dim contract, action_latency absolute seconds, PD/torque limits) - the change's blast radius stated in both directions.
Change
feet_air_time threshold re-expressed as a fraction of cycle_time; auto-following vs must-not-change lists written into the spec for the clock migration.
Outcome
The clock migration (v10, repeated in v11) carried no silent semantic flips; the expression form removed the trap for every future clock change.
Mechanism
Constants derived from a base parameter encode a ratio at their birth; storing the evaluated number severs the dependency, so changing the base leaves stale semantics with no failing test. Expressions preserve the intent; and an explicit both-directions dependency list (follows / must-not-follow) is what makes a base-parameter change reviewable.
Applies when
- changing gait clock, control frequency, or units
- a reward threshold interacts with a phase/window duration
- config audit finds literals that encode ratios
“feet_air_time 阈值 0.25 是写死的,不跟 ct 走——0.40 时摆动窗 ~0.20s<0.25(恒拉长压力),0.50 时摆动窗 ~0.25s≈阈值(语义翻转)。按比例保原压力:0.25 → 0.3125(=0.625×ct;建议直接写成 0.625 * ct 表达式,一劳永逸)。”
train/WALK_V10_SPEC.md § 3. T —— 慢时钟 (训练侧必做一件) Single-impulse push recovery is a binary chaotic quantity - cross-machine floating-point divergence can flip the outcome
single-impulse-recovery-is-chaoticNever gate or compare single-event recovery outcomes across machines or domains: evaluate disturbances as survival distributions over phases and seeds, compare longitudinally on one machine, and treat any single-point cliff as unconfirmed until it survives the statistical protocol.
Symptom
Mac evaluation found a hard "0.8 N*s cliff" (0/3 survival) that the training machine flatly contradicted: the identical protocol (0.8 impulse at 8 s, cmd 0.2) survived 3/3 there, and a 0.6/0.8/2/4 cross sweep survived everything.
Context
The verdict became a named lesson ("跨机混沌课文"): whether one specific push at one specific phase is survived depends on a trajectory that diverges across machines from floating-point differences alone - "单次冲量恢复是二值混沌量, 跨机浮点发散可翻结局". The boundary was drawn precisely: the 20-seed statistical gates DO agree across machines (established precedent), but that agreement cannot be extrapolated to single-point recovery tests. Protocol amended: disturbance evaluation uses multiple push phases (8/10/12 s), >=10 seeds, and only same-machine longitudinal comparisons; the Mac-side recommendation built on the unreproducible cliff was not adopted, while its directionally-consistent small-impulse data was kept.
Change
Push evaluation redefined from single-event pass/fail to multi-phase multi-seed statistics, with cross-machine comparison banned for event-level results and allowed for distribution-level ones.
Outcome
A false hardware-relevant "cliff" was prevented from steering the ladder (the s2e push rung decisions were made on same-machine statistics); the chaos lesson was cited again when real push tests were restricted to qualitative cross-domain use.
Mechanism
Perturbation recovery near the viability boundary has sensitive dependence on initial conditions; different BLAS/GPU reduction orders yield different trajectories from identical configs, so a binary outcome at one phase is machine-specific noise. Averaging over phases and seeds restores a quantity whose expectation is machine-stable.
Applies when
- a push/disturbance result differs between machines or sim and real
- designing push-recovery acceptance tests
- a sharp pass/fail cliff appears in a chaotic-regime evaluation
“训练机上 Mac 原协议 (0.8 @8s cmd0.2) 3/3 全活 … 与 Mac 的 +0.8 0/3 直接矛盾。定性: 单次冲量恢复是二值混沌量, 跨机浮点发散可翻结局;统计门 (20-seed 八门) 跨机吻合的先例不能外推到单点恢复测试。协议改判: 抗推评测多相位 (push 时刻 8/10/12s) + ≥10 seed + 只做同机纵向比”
train/README.md § s2e 支线终章 (跨机混沌课文) The 1.5 Hz step-frequency gate was retracted - the author had misread his own actuator data, and the limit fought pendulum dynamics
gate-threshold-retracted-frequencyEvery gate threshold must cite its measurement and survive a first-principles sanity check; when a gate keeps failing otherwise healthy behavior, re-derive the threshold from the raw data before enforcing it again - and retract wrong gates in writing.
Symptom
An acceptance criterion "step frequency <= 1.5 Hz" kept failing healthy policies (walk_v4 at 2.33 Hz), and an earlier attempt to force slower stepping (v3) had killed stepping altogether.
Context
The threshold had been derived from the author's own actuator frequency-response measurements - but re-reading the raw table showed the misread: amplitude ratio at 2.0 Hz is 0.88 (knee) / 0.83 (ankle), acceptable; the genuinely bad point was walk_v2's 3.8 Hz at 0.58. Mechanism check agreed: the leg as a compound pendulum (L ~ 0.30 m) has a natural frequency ~1.1 Hz, swing half-period 0.45 s - the observed 2.1-2.4 Hz sits near where the leg wants to swing, and forcing 1.5 Hz "是跟摆动动力学对着干" (fights the swing dynamics). The frequency definition itself was pinned by two independent methods (contact-event counting 2.39 Hz vs FFT 2.33 Hz, agreeing): reported numbers are cycle frequency = steps per leg per second.
Change
Gate retracted in writing: "步频 ≤1.5 Hz 应删除或放宽到 ≤2.5 Hz"; frequency definition standardized before entering any config.
Outcome
walk_v4/v6's 2.1-2.4 Hz reclassified from disease to normal; the v3 failure got its probable explanation (suppressing stepping to meet a wrong gate).
Mechanism
A gate is only as good as the measurement and the reading behind it; thresholds inherited from a misread plot become invisible design constraints that later training obeys at real cost. Cross-checking a threshold against first-principles dynamics (pendulum frequency) is a cheap way to catch such misreads.
Applies when
- an acceptance threshold repeatedly fails policies that look healthy
- thresholds were set from a single person's reading of raw data
- a forced compliance with a gate degrades the behavior it guards
“我当初依据自己测的执行器频响定的,但看错了区间。… 2.0~2.4 Hz 的幅值比 0.83~0.88 是可接受的;真正不行的是 walk_v2 的 3.8 Hz。… 腿按复摆算(L≈0.30 m)自然频率约 1.1 Hz … 把它压到 1.5 Hz 是跟摆动动力学对着干(walk_v3 把迈步压没了,可能正是这个原因)。”
train/WALK_DIAGNOSIS.md § ② 撤回"步频 ≤1.5 Hz"这条验收标准 —— 是我定错了 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 IMU observation age cut 52-68 ms to ~4 ms by moving AHRS onto the MCU - as a single variable
imu-age-move-fusion-downstreamAudit observation age end-to-end and move time-critical fusion as close to the sensor as possible - and when you fix a latency, change only that one variable so the gain is attributable.
Symptom
IMU-derived observations reaching the policy were 52-68 ms old because attitude fusion ran in Python on the loaded host computer - stale attitude is a direct feedback-loop delay the policy was not trained with.
Context
The fix was scoped deliberately narrowly: move the AHRS computation from Python to the STM32 H7 (MC02). CAN topology explicitly unchanged, so the change is a clean single variable.
Change
AHRS fusion relocated Python -> H7. Before/after - IMU age: 52-68 ms -> ~4 ms; CAN timing: unchanged; Python load: high -> ~0.
Outcome
IMU age reduced by an order of magnitude with no confound; host CPU headroom recovered ("把计算单元搬在stm32上, 这样imu有剩余").
Mechanism
Sensor age is pipeline latency, not sensor quality: fusing on the MCU next to the sensor removes host scheduling jitter and interpreter overhead from the critical path. Keeping the bus topology fixed makes the improvement attributable to the relocation alone.
Applies when
- measured sensor-to-policy age far exceeds sensor sample period
- attitude fusion or filtering runs on a loaded host CPU in an interpreted runtime
- planning infrastructure changes during a sim2real campaign
“AHRS 搬到 H7——这个不改 CAN 拓扑，只是把一段计算从 Python 挪到 MC02，单变量：IMU age 52–68 ms → ~4 ms / CAN 时序 不变 / Python 负载 高 → ≈0”
Experience.md § AHRS 搬到 H7 (lines 28-35) Friction DR was demoted after a measurement (94% success at mu 0.4 with no friction randomization) and promoted again when the action contract changed and mu 0.4 fell to 76% - DR priorities belong to a plant and contract, not to a task
friction-priority-re-measured-after-plant-changeRe-measure transfer along the friction axis for every new action contract or plant, not once per task; a DR priority settled under one action parameterization does not carry to the next.
Symptom
Getting up is all scraping and pushing against the ground, and training pinned friction at 1.0, so friction looked like the first thing to randomize.
Context
The MuJoCo gate on R0.5 (5 categories x 10 seeds x 4 friction levels) measured 100/100/98/94% at mu 1.0/0.8/0.6/0.4: degradation showed first as time (prone 3.2 -> 5.3 s), not failure, so friction DR was demoted and the DR budget earmarked for mass/COM. After the switch to the beta-anchored action space, V2.2 read 90/94/90/76%: mu 0.4 was now the weak row.
Change
V2.3 (single variable): friction DR static (1.0, 1.0) -> (0.2, 2.0), dynamic (0.15, 1.6), the HiFAR range keeping the base dynamic/static ratio; restitution untouched. Continued from v2_2.
Outcome
Isaac nominal 99.8% (DR did not hurt the nominal plant); MuJoCo 98/98/96/92% - mu 0.4 76 -> 92%, mu 1.0 back to R3.1's 98% with bounded torque.
Mechanism
How much a policy leans on friction depends on how it moves; the spec records that the sensitivity rose after the action contract changed but does not establish why.
Applies when
- changing the action space, gains or authority of an existing skill
- deciding which DR axis to spend the next rung on
- an earlier sweep justified leaving an axis unrandomized
“**μ 砍到 0.4(训练值的 40%)仍有 94%**,退化先体现在**用时**(prone 3.2→5.3 s) 而不是成败。μ≥0.8 完全无损。→ **§17 曾把"摩擦随机化提到 R4 第一项"当作优先 事项,这条实测把它降级了**”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §21 MuJoCo 复核门 ② 摩擦依赖 Knee swing collapsed because it directly trades against the slip penalty - price the conflict explicitly and clamp what reward cannot hold
knee-swing-vs-slip-pricingWhen a behavior collapses as another metric improves, look for the term pair trading them and set their price ratio deliberately (with escalation and reverse tripwires pre-registered); where the policy actively spends action budget to undo your target, stop paying more reward and clamp the target space structurally.
Symptom
Knee peak-to-peak swing collapsed across generations - v5 33 deg, v10 26-30, v10b 7-8, v11 6.5-8.6 - and rolling the clock back did not recover it, acquitting the clock; the collapse tracked the gated slip penalty instead: "屈膝与不打滑在当前奖励里直接对抗" - v10b's excellent 93 deg slip was purchased with knee amplitude.
Context
Reward-side flexion fixes had failed three times: raising reference amplitude backfired twice (v9/v11), and v11's deep-squat default was actively fought by the policy - it spent 0.68 of action budget pulling the squat straight ("被策略花 0.68 动作拉直反杀"). v12's design accepted the conflict as real and attacked on two tracks: (1) ECONOMICS - a direct knee_swing_amplitude reward (+0.3, target 0.55 rad, capped at 0.6/step = 55% of tracking), explicitly opposed to the slip penalty by design ("显式对立——这正是设计:v12 就是这场对抗的定价实验"), with an escalation ladder (K +0.3 -> +0.5, then slip -0.5 -> -0.3, one layer at a time) and a reverse tripwire (slip telemetry back at v10 levels -> slip weight to -0.8, accept ~20 deg knee compromise); (2) STRUCTURE - knee target bounds [0.2, 0.9] rad so full straightening is physically impossible (straightest 11.5 deg) and the 0.68 fighting budget is released. A bonus falsifiable prediction was attached: phase-lock strength tracks amplitude (v9_probe 48 deg locked 2.5 Hz; v11 low-amplitude 1.36 Hz unlocked), so if K works, hardware phase-lock should return - one change, two verdicts.
Change
knee_swing_amplitude reward + knee target clamp + pre-registered escalation/reverse levers; the failed reward-side-only approach retired.
Outcome
The lineage was frozen before v12 trained (strategic reset), but the diagnosis stands as the walk line's clearest example of two reward terms trading a behavior between them, with the pricing experiment and structural clamp fully designed and calibrated.
Mechanism
When two terms price opposite aspects of one motion (swing amplitude creates yaw momentum that becomes slip), the optimizer settles wherever the price ratio puts it - patching one side moves the equilibrium, not the conflict. Explicit pricing makes the trade a designed quantity; structural clamps remove the regions where the policy spends budget fighting the designer.
Conflicts
The pricing experiment (K vs slip) was designed and calibrated but never trained - the 2026-08-05 reset suspended v12; the collapse attribution table and the 0.68-action counterattack are measured, the remedy's效果 is untested.
Applies when
- one gait quality degrades in lockstep with another's improvement
- the policy visibly fights a default pose or reference
- repeated reward-side fixes for the same behavior have failed
“膝摆塌在 v10→v10b,头号嫌疑是门控滑移罚(四代实测膝 p2p:v5 33° / v10 26~30° / v10b 7~8° / v11 6.5~8.6°;退时钟没救回 → 非时钟)——"屈膝"与"不打滑"在当前奖励里直接对抗 … 奖励侧修屈膝已三败 … v11 深蹲 default 被策略花 0.68 动作拉直反杀”
train/WALK_V12_SPEC.md § 0. 定位 / 2. K —— 膝摆经济(与滑移罚的对偶) Cross-simulator gate (Isaac Lab to MuJoCo) comes before any hardware attempt
sim2sim-gate-before-sim2realGate every policy through a second simulator with an independently built plant before hardware; treat sim2sim failure as a contract or overfitting bug, and sim2real failure after a sim2sim pass as a plant/actuator gap.
Symptom
A policy that only ever ran in its training simulator carries untested dependencies on that simulator's solver, contact model, and defaults; the first place those dependencies surface should not be the real robot.
Context
Standing order of operations for the whole Lucen program, recorded as the opening line of the experience log: train in Isaac Lab, gate in MuJoCo, only then go to hardware. The MuJoCo side is the same plant used for evaluation batteries, so a sim2sim pass also validates the exported policy + contract (obs ordering, scales, defaults) outside the training stack.
Change
Pipeline rule adopted - every checkpoint must pass the MuJoCo evaluation battery (sim2sim) before it is considered for real deployment (sim2real).
Outcome
Used generation after generation as the cheap filter; hardware sessions only ever received policies that had already survived a second simulator.
Mechanism
Two simulators disagree exactly where a policy is overfit to simulator-specific artifacts (contact softness, integrator, default parameters, obs conventions); a cross-sim transfer catches contract bugs and solver overfitting at zero hardware risk, so real-robot failures that remain are attributable to genuine plant/actuator gaps.
Applies when
- planning the path from training to first hardware trial
- exported policy behaves differently outside the training framework
- triaging whether a real-robot failure is contract vs plant
“先sim2sim - 从isaaclab 到mujoco / 再sim2real”
Experience.md § opening lines (1-2) Write each config's expected hardware signature before the session - and if reality disagrees, change the books, not the conclusion
preregistered-real-expectationsBefore hardware runs, write per-config expected signatures and the disagreement rule (hardware outranks sim; discrepancies get recorded, not reconciled); validate the harness by checking it reproduces at least one known real behavior.
Symptom
Hardware impressions are easily narrated after the fact; without written expectations, any real-robot outcome can be made to "match" the sim story.
Context
The S2 acceptance sheet carried a section titled "sim 侧预注册预期 (事后核对, 不许事后改)" - per-configuration behavioral signatures written before the session: s1e@0.8 the disturbance king (push 159/160, zero chirality, all-mu 20/20) at the cost of speed gates 0/20 and zero-command wander ~0.98 m with -29.5 deg/20 s rotation; fric-3000 "walks accurately but is easier to push over"; fric-2400 neither. Credibility check included: the sim harness had reproduced the already-recorded real behavior (pace in place + right drift + net rotation -30 deg/20 s), which "提高本单全部预期的可信度". The anomaly clause fixed the epistemics in advance: if results systematically disagree with sim, "不改结论改账" - don't massage the conclusion, write the discrepancy into the books, and per the earlier zero-warning lesson, hardware wins.
Change
Every hardware session ships with a pre-registered expectation table (signature per config), a baseline-match credibility check, and a written precedence rule for disagreement.
Outcome
The A/B session became falsifiable: agreement confirms the proxy, disagreement is booked as a proxy-bias finding rather than argued away.
Mechanism
Pre-registration converts qualitative hardware sessions into tests of the sim-to-real mapping itself; a reproduced known behavior calibrates trust in the remaining predictions; and fixing "who wins on disagreement" beforehand prevents authority from drifting to whichever source flatters the plan.
Applies when
- planning any hardware acceptance or A/B session
- the sim harness's credibility in this regime is unestablished
- post-session write-ups tempt narrative fitting
“⚠️ sim 复现了真机已记录的「原地踏步 + 右漂 + 净旋 −30°/20s」—— harness 与真机行为对得上, 提高本单全部预期的可信度。… 结果与 sim 系统性不符 → 不改结论改账: 写进 README 该节, 按 「Isaac 指标三次零预警」的教训, 以真机为准。”
train/REAL_RUN_S2.md § 2. sim 侧预注册预期 (事后核对, 不许事后改) / 4. 异常处置 An edge-triggered landing penalty missed the tail and fired after the harm - penalize overspeed continuously inside the contact window
penalize-tail-before-touchdownPenalties aimed at impact/violation events must (a) price the excess over a threshold, not the mean, and (b) be active on the approach (state-gated window), not triggered by the event - check your control rate can even see the event you are penalizing.
Symptom
The v7 landing penalty (vz^2 on the contact-force rising edge, weight -10) did not bite: landing-velocity 95th percentile stayed at 2.61 m/s against a 0.3 target.
Context
Two structural faults were identified: (1) it penalized the MEAN over sparse events - many soft landings dilute the occasional violent slam, while the damage (GRF peaks, motor peak load) lives in the tail; (2) it fired AFTER touchdown - at 50 Hz evaluation the rising edge is aliased by physics decimation, so the read vz is often the already-decelerated post-impact value: underestimated, and with no shaping gradient before contact. Replacement: continuous penalty while the sole is inside a height gate (h < 0.03 m): relu(-vz - 0.30) - only the excess over an allowed approach speed is penalized (tail only), and gradient exists for several frames BEFORE touchdown. The sole-height computation again subtracts the 0.0585 m link offset ("WALK_DIAGNOSIS 坑#1, 别再踩"); the edge-triggered version was kept as a diagnostic only.
Change
feet_landing_vel reformulated: edge-event vz^2 -> in-window relu(-vz - v_ok) with v_ok 0.30 (conservative vs the sqrt(L)-scaled human value ~0.19, to be tightened after passing), h_gate 0.03, weight unchanged -10.
Outcome
The failure analysis of the first form was written before the second was trained; the v_ok escalation path (0.30 -> 0.45 if the robot becomes afraid to land) was pre-registered in the risk table.
Mechanism
Sparse-event mean penalties optimize the average case while the constraint is a quantile; and any penalty evaluated only at/after a discrete event gives the optimizer no gradient along the approach trajectory that determines the event. A state-gated continuous excess penalty fixes both: it prices only violations and shapes the approach.
Applies when
- impact/landing penalties fail to move tail percentiles
- a penalty is triggered by contact edges at a coarse control rate
- designing constraint-style penalties for rare violent events
“罚的是均值路径:上升沿是稀疏事件 … 大量软着陆稀释偶发猛砸;而伤害在尾部 … 罚在触地后:50 Hz 评一次,上升沿被物理 decimation 混叠,读到的 vz 常是撞完已减速的值——既低估,又没有触地前的塑形梯度。”
train/WALK_V8_SPEC.md § 2. 改动 B — 落地惩罚改罚尾部、罚在触地前 When hardware underperforms, audit deployment knobs before prescribing retraining
deploy-knob-attribution-before-retrainingBefore any "retrain it" decision, reproduce the symptom in sim under the exact deployment configuration; if the symptom follows the deployment knob rather than the checkpoint, fix the knob or randomize it in training - never top-up-train the skill.
Symptom
Real-robot feedback after the C4 deployment - "turning is weak" - with two retraining options on the table: top up turn training, or restart from the s1e root.
Context
The sim account showed the policy turned well (75-81% at pw1.0); the robot was deployed at power-scale 0.8. The 3-6 pp difference between C2 and C4 policies at the same power was noise; the 40-50 pp difference between power levels was the entire effect. Both proposed retraining paths would have burned budget on a non-existent training gap, and restarting from s1e would additionally have discarded the sidewalk skill that took four rungs and a coordinate-bug hunt to obtain.
Change
Decision: retrain nothing. (1) Try pw1.0 on hardware first - sim says net gain; (2) only if 1.0 is unacceptable (heat/feel), the correct training fix is power/torque randomization in the S2 plant line (one variable, fixes turn and backward together) - not skill top-up; (3) restart-from-root explicitly ranked worst.
Outcome
The "weakness" was fully explained by the deployment knob; the sim/real signatures matched the earlier power-derating law verbatim ("与 C2 时代 power 衰减主要伤非前进轴 逐字吻合").
Mechanism
The policy's competence is defined under its training plant; deployment knobs (power scale, teleop mapping, command bands) silently define a different plant. Attributing a deploy-plant effect to a training gap produces exactly the wrong fix - more training on the wrong variable.
Applies when
- real robot underperforms a skill that sim says is fine
- proposals on the table include retraining or re-rooting
- deployment uses any override the trainer never saw (power scale, remapped commands, different control rate)
“正确的训练修法不是补训转向，而是训练时加 power/力矩随机化让策略在 0.8 下自己补偿 —— 单变量，属 S2 plant 线，一次同时修好转向与后退；从 s1e 重训是最差选项：丢掉四轮 + 一个指标 bug 才换来的侧走，而 C2 的转向本来就没问题。”
train/C_LADDER_RUN.md § 3p. 三 处置顺序（回答「补训转向 还是 回 s1e 重训」：都不该） 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 Gate a new reward term by its command so all old modes score pointwise identical
gate-new-reward-terms-by-commandWhen a reward term must be added mid-lineage, gate it on the condition that defines the new task so every pre-existing situation scores exactly as before - and still watch for value-rescale pathologies inside the new mode.
Symptom
Adding a lateral tracking reward (track_lin_vel_y_exp) ungated would have paid 0-2.0 per step even in modes with cmd_vy = 0 (healthy gait sway of vy ~0.1 already earns 1.28), shifting the whole reward table by a large bias and rescaling the value function - no longer "just adding one mode".
Context
C4 was the C ladder's only true reward surgery. Single-variable discipline required that the change be invisible to every existing mode. The chosen construction: gate_by_cmd=True - the term pays only when |cmd_vy| > 0.02, so for all modes with cmd_vy == 0 the term is pointwise zero, i.e. the reward is pointwise identical to before the change. The same trick appeared earlier in C1: replacing the vy L2 tax with a command-error version that is "对 cmd_vy≡0 逐点同值" (pointwise equal when cmd_vy is 0), explicitly classified as not-a-reward-change.
Change
track_lin_vel_y_exp added with gate_by_cmd=True (weight +2.0, std 0.15); the residual acknowledged honestly - inside the side bucket the values DO change, so the rung still watched the known reward-reshuffle pathology signature (s1c B-arm: scatter -> half-recover -> collapse) as a stop criterion.
Outcome
Old modes provably unaffected (pointwise-equal argument); attribution for any change in old-skill metrics stayed clean through the C4 redo series.
Mechanism
PPO's critic normalizes to the reward scale it sees; an ungated additive term shifts returns in every state and re-scales advantages globally, entangling the new skill with all old ones. Command-gating confines the new term's support to the new mode's state distribution, making "pointwise identical elsewhere" a provable property rather than a hope.
Applies when
- adding a tracking/shaping term for a new command or skill to a lineage that must not regress
- reward change proposed while other skills are still being gated
- reviewing whether a config diff counts as a reward change
“只在 |cmd_vy| > 0.02 时付。不门控的话它对 cmd_vy≡0 的老模式也给 0~2.0 分（健康摇摆 vy≈0.1 → 1.28），等于给整张奖励表加一个大偏置、值函数尺度全变 … 门控后老模式逐点得 0 = 与加项前逐点同值，单变量纪律成立。… 但 side 桶内的值确实变了 —— 这仍是奖励表改版，开级盯 s1c B 臂签名”
train/C_LADDER_RUN.md § 3d. gate_by_cmd=True（重要） Removing a hand trim re-exposed the plant offset it had been silently compensating - and a slope scan told bias from sensitivity
hand-trims-hide-plant-offsetsTreat hand-tuned trims as undocumented plant measurements: before deleting one, find what it compensates and re-house that knowledge in the model or the reward budget; diagnose posture errors with a sensitivity sweep to distinguish constant bias from gain problems.
Symptom
After switching from the old hand-trimmed default to the clean geometric zero, the retrained stand policy's only regression was torso lean: 1.8 deg -> 4.1 deg backward.
Context
The old default's ankle-pitch trim (-0.0489/+0.0628) had been pre-compensating a fore-aft COM mismatch; removing the trim removed the hidden compensation, and the posture reward alone was too weak to win it back. A COM sensitivity scan settled what kind of problem this was: sweeping base COM offset -50 to +50 mm gave nearly identical slopes for old and new policies (~0.026 deg/mm) - "不是质心敏感度问题, 是恒定偏置" (not a sensitivity problem, a constant bias). Fix landed in stand_v1b: posture corrected to +0.24 deg while keeping symmetry (<=0.1 deg) and low effort (0.259), disturbance rejection better than both predecessors. Model credibility was checked the honest way: v0's sim prediction at the real COM position (-22 mm) was -2.31 deg lean vs real measured 2.2-3.1 deg - "预测精准命中" - which is what licensed trusting v1b's -0.52 deg prediction. (Side flag from the same file: a sign convention had been documented wrongly in early comments - gravity_base[0] > 0 is forward lean.)
Change
Trims retired in favor of explicit modeling: symmetric geometric default plus a posture-reward budget sized to carry the real COM offset; the offset itself known (real COM ~22 mm behind model).
Outcome
stand_v1b passed acceptance as the standing lineage's final version; the walk-line requirement "加大躯干姿态惩罚权重" was upgraded from suggestion to mandatory, since walking amplifies what standing tolerates (real walk_v1 hit 26 deg lean vs sim 7.4).
Mechanism
Hand trims are plant knowledge stored in the wrong place - invisible, asymmetric, and stale after recalibration; removing them re-exposes the raw plant error. A sensitivity sweep separates the two possible diagnoses (slope change = control problem; parallel offset = constant plant bias), each with a different fix.
Applies when
- cleaning up hand-tuned offsets/trims in defaults or calibration
- a posture bias appears after a default or calibration change
- deciding whether a lean is a COM-sensitivity or constant-offset issue
“两者斜率几乎相同(≈0.026°/mm),v1 只是整体多后仰约 2.4° —— 不是质心敏感度问题,是恒定偏置。成因:旧 default 的踝俯仰 trim(−0.0489/+0.0628)本就预补偿了前后质心偏差,换成零位 default 后这份补偿没了 … v0 在真机质心处(−22 mm)的 sim 预测为 −2.31° 后仰,真机实测 2.2~3.1° 后仰 —— 预测精准命中。”
train/RETRAIN_v2.md § 4b. stand_v1 独立验证结果 / 4c. stand_v1b 验收结果 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) Deployment power derating damages non-forward axes far more than forward - sweep it in sim before deploying
power-scale-hurts-nonforward-axesTreat deployment power/torque scaling as a plant parameter: evaluate the policy in sim at the exact deployment scale, expect non-dominant axes to degrade first under derating, and either deploy at the training power or train with power randomization.
Symptom
Policies deployed at power-scale 0.8 (a safety derating of commanded torque) looked fine walking forward but were weak at backward and turning, inviting the wrong diagnosis "the skill was not trained well".
Context
Measured repeatedly: on s1e, going 1.0 -> 0.8 cost forward 18% but backward 58%; on C4-ff800, turn tracking was +25%/+40% at pw0.8 vs +75%/+58% at pw1.0, backward 51-52% vs 97-103%, while forward stayed 96-98% at both. Sim evaluation numbers in the plan were all pw1.0, but the robot was being run at 0.8.
Change
Pre-deploy protocol added: sweep the exported policy across power in sim (for PW in 0.8 0.9 1.0: eval_c_matrix --power $PW --seeds 20) and deploy at the first level where both turn directions reach >=50%. For C4 the recommendation was raise the robot to pw1.0 - the sweep showed it nearly free (saturation 47%->33%, left foot-clipping danger zone 25%->6%, cost only tilt 6.7->8.3 deg).
Outcome
Turning "weakness" resolved without any retraining; the sim sweep correctly predicted the real-robot signature at both power levels.
Mechanism
Forward walking is the reward-dominant, torque-cheapest skill with the most margin; backward/turn/sidewalk live closer to the torque envelope, so a uniform torque derating consumes their margin first. Training ran at power 1.0 (the trainer does no power scaling), so deploying at 0.8 is a systematic underactuation the policy never experienced.
Applies when
- deploying with any torque/power derating or safety scale
- secondary skills (backward, turn, lateral) underperform on hardware while forward walking looks fine
- choosing the deployment power level for a new policy
“power 衰减对非前进轴的伤害远大于前进轴（s1e：前进 1.0→0.8 掉 18%，后退掉 58%）。转向是非前进轴，0.8 下很可能明显跟不动。”
train/C_LADDER_RUN.md § 3c. A-2 上机前先定部署力度档 / 3p. 二 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 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 Order hardware runs by sim risk, gate each stage on the last, and put the fragile cell last with a spotter
risk-ordered-real-deploymentScript hardware sessions as a risk ladder: baseline first, sim-riskiest last with a spotter, suspended smoke before ground, each stage gated on the previous, environment (floor mu) recorded as a selection input - and stop at the stage that misbehaves.
Symptom
Five policy-x-gain combinations had to go on hardware in one session, with sim survival ranging from 20/20 down to 17/20 (and zero-command survival down to 2/20) - an unordered session risks breaking the robot on an avoidable run.
Context
The execution sheet fixed the order as sim-risk low to high, control baseline first (current SOTA establishes the floor reference), the fragile cell (fric-2400@kd1.0) last with a person spotting throughout. Stage gating: suspended smoke (feet off ground, 10 s each, all five pass before anything touches down) -> suspended with IMU and forward command (gait forms in the air) -> grounded runs -> speed raise only for combos that survived the previous stage -> zero-command tests only with a spotter, ordered by sim zero-cmd survival, with the 2/20 cell skipped by default. Preconditions include recording the floor material and estimating mu (if mu <~0.6, sim says pick the kd1.2 gain as main), port/CAN self-check, calibration frozen. Any stage failing stops the session at that stage: "任一段出问题就停在那一段, 不要跳到下一段".
Change
Session structured as a risk ladder with per-stage gates instead of a flat checklist; per-combo sim survival numbers written into the run table as the ordering key.
Outcome
The session design localized any failure to the cheapest stage that could reveal it, kept the robot safe for the informative fragile run, and made the control baseline available before any comparison run.
Mechanism
Hardware sessions consume a shared budget (robot integrity, battery, floor time); ordering by predicted risk means information is bought cheapest-first, and stage gates convert an expensive failure into a cheap earlier one. Baselines run first because every later reading is relative to them.
Applies when
- taking multiple policies/configs to hardware in one session
- a candidate is known-fragile in sim but must be measured
- writing a deployment runbook for a new robot
“跑序 = sim 风险从低到高, 最险的放最后 (依据 = 存活门/零指令存活) … ⑤ 是 sim 里最脆的一格 … 放最后跑, 全程留人扶, 起步即给 cmd, 零指令不做。… 任一段出问题就停在那一段, 不要跳到下一段。”
train/REAL_RUN_S2.md § 上机名单 / 全部命令 A soft joint-limit penalty charged the standing pose itself - the geometric-zero knee sat on its hard limit, so stand_v1 bent its knees to dodge 0.419 per step and leaned 4.1 deg forward; excluding the knee gave 0.24 deg
soft-limit-penalty-charges-nominal-poseBefore training, evaluate every penalty at the nominal pose; if a joint's soft limit sits inside the pose the task requires (a straight knee on its hard stop), exclude that joint from the soft-limit penalty and let the action clip enforce the hard limit.
Symptom
stand_v1 (retrained after the default pose moved to the CAD geometric zero and mirror augmentation was added) fixed left/right asymmetry (6.8 -> 0.0 deg) but settled at a 4.1 deg forward lean, where pure PD at the same default settled at 0.1 deg - the policy was actively pushing itself forward, which is exactly the real robot's failure direction.
Context
soft_joint_pos_limit_factor = 0.9 shrank the knee's soft limit to -/+0.1047 rad, while the geometric-zero default has the knee at q = 0, exactly on the hard limit. Standing in the nominal pose therefore paid 0.2094 x 2.0 = 0.419 per step in dof_pos_limits (alive earned only 0.5). The policy's way out was to bend the knees to -/+0.1013 rad, and the cost was the forward lean.
Change
stand_v1b: the knees excluded from dof_pos_limits (a straight knee IS the standing pose; the hard limit is still enforced by the action clip). No other change.
Outcome
stand_v1b: max tilt 0.3 deg, steady tilt 0.24 deg, asymmetry 0.1 deg, height 0.384 m - exactly nominal - with knees at -0.0007 / +0.0005 rad. It became the standing release used on the real robot, and later the standing side of the recovery switch. The same exclusion was carried into the recovery contract (knee at the clip in the standing pose) and the one-leg reward table.
Mechanism
A limit penalty whose soft boundary lies inside the nominal pose turns the nominal into a taxed state, and the policy buys its way out with whatever posture change is cheapest - here a knee bend paid for with lean.
Applies when
- a standing or default pose has a joint at or near its hard limit
- a policy settles in a small steady tilt that pure PD does not show
- soft-limit factors shrink limits uniformly across joints
“`soft_joint_pos_limit_factor=0.9` 把膝软限位内缩到 ∓0.1047,而几何零位 default **膝盖 q=0 正好压在硬限位上** ⇒ 站在标称姿态每步白扣 `0.2094 × 2.0 = 0.419` (alive 才 +0.5)。策略只能屈膝到 ∓0.1013 躲罚,代价是躯干前倾 —— 恰好是真机的 失效方向。 … 修掉"软限位罚标称姿态"后重训(`dof_pos_limits` 排除膝盖)。**前倾问题彻底消失** … 不再屈膝躲惩罚,高度正好落回标称 0.3840。”
train/README.md § 三期: 镜像对称增强 + 站立 v1 (2026-07-28) / stand_v1b (2026-07-28): 站立定版 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) Train with self-collisions ON (filtering nested-link ghost pairs) - the reward wall prevents, the physics makes cheating impossible
self-collision-physics-plus-reward-wallNever train a contact-risk behavior with self-collisions disabled; enable them with an audited filter list for nested/overlapping pairs (zero contacts across a pose sweep), record the fps cost, and keep a calibrated distance penalty as the preventive layer on top.
Symptom
walk_v8 logged 107 frames of leg-on-leg contact while still earning 0.751 tracking score - because training-side self-collisions were OFF, leg clipping was literally imperceptible to the policy ("碰腿在训练里 根本感知不到").
Context
Enabling self-collisions naively is its own trap: an Isaac audit had shown PhysX auto-filters adjacent bodies (base-hip clean for free) but nested links generate ghost forces - calf and ankle_roll overlap 65 mm at the zero pose, producing 12x body-weight phantom forces. The v10 recipe: enable self-collisions, explicitly filter only the two nested pairs (l/r calf-ankle_roll), then run a zero-contact audit at three poses (nominal stand, walk crouch, swing-extreme) requiring contact count = 0, adding any residual pair to the filter and re-auditing; a 500-iter sanity run for NaN and an fps-cost record (measured -8.8%). Redundancy with the reward-side foot-distance wall was argued, not assumed: "N2 离得远(奖励侧预防),SC 碰了疼(物理侧兜底)" - the reward keeps distance at range, the physics makes contact hurt - so the v8-style "clip legs and still score" outcome becomes physically impossible.
Change
enabled_self_collisions=True + 2-pair filter + three-pose zero-contact audit (re-verified at 0.00 N after the later mass update) + fps budget recorded.
Outcome
Leg contact entered the training signal; the audit protocol caught the nested-pair ghost-force hazard before it corrupted training; combined with the calibrated distance wall, later versions held contact = 0 on hardware and in sim.
Mechanism
A hazard absent from the training physics cannot be learned about, no matter the reward; but collision meshes that interpenetrate at rest inject large fictitious forces if enabled blindly. Filtered enabling plus a pose-swept zero-contact audit gives true contact physics with no phantom energy - and layering prevention (reward) with consequence (physics) covers both learning and enforcement.
Applies when
- real robot self-contacts while training scored it healthy
- enabling self-collisions on a model with nested collision meshes
- deciding between reward-side and physics-side fixes for clipping
“PhysX 自动过滤相邻体(base↔hip_pitch 免费干净),幽灵力只在 calf↔ankle_roll(零位嵌套 65mm,12 倍体重)。… 与 N2 互补不冗余:N2 离得远(奖励侧预防),SC 碰了疼(物理侧兜底)—— v8 那种 107 帧互碰拿 0.751 跟踪分的事从此物理上不可能。”
train/WALK_V10_SPEC.md § 4. SC —— 训练侧自碰撞(范围已探明,比想象便宜) The trainer read a stale USD after the URDF mass update - regenerate derived assets and gate on an automated equality instrument
derived-asset-staleness-checkFor every derived plant artifact (USD from URDF, generated value files), pair the generation step with an automated source-vs-derived equality instrument, prove the instrument can fail, gate training on its PASS, and re-run physical audits after every regeneration.
Symptom
Measured link masses had been committed to URDF/MJCF (total 9.58 -> 9.792 kg, weighed values), but Isaac reads the derived USD asset - which still carried the old masses: a silent 2.2% mass fork between the training plant and the evaluation plant.
Context
The v12 checklist made USD regeneration a hard precondition ("硬性 前置") and, crucially, backed it with an instrument: check_usd_mass.py compares USD vs URDF per-link mass AND inertia trace, validated by showing it FAILs the stale asset naming 9 offending links, then PASSes after re-conversion (13/13 links consistent, total 9.7920). The self-collision filter audit was re-run after the regeneration (three poses, 0.00 N) because a regenerated asset invalidates physical audits done on the old one. This milestone was also where the three plants first aligned: "三边 plant 首次对齐(armature+摩擦+ 实称质量)就在这一代".
Change
convert_urdf re-run on the training machine, regenerated asset committed, check_usd_mass.py PASS required as an acceptance gate for the generation; dependent audits repeated post-regeneration.
Outcome
The 2.2% plant fork was closed before it could distort a generation's acceptance numbers; the staleness class of bug now has a permanent detector instead of a memory.
Mechanism
Source-of-truth edits do not propagate to derived binary assets by themselves; any consumer reading the derivative silently trains or evaluates on the old plant. An automated equality check between source and derivative - proven able to fail - turns an invisible staleness into a red gate, and regeneration invalidates every audit performed on the old artifact.
Applies when
- editing masses/inertia/geometry in URDF or MJCF sources
- a trainer or evaluator consumes converted/derived assets
- plant numbers differ between simulators for no visible reason
“25ba997 把连杆质量更新为实称值(总重 9.58→9.792 kg,URDF/MJCF 已改),但 Isaac 读的是 train/assets/laika_v2.usd —— 仍是旧质量。… 否则 Isaac(9.58)与 MuJoCo(9.79)质量分叉 2.2%,v12 验收数字失真。验收门:python tools/check_usd_mass.py 必须 PASS … 对旧资产实测 FAIL/9 连杆点名,仪器已验证”
train/WALK_V12_SPEC.md § 7. 核查单 (⚠️ 先重转 USD) 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） 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) Multiple changes may share one rung only if their symptom spaces are orthogonal - with the ablation order written in advance
orthogonal-batch-with-ablation-orderBatch changes into one rung only when you can name each change's private symptom space in writing; pre-register the ablation order (numeric before structural) and per-change escalation plans, so a mixed outcome decomposes without new decisions.
Symptom
v8 needed four repairs at once (saturation cheating, landing impact, leg narrowing, heading alignment) - strict one-variable laddering would have cost four training cycles for wounds that were all already diagnosed.
Context
The batch was allowed because each change owns a disjoint symptom space, stated explicitly: "A→形态/饱和, B→落地/步态高度, C→腿距/roll 摇摆, D→偏航/转向" - so a single run can attribute each outcome to its change by which symptom moved. For the failure case, the ablation order was pre-registered (A2 -> A1 -> B -> D -> C, "先撤数 值改动" - retract numeric tweaks before structural ones), and every change carried its own escalation/rollback plan (e.g. A insufficient: joint_pos_ref 1.6->2.0 or widen the exp kernel; B overdone - robot afraid to land: v_ok 0.30->0.45; D unstable: rel 0.5->0.25, not back to 0).
Change
Four-change rung executed as one run with per-change symptom ownership, per-change contingency plans, and a pre-registered global ablation order for unattributable regressions.
Outcome
The rung retained single-run attributability without paying 4x training cost; the contingency table meant no failure mode would require improvising an ablation under pressure.
Mechanism
The one-variable rule exists to keep attribution possible, not as an end in itself; attribution survives batching exactly when the changes' observable effects are separable. Orthogonality is a claim that must be argued per pair in advance - and the pre-registered ablation order is the escape hatch for the case the claim fails.
Applies when
- several diagnosed fixes are queued and ladder time is scarce
- deciding between strict laddering and a combined rung
- a combined rung shows a regression no single change explains
“四个改动症状空间基本正交,可单 run 归因:A→形态/饱和,B→落地/步态高度,C→腿距/roll 摇摆,D→偏航/转向。出现无法归因的整体退化时消融顺序 A2→A1→B→D→C(先撤数值改动)。”
train/WALK_V8_SPEC.md § 8. 风险与归因 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) Model CAN polling skew - joint observations are 6-9 ms stale by read order
can-timing-skew-modelingIf joints are read sequentially over a shared bus, reproduce the per-group observation staleness in sim (or randomize it over the measured range) - synchronous observations are a privileged fiction.
Symptom
Policies trained with synchronous joint observations degrade on hardware where motors are polled sequentially over CAN - hip data is already 6-9 ms old by the time ankle data arrives.
Context
Menlo's core sim2real finding on a leg platform of almost identical mass to Lucen's. CAN is a sequential bus: one poll cycle reads motors in a fixed order, so the observation vector mixes timestamps. Lucen runs 6:6 motors on a dual CAN split, so the problem transfers one-to-one.
Change
Explicitly model CAN timing skew in sim: give the joint groups different observation delays matching physical read order. Menlo went further - running real firmware in the loop with a motor simulator between MuJoCo and firmware injecting 0.4-2 ms uniformly distributed delay.
Outcome
Reported by the reference team as their core sim2real enabler on a same-scale platform; recorded in Lucen's experience log as directly applicable ("这个问题一模一样").
Mechanism
A policy exploits any cross-joint temporal coherence present in training observations; when hardware breaks that coherence per bus position, the learned feedback acts on inconsistent state estimates, injecting phase error exactly at the control bandwidth.
Conflicts
Second-hand episode: outcome numbers are the reference team's report, not a Lucen-run experiment; Lucen adopted the requirement but the corpus has no Lucen A/B of skew-on vs skew-off.
Applies when
- robot polls actuators sequentially over CAN/RS485 or similar shared bus
- sim2real degradation appears as jitter or oscillation not seen in sim
- designing the observation/delay model before a training run
“电机走 CAN 是顺序轮询的，髋部电机的数据到踝部电机上报时已经陈旧了 6-9 ms，他们直接在仿真里显式建模了 CAN 时序偏斜，按读取顺序给三组关节不同的观测延迟。… 注入 0.4–2 ms 的均匀分布延迟。你们是 6:6 双 CAN 分总线，这个问题一模一样”
Experience.md § 执行器 + 时序建模 (line 6) A proposal in the runbook - torque and action limits as versioned safety tiers (classroom / research / expert) written to motor RAM and read back, separate from the reward's effort penalty - recorded as a proposal, its implementation unrecorded
safety-limits-are-a-layer-not-a-rewardKeep hardware limits as an explicit, versioned safety layer (tiers written and read back at start, the persisted default the safest one) and the effort penalty as a behaviour layer; when a skill needs more torque, change tier deliberately rather than trading one layer against the other.
Symptom
Running and jumping need more torque than the deployed limits allow, and the temptation is to trade the training-side effort penalty against the hardware limit, or to hand a new user a robot "tuned however the last person left it".
Context
A message pasted into the operator runbook (undated, citing Berkeley's practice of storing the full motor configuration as JSON with write and read-back scripts) proposes: configuration is a versioned artifact, not a verbal agreement; three safety tiers in robot.yaml beside the gain and policy profiles - classroom (RS06 limited to 10 N*m, lateral joints clamped: "however bad the policy, it only moves awkwardly"), research (14 N*m, clamps at twice the measured need, the default) and expert (the 36 N*m rating, joint limits only, requiring an explicit flag); deploy writes the tier to motor RAM at start and reads it back, while the stored copy stays classroom so a power cut returns to the safest state. It frames limits as the safety layer and the effort penalty as the behaviour layer - more torque for running means switching tier, not weakening the penalty.
Change
None recorded: the message ends by asking which to do first, a rollback or the tiers.
Outcome
The sources do not record the tiers being implemented; the deployed limits stayed at 12/17/11 N*m through the recovery and one-leg lines (the one-leg spec treats raising the RS06 limit as a separate, unapproved hardware decision). Related and recorded elsewhere: torque limits were written to RAM only in a scripted, self-reversing field experiment.
Mechanism
Hardware limits bound the damage any policy can do; reward terms shape what a policy prefers. Mixing them either weakens safety to buy behaviour or distorts behaviour to buy safety.
Applies when
- a new skill needs more torque than the deployed limits
- robots are handed to students or new users
- motor configuration lives in people's heads or in the firmware only
“配置是版本化的产物,不是口头约定。 … deploy_policy 启动时按档写进电机 RAM 并读回校验(落盘的那份永远保持 classroom,断电自动回到最安全状态)。 … 限幅是安全层,dof_torques_l2 是行为塑造层,它们在不同的层,不冲突。跑步要更大力矩就换档,而不是去动训练里的省力惩罚。”
RL系统/FOLLOW THIS copy 2.md § 面向 developer / 教育机构该怎么做 (pasted proposal, undated) Add a termination that makes the degenerate strategy fatal - no height cut-off meant crouch-shuffling could live forever
termination-closes-degenerate-basinFor each known degenerate strategy, check whether the termination set makes it fatal; if the robot can live indefinitely inside the degenerate posture, add a termination just past the intended operating envelope rather than escalating penalties.
Symptom
Crouched foot-dragging survived indefinitely because the termination set contained only bad_orientation (40 deg) and base contact - there was no height termination at all, so a deep squat was a viable long-term strategy.
Context
The hypothesis audit found the missing termination (hypothesis 7); the cross-check against published configs found the field practice: Booster terminates at 0.45 m (38% of body height) and the research warning is that the termination height must not be so low that crouching survives it. The proposed value: 0.32 m, just below the walk crouch base height 0.3739 - a deep squat terminates immediately, "断掉蹲着蹭的活路" (cutting off the crouch-shuffle's livelihood).
Change
Add height termination at 0.32 m as a second-priority item of the walk fix package, alongside restoring base_height_l2 to -10.
Outcome
Entered the v5/v6 fix package under which the crouch-shuffle optimum disappeared (34 mm clearance, 87% tracking by v6).
Mechanism
Termination conditions define which strategies exist at all: a reward penalty prices a behavior, but a termination deletes its future returns entirely. Degenerate basins that are merely penalized can remain optimal under enough tracking pressure; a termination placed between the degenerate posture and the intended one makes the basin unreachable as a steady state.
Applies when
- a degenerate but stable behavior persists across reward tunings
- auditing termination conditions for a locomotion task
- a policy exploits the gap between penalized and terminated states
“加终止高度:研究第 6 条"终止高度不能低到让蹲着也能活"。我们完全没有高度终止。建议 0.32 m(略低于 walk 蹲姿基座高 0.3739,深蹲即终止)。… 加终止高度 0.32 m(深蹲即终止,断掉蹲着蹭的活路)”
train/WALK_DIAGNOSIS.md § 修正 ④ / 最终改动清单 第二优先 A ratio metric flipped the verdict - spectral share rose while absolute high-frequency energy fell 16%
ratio-metrics-need-absolute-checkNever compare share/percentage/centroid metrics across conditions whose totals differ; pair every ratio with its absolute numerator before issuing a verdict, and log retracted judgments so they are not re-derived.
Symptom
walk_v6 was provisionally judged "more jittery" than v5 because the joint-velocity spectral centroid rose 2.91 -> 3.62 Hz and the >4 Hz energy share rose 12.6% -> 17.8%.
Context
Absolute measures said the opposite: first differences of actions fell 1.54 -> 1.31, second differences 2.55 -> 2.19, and absolute high-frequency energy fell 16% (0.490 -> 0.413). The shares and centroid rose only because low-frequency content fell even more - the denominator shrank. The interim judgment was retracted in writing so it would not be reused.
Change
Metric discipline noted: "占比类指标在总量变化时不能直接比较" - share/ratio metrics are not comparable across conditions when the total changes; verdicts about smoothness must cite absolute energies or difference norms.
Outcome
v6 correctly classified as smoother, not jitterier; the retracted judgment logged under "被推翻的一个中间判断(记下来免得复用)".
Mechanism
A ratio confounds numerator and denominator; any intervention that removes low-frequency content raises every high-frequency share without adding a single joule of jitter. Only absolute quantities support cross-condition comparison when totals move.
Applies when
- comparing smoothness/jitter/spectral metrics across versions
- any percentage-based metric moves after an intervention
- writing an eval report that includes normalized quantities
“我一度说"v6 动作更抖" … 错了: 动作一阶差 1.54→1.31、二阶差 2.55→2.19 都在降 … 谱质心升高只是因为低频成分掉得更多, 绝对高频能量实际下降 16%(0.490→0.413)。占比类指标在总量变化时不能直接比较。”
train/WALK_DIAGNOSIS.md § 过程中被推翻的一个中间判断(记下来免得复用) 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 The latency DR range must cover the measured deployment pipeline - 0-20 ms could not even reach the real 1-2 control steps
latency-dr-covers-measured-pipelineMeasure end-to-end action latency in control steps on your own stack (including cross-process queue boundaries), set the DR range to cover it with margin, and never import a delay count without its control frequency.
Symptom
Action latency was randomized over 0-20 ms (0-1 control step at 50 Hz), but the measured deployment path is 1-2 steps: the deploy process writes the target, an independently running BusWorker picks it up on its NEXT cycle, plus CAN round-trip - the training range could not cover the robot's actual latency at all.
Context
Fix: widen action_latency_s to 0-0.06 (0-3 steps). The external reference's "uniform 6 steps" was explicitly NOT copied - that number depends on his unknown control frequency; locally, a sweep at 0/1/2/3 steps showed walk_v5 survives all with insensitive metrics, so 6 steps "在我们这里没有依据" (has no local basis). The range was set from the measured pipeline with margin, not from a foreign constant.
Change
action_latency_s (0, 0.02) -> (0, 0.06), justified by pipeline analysis (writer/worker cycle boundary + bus time) and bounded by the local latency sweep.
Outcome
The DR band now brackets the true deployment latency; the policy trains against the delay it will actually face instead of a fictional sub-step world.
Mechanism
Latency DR only immunizes against delays inside its support; a range below the physical pipeline guarantees an untrained distribution shift at deployment. The correct range comes from tracing the pipeline's worst case (queueing boundaries + transport), and foreign step-counts are meaningless without the control rate they were measured at.
Applies when
- setting or auditing action-delay randomization
- deployment uses a separate bus/worker process from the policy loop
- importing delay-modeling numbers from other projects
“现行 0~20 ms = 0~1 个 50Hz 控制步, 而实测部署链路是 1~2 步(deploy 写 STATE.target 后, 独立跑的 BusWorker 下一轮才取走下发, 再加 CAN 往返)——现在的区间覆盖不到真机的实际延迟。… 不照抄参考来源的"统一 6 步": 那取决于他的控制频率(未知), 而我们扫过 0/1/2/3 步 … 6 步在我们这里没有依据。”
train/WALK_V7_SPEC.md § ⑤ action_latency_s 0~0.02 → 0~0.06 Reward fixes come in causal chains - foot height, then landing impact, then foot spacing
reward-chain-foot-height-landing-spacingPlan reward shaping as a chain, not a point fix: when you patch a degenerate gait behavior, pre-register which adjacent behavior the optimizer will exploit next and watch for it.
Symptom
Three problems appeared strictly in sequence: (1) swing feet lifted too low; (2) after fixing that, feet slammed down - "实际比视频里暴力得多" (far more violent in person than on video); (3) after fixing that, feet drifted too close together and collided.
Context
Each reward fix removed one degenerate optimum and exposed the next. The fix for foot spacing (COM lateral randomization +/-5 cm to force leg spread) itself caused base side-to-side sway, requiring a further foot-to-centerline distance penalty. Lucen had just solved its own foot-height problem (19mm -> 40mm swing height) and logged landing impact and foot spacing as the predicted next two problems.
Change
Chain of additions - (1) penalty when swing foot below 5 cm; (2) landing vertical-velocity penalty at touchdown; (3) COM lateral randomization +/-5 cm, then foot-centerline distance penalty to cancel the induced sway.
Outcome
Reference robot progressed through each stage; each individual fix worked and predictably surfaced the successor problem. For Lucen the chain served as a pre-registered roadmap of what breaks next.
Mechanism
Locomotion rewards are coupled through contact dynamics: raising swing height adds potential energy that must go somewhere at touchdown (impact); penalizing impact and forcing robustness to COM shifts changes lateral support strategy (spacing/sway). The optimizer always exploits the cheapest unpenalized channel, so fixing one channel routes the exploit to its neighbor.
Applies when
- adding a foot-height / clearance reward
- feet slam or landing impact grows after a clearance fix
- feet converge toward the centerline or self-collide
- any single-reward fix to a coupled gait behavior
“抬脚太低 → 加惩罚：摆动足低于 5 cm 就扣分 / 加完之后砸脚 → 抬起来了但落地极猛，"实际比视频里暴力得多" → 加落地速度惩罚 … / 两脚太近甚至互撞 → 先试质心横向随机化 ±5 cm … 有效但引发新问题——基座开始左右摇摆 → 再加足-中心线距离惩罚 … 这三条是串联的：每个修复都会暴露下一个问题。”
Experience.md § 三个问题的解法链 (lines 72-77)