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
169 cards matching “prone-dead-end-is-foot-placement”.
Prone get-up sat at 0/159 until two gated hinge terms moved the seated feet - first sideways (561 -> 360 mm), then fore-aft (-168 -> +56 mm) - and success went to 158/159 with nothing else changed
prone-dead-end-is-foot-placementWhen a stuck state and a successful state differ geometrically, penalize the discriminating quantity with a gated hinge that is exactly zero in the state the policy actually reaches (measure it - not the nominal), then re-probe: flattening one axis can move the discriminant to another.
Symptom
Prone falls always righted and then sat with the feet splayed wide or tucked behind the hips, from where the policy never stood (0% for four generations).
Context
Four lines of evidence pointed at foot position: the configuration probe (ankles 215 mm apart stood 52.3%, 561 mm apart 0.0%); FK showing the action contract's nominal (a = 0) is itself a 465 mm straddle, so the action_rate and still terms were pulling toward the splits; biomechanics (feet tucked under the body cut peak hip-extension torque 148.8 -> 32.7 N*m, -78%); and HoST's foot-displacement term, which this reward table lacked. The earlier "not a reward hole" reading was corrected to "a gradient hole, not a level hole": at the dead point the heaviest term (upright) was saturated with zero gradient, still paid for not moving, and the one live gradient (base_height) pointed at the thigh-horizontal torque barrier. A prone ROM scan had already ruled out pushing up from prone.
Change
R0.4: feet_spread_excess = clamp(ankle distance - 0.215, 0, inf) x upright gate, weight -2.0, plus a height-decay factor added after measuring that the policy's real standing stance was 406 mm, not the 215 mm nominal (the plain version would have taxed every successful stand 0.38/s). R0.5: the same shape on the fore-aft axis, feet_fore_seated = |fore-aft offset - 0.05| x upright gate x height decay, target +50 mm (the measured natural offset of standing postures). One variable per rung.
Outcome
R0.4: seated ankle distance 561 -> 360 mm, supine/side exactly unchanged, prone 0 -> 1.9%, mid 45.9 -> 62.2%; a probe then showed the discriminant had moved to the fore-aft axis (standing starts +42 to +51 mm, the prone seat -168 mm). R0.5: supine 99.4, prone 99.4, side 100, mid 100%, re-falls 0%; both geometry terms collapsed to ~0 near iteration 13,100 as base_height rose, and the prone fore-aft offset went -168 -> +56 mm - the term's own target, closing the causal chain. The cost, unmeasured at the time: action jitter rose 33% (sum |da|^2 6.82 -> 9.06).
Mechanism
An upright-gated hinge is inert while the robot rolls and exactly zero in the achieved stance, so it adds gradient only inside the stuck basin; a seated robot with its feet behind or outside its COM must make a kinematically unfavourable transition to stand, and moving the feet under the body removes it.
Applies when
- a get-up or transition skill fails from one start category only
- successful and failed episodes differ in a measurable geometric quantity
- a shaping term might tax the posture successful episodes already use
“`recovery_r0_5`,唯一变量 = 追加 `feet_fore_seated`(与 R0.4 同形状,只换测量轴)。 … 对照 R0.4 的 prone(3.1%,360 mm,**−168 mm**):前后偏移从 −168 走到 +56, 正是这一项的目标量,**判别量被消掉后成功率随之到顶** —— 因果链完整。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §20 R0.5(前后向脚位置项):成功率门全过 —— prone 0/159 → 158/159 Record where every failed episode ends - an end-state confusion matrix showed all failures finishing seated and overturned a "cannot roll over" diagnosis that per-category success rates hide by construction
end-state-confusion-matrixFor any multi-category acceptance, report where each failed episode ends, not only which category it started in; it costs a few lines and no extra simulation, and it separates "cannot reach the goal" from "reaches the wrong basin".
Symptom
Prone scored 0% for three generations; the working diagnosis was "prone lacks the roll-over skill", and R0.3 spent a run adding prone-to-side roll-arc start states. It bought nothing: the 45-deg roll band itself only moved from 24.2% to 26.6% after 3,000 iterations.
Context
Acceptance reported success per starting category. A final-state table (lying prone / on the side / supine / seated / standing for every failed episode) was added to accept_recovery.py at R0.3.
Change
The confusion matrix became a permanent part of the acceptance output, and the prone diagnosis was rewritten from it.
Outcome
The prone, side and supine columns were all zero - every failure ended seated - and prone had righted its torso in 159/159 episodes (tilt under 30 deg in 100%). The missing ability was standing up from one specific seated configuration, not rolling over, which redirected the next rungs to foot placement and to a configuration probe.
Mechanism
Per-category success rates collapse "reached the wrong basin" and "never reached anything" into the same zero; the end state separates them.
Applies when
- a category sits at 0% and the diagnosis rests on its label
- recovery, manipulation or navigation tasks with distinct terminal states
- an intervention aimed at the presumed cause shows no effect
“**① 末态混淆矩阵 —— 固化(已在 `accept_recovery.py`)。** 它给出的 "趴/侧躺/仰躺三列全 0、所有失败都终于坐姿"是本线最改变决策的一个事实, 而**逐类成功率按构造看不见它**。 … 成本十来行、零额外仿真。 … **② prone 病因更正(旧诊断作废)。** 旧:"缺翻身"。新:**prone 159/159 全部 把躯干翻正**”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §15 R0.3 判决 + 三件事的判断 A pull-assist curriculum keyed to a global success share was satisfied by the categories that already worked and withdrew before prone learned anything - conditioning the criterion on prone took it from 2.5% to 98.7%
curriculum-criterion-conditioned-on-lagging-categoryMeasure a curriculum's advancement criterion on the population the scaffold is meant to help; a global success share is met by whatever already works, and the help is withdrawn before the lagging case learns.
Symptom
Under the beta-anchored action, supine and side stood reliably while prone still sat (1.3%). A pull-assist curriculum added to help it was withdrawn completely within ~790 iterations and prone moved only to 2.5% (noise).
Context
The pull assist follows HoST: an upward force on the base, active only when the torso is within 30 deg of vertical, scaled by body weight (HoST's 200 N on G1 = 0.583 BW -> 56 N here, steps of 5.6 N, ten levels to zero); the product must pass with no assist. It had already taught sit-to-stand in V1. In V2.1 its advancement criterion was the standing-time share over all envs (threshold raised to 0.55 because the share was already ~0.53).
Change
V2.2: PullAssistForce with gate_category = "prone" - only envs whose first step classifies them as prone count toward the criterion - and the threshold back at 0.35. A feasibility signal was pre-registered: if prone's share stayed near zero under the full 56 N, return to the roll-over path instead of adding force.
Outcome
The prone-conditioned curriculum withdrew level by level only as prone itself passed: prone 98.7%, and the four-category gate passed for the first time on the line (98.6% overall, re-falls 0%, torque gate PASS).
Mechanism
A pooled success share is filled by the categories that already succeed (supine/side ~53%), so the scaffold is removed on their account before the lagging category has used it.
Applies when
- an assist, guide force or easier setting is withdrawn by a success threshold
- one task category lags while the pooled metric looks healthy
- a curriculum ran to completion without changing the lagging category
“**教训:全局站立占比阈会被存量类别(supine/side ~53%)凑够,拉力在 prone 学会前就撤光了 —— metric 设计失误,不是拉力机制失效**(它在 V1 教会过 坐→站)。 … **V2.2(已启动)**:`PullAssistForce` 加 `gate_category="prone"` —— 达标判据 只统计 prone 类 env”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §34 V2.1 判决(2026-08-10) Three times a joint-angle stand-in for a foot-level quantity was gamed or lied - the absolute ankle roll sold stance width to buy flat feet, a hip-roll criterion was confounded by 50 deg of yaw, and the joint table said 0.271 m where the feet were 0.159 m apart
joint-space-proxy-for-task-space-quantityExpress foot-level (task-space) goals and acceptance criteria in task space - link attitude and lateral spacing from world poses - never through joint angles that assume other joints are at zero, and measure the task-space value before trusting a joint-space estimate of it.
Symptom
The line's first real-robot get-up (v2_6, 2026-08-11) was "fairly stable", but after standing the feet were too close and the robot slid into the splits and fell several times; later reports added that it also got up through a split posture.
Context
(1) flat_feet penalized sum |q_ankle_roll|, but a flat foot is ankle roll compensating hip roll; the proxy taxed the compensated wide solution, and the untaxed combination was hips straight plus ankles at zero - flat and narrow. On hardware the lateral support shrank to hip width plus centimetres, lateral balance rested on 11 N*m ankle motors, and the feet slid apart. (2) The next rung's acceptance criterion "hip roll >= 20 deg" assumed zero hip yaw; at 50 deg of yaw the lateral contribution is x cos 50 ~ 0.64 - the same substitution again, inside a criterion. (3) A new task-space diagnostic reading the foot links' world poses measured v2_6c's stance at 0.159 m where the kinematic audit from joint angles had said 0.271 m (0.271 x cos 47 ~ 0.17).
Change
Rule written into the spec: task-space quantities are never expressed through joint-space proxies. V3.1's stance terms were all task-space: flat_feet_task from the foot links' world orientation, lateral foot spacing in metres, stand_pose stripped of both roll joints.
Outcome
From scratch with task-space terms (V3.1 P1c): lateral stance 0.355 m, foot residual tilt median 0 deg / P75 2.0 deg, all six criteria passing, mu 1.0-0.4 all 100%.
Mechanism
A joint proxy bundles the goal with everything else those joints do; the optimizer finds the combination the proxy does not tax, and a joint-based criterion silently assumes the other joints sit at their nominal.
Applies when
- rewarding flat feet, stance width, foot placement or end-effector pose
- an acceptance criterion is written in joint angles for a geometric goal
- joints with large yaw or coupled axes are involved
“**病根 = 关节空间代理**:`flat_feet` 罚 Σ|q_ankle_roll|(§41 取的简易口径)。 "脚掌平"的运动学正解是 **踝滚补偿髋滚**(q_ankle_roll ≈ −q_hip_roll) … 代理把"脚平"和"站距"绑死在一起卖了。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §43 真机首试入账(2026-08-11)判读:flat_feet 的代理口径错误 A get-up policy righted itself and sat - three terms paid the seated pose 84% of the return, and the only shaping term that could tell sitting from standing was an exp kernel outputting 5e-5
seated-basin-dead-exp-kernelWhen a policy parks in a degenerate posture, tabulate what each reward term pays that posture against the target (watch contact terms that reward touching rather than bearing load) and evaluate every exp kernel at the error actually observed; a kernel narrower than the real error is switched off, and widening it is a one-variable repair that adds nothing new.
Symptom
R0 converged by iteration 700 and gained 1.7% over the next 2,300; success 0.0% in all four fall categories. Three of four success conditions passed (tilt median 1.0 deg, both feet in contact 99.6%, angular rate low); height passed 0.6% (median 0.204 m against 0.326). The robot knelt in a W-sit: hip yaw +/-47 deg, knees folded to 92% of the hard limit, shins flat, pelvis on the ground, torso vertical.
Context
Minimal reward table: upright (1-g_z)/2 +2.0, base_height linear progress +1.5, stand_pose exp(-||q-q_stand||^2/std^2) x upright gate +1.0 with std 1.0, still +0.5 and feet_on_ground +0.5 both x the upright gate, plus regularizers. The upright gate is a hinge that opens below 30 deg of tilt. The pre-registered fallbacks were then checked against the measured state: tightening the tilt gate was falsified (tilt was already 1.0 deg); a success bonus contradicted the spec's own no-cliff-bounty rule; narrowing the categories was useless (all four converged to the same pose); raising init noise was too weak for a basin this deep. Only a half-rise intermediate state addressed it, and a cheaper repair existed.
Change
R0.1 (user decision, single variable): stand_pose std 1.0 -> 3.0. Not a new term and not a bounty - repairing a declared term that was numerically dead. The two runs' logged env.yaml differ in log_dir and std only.
Outcome
R0.1 58.2% overall (R0 0.0%): supine 91.5%, side 82.2%, mid 55.9%, prone 0/156; knees fully straight; ||q-q_stand||^2 9.99 -> 0.91 and the stand_pose term 4.6e-5 -> 0.90; get-up ~1 s, no re-falls, the curve still rising at the 3,000-iteration cap. Prone stayed at zero and needed a different fix (see prone-dead-end-is-foot-placement).
Mechanism
Sitting earned upright 1.98/2.0, still 0.43/0.5 and feet_on_ground 0.46/0.5 - 3.0 of a 3.57 per-second return - because feet_on_ground asked for contact, not load. The only terms separating sitting from standing were base_height (+0.70/s for standing) and stand_pose, whose kernel at the real 9.99 rad^2 error (75% of it in the two knees) was exp(-9.99) = 4.6e-5 with a gradient near 1e-4. Standing up meant risking 3.0/s to gain 0.70/s while unfolding knees at 92% of their limit under load. With std 3 the same term is exp(-9.99/9) = 0.33 - a live gradient, three quarters of it on the folded knees.
Applies when
- a policy converges early to an upright but low, seated or kneeling pose
- a posture-matching exp term reads ~0 in the training logs
- contact-based rewards saturate while the task metric does not move
“**关键:`feet_on_ground` 只问"触地"不问"承重", 跪坐时双脚确实贴地,照样满分。** 三项 3.0/s = 总回报 3.57/s 的 84%。 … **exp(−9.99) = 4.6e-5** —— 权重 1.0 的项实际输出 5e-5、梯度 ~1e-4, **不是"还没学会",是数值上根本不存在**。 … **R0.1 决定(用户 2026-08-09 定,单变量)**:`stand_pose` 的 `std` **1.0 → 3.0**。 不是加新奖励、不是悬崖悬赏,而是**修复一个已声明但数值失效的项**”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §11 R0 首跑(recovery_r0, 2026-08-09):FAIL —— 翻正了但坐着 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) Before training a one-leg stand the spec named the cheapest cheats - hopping on the support foot, a raised foot resting unloaded, a leg tripod - and gave each a countermeasure and a gate; one still appeared and was caught by exactly those gates
enumerate-cheapest-cheats-before-trainingBefore training, list the cheapest behaviours that would satisfy each reward term without doing the task, give each a countermeasure in the reward and a gate in acceptance, and prove the intended behaviour is reachable with a probe - then treat any gate the policy games as evidence about the reward, not the gate.
Symptom
The literature's single-leg benchmark reports eight state-of-the-art general policies holding a clean one-leg stand 0 times out of 90 - they survive by sneaking steps and hops - so the task's first adversary was the policy's own cheating.
Context
The spec's shape self-check ("what is the zero-cost option?") listed, for the one-foot bucket: the cheapest cheat, a foot resting on the ground without load, countered by a 5 N contact threshold plus positive swing income; the second cheapest, small hops on the support foot to reset balance, countered by a continuous support-air penalty plus a gate of zero support-foot flight segments. The probe that preceded training had already seen a third: early low-lift postures "survived" by pressing the swing foot at 78-95 N, a leg tripod, removed by folding the shank back. The two-foot bucket was checked too: its zero-cost behaviour is ordinary standing, with no odd base state.
Change
Countermeasures and gates written before training: swing-contact and support-air penalties, gate 2 (zero swing-foot contact frames above 5 N), gate 3 (zero support-foot flight segments).
Outcome
The first run still found the unloaded-foot cheat (a binary reward band gave it no gradient to lift) - and it was caught, by the contact gates and the cross-simulator comparison, not discovered on hardware. The retrained V0 passed all gates 40/40, including zero support-foot flight after the flight detector was corrected.
Mechanism
A policy optimizes the reward, not the intent; the cheapest behaviours that satisfy the reward are predictable from the reward's structure, and a gate written for each before training turns a silent cheat into a failed row.
Applies when
- designing rewards for balance, contact or "hold still" tasks
- benchmark policies are known to cheat the task
- writing acceptance gates for a new skill
“文献里 8 个 SOTA 通用策略在单脚站基准上 0/90 干净保持， 全靠偷步偷跳活命，这是本任务的第一反作弊对象 … 单脚桶下最便宜的作弊是"脚虚放地上不受力"——接触判定 >5 N 力阈（沿用），配 swing_height_band 正收入拉开。 … 第二便宜是"支撑脚小跳重置"——support_air_penalty 连续罚 + 验收门支撑脚腾空段=0 双保险。”
git:Lucen V2@origin/oneleg-line:train/ONELEG_V0_SPEC.md § §0 目标口径 / §5 形状自检(零成本选项是什么) With no term on foot attitude the robot stood on the edges of its feet (ankle roll -26 deg) from R0.5 to V2.5b; pricing it fixed that and turned stance width into the next free variable - the user saw both on video before any metric flagged them
unpriced-foot-attitude-is-a-free-variableList every posture quantity the hardware cares about (foot attitude, stance width, yaw, knee flexion) and make sure each is priced by a term with real gradient at the observed error; pricing one exposes the next, so re-inspect the feet-level video after every change.
Symptom
Watching the v2_5 video the user said the ankle roll after standing looked very strange - the feet were not flat. The feet view showed the right foot standing on its outer edge; median ankle roll at t = 8 s was -/+26 deg (limit +/-35), mirrored. This was the physical form of the ankle_roll saturation criterion that had failed since R0.5.
Context
No term priced foot attitude: feet_contact_upright counts an edge contact as contact, and stand_pose's wide exp kernel (sigma^2 = 9) gives almost no gradient at 0.45 rad. HoST carries an "ankle parallel" term (+20); this table never had one. Edge standing was made a blocking precondition for hardware (continuous ankle load, unstable contact, wear).
Change
V2.6 (single variable): flat_feet = (|q_l_ankle_roll| + |q_r_ankle_roll|) x upright gate x height gate, a linear hinge with a 5 deg margin, weight -2, continued from V2.5b.
Outcome
Ankle roll -/+26 -> 5.3/5.2 deg, ankle_roll saturation 6.6% (< 10%), feet flat on the feet-view video; success 99.6%, re-falls 0-1%. Then the user watched v2_6: hip yaw constantly tense and the legs very close together. The numbers: hip roll -/+4.9/4.8 deg against a nominal 25 - feet flat and hips open 25 deg cannot coexist without ankle compensation, the new term taxed that compensation, and nothing priced stance width. "Foot attitude as a free variable" was fixed and "stance width became the new free variable" - which the real robot then exposed as splits.
Mechanism
An optimizer spends every posture degree of freedom no term prices; closing one reallocates the slack to the next unpriced one.
Applies when
- a standing or landing posture looks wrong on video while gates pass
- a saturation criterion keeps failing on one joint
- a new posture term was just added
“用户看 v2_5 视频:"起身之后 ankle_roll 非常奇怪,脚根本不是平着站立"。 … 机理:奖励表**无任何脚掌姿态项** —— feet_contact_upright 边缘接触也算触地, stand_pose 的 exp 核(σ²=9)对 26°=0.45 rad 梯度≈0。脚掌姿态是自由变量。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §41 预注册 V2.6:flat_feet —— ⑥ 老账的物理形态被用户目视锁定 The real robot's right-leg kicking was over-trained-delay times loop gain - irreducible pipeline latency is plant, model it fully from day one
pipeline-latency-is-plant-not-drMeasure the end-to-end action pipeline delay and build it into the nominal plant and every acceptance gate from day one; treat power/scale deratings that "fix" oscillation as gain-reduction crutches flagging an unmodeled delay, and expect higher-feedback-gain policies to be MORE delay-fragile.
Symptom
On hardware, s1c/s1d at action scale 1.0 always kicked wildly with the right leg (s1c only ran as SOTA at power 0.8; s1d only at 0.7) - while sim showed nothing under default evaluation.
Context
Sim reproduced the incident item by item once the real pipeline delay was injected: s1d@1.0 with --delay 1 fell at 10.2 s, --delay 2 at 5.2 s; s1c@1.0 stressed (r_hip_roll saturation 5 -> 16%; "右脚" = the policy's chirality makes the right leg its high-gain leg); and the combos that worked on hardware (s1c@0.8+delay2, s1d@0.7+delay2) all survived in sim. Mechanism: the real pipeline is ~1-2 ticks (BusWorker next-cycle pickup + CAN round trip) but S1.1 trained only to 1 tick - "超训延迟 × 全环路增益 = 振荡;衰减 = 压环路增益换稳定" (delay beyond training x full loop gain = oscillation; the power derating had been buying stability by compressing loop gain). s1d was MORE fragile than s1c because its yaw 3-layer stack had learned higher feedback gain - higher gain, lower delay tolerance. Three changes: latency DR widened to cover reality; acceptance gates and smoke runs moved permanently to --delay 2 ("门必须在真机条件下预测 真机"); and the doctrine written twice-paid: "不可约的管线属性(延迟、 限速)不是'随机化选项',是 plant 本体,第一天就该全额建模" - S1's nominal-then-robust staging falsified by hardware for the second time. The later s1e hardware run at power 1.0 (no kicking, normal force) closed the loop: "0.8 = 旧代拐杖" - the derating had been a crutch for the under-modeled delay, not a real requirement.
Change
Latency modeled as plant from day one of any lineage (measured 1-2 ticks covered, bridge-layer rate limits likewise modeled by default); every gate and smoke evaluation issued under --delay 2.
Outcome
Kicking reproduced, explained, and eliminated in the s1e generation at full scale and full power; the deploy-side crutches (0.7/0.8) retired for the new lineage.
Mechanism
Feedback oscillation onset is a product of loop gain and phase lag; a policy trained below the real delay learns gains that sit past the real stability margin, and any output derating masks it by scaling gain down. Since pipeline delay is deterministic hardware property - not an uncertainty - it belongs in the nominal plant, and every evaluation must include it or the gate predicts a robot that does not exist.
Applies when
- hardware oscillation/kicking that sim only reproduces with added delay
- a policy only runs on hardware at reduced power/scale
- defining what belongs in the nominal plant vs the DR list
“真实链路延迟 ~1~2 拍 … S1.1 只训到 1 拍——超训延迟 × 全环路增益 = 振荡;衰减 = 压环路增益换稳定。s1d 比 s1c 更脆 = yaw 三层栈学出更高反馈增益,增益越高延迟容忍越低。… 教训入账:S1「先标称后鲁棒」第二次被真机证伪——不可约的管线属性(延迟、限速)不是"随机化选项",是 plant 本体,第一天就该全额建模。”
train/OMNI_V0_SPEC.md § 3. S1.4(真机右脚乱踢事故强制) A torque-tail penalty was paid for by bracing the legs against each other - the second simulator's leg-contact count caught it, and the first explanation ("the trainer can't see self-collision") was retracted from the run's own config
torque-penalty-bought-by-leg-bracingWhen a penalty lowers a demand metric, look for what the policy traded to get there - keep self-contact frames and foot spacing as standing sim2sim readouts - and check any "the trainer cannot see X" explanation against the run's resolved config before it enters the record.
Symptom
After R3.1's torque_headroom term collapsed the demand tail, MuJoCo success fell 100 -> 98% and leg-leg contact frames at mu 1.0 rose 750 -> 2,190 (worst rollout 177 -> 450). The one failure (prone seed 2) had the legs crossed, one foot on the other leg, trapped at 0.067 m - visible on video.
Context
Across the ten prone seeds, foot spacing and leg-leg contact frames were monotonically anti-correlated, and the failure was the extreme of the series. Pulling the legs toward the midline shortens the hip_roll lever arm and lowers torque demand. At the time the spec explained it as Isaac training without self-collisions ("a free lunch in a simulator without self-collision").
Change
Leg-leg contact frames and foot spacing were tracked in every MuJoCo gate; R3.2's candidates were "train with self-collision on" or "a minimum leg spacing term" - not stacked.
Outcome
The next rung's joint-velocity penalty incidentally erased the dependency (2,190 -> 86 frames). On 08-10 the runs' logged env.yaml showed enabled_self_collisions true in both r3_1 and v2_2 (inherited from walk v10): the tangle was physically learned bracing, visible to both simulators, and the Isaac/MuJoCo contact-count gap was mesh and contact fidelity. The "self-collision debt" narrative was withdrawn for the whole line.
Mechanism
A penalty on demand rewards any configuration that lowers demand; legs pressed together act as a mutual support that fails when contact geometry shifts slightly.
Conflicts
§24 attributes the dependency to self-collisions being disabled in training; §36 retracts that from the runs' env.yaml ("§24's mechanism explanation was wrong") and keeps the older sections unedited as history.
Applies when
- a torque, impact or energy penalty improves its metric and cross-sim success drops
- legs or links approach each other after a regularization change
- an explanation relies on a simulator setting nobody checked in the run config
“prone 十个 seed 逐条看,脚距与腿-腿接触帧数单调反相关, 而唯一失败的那条正是最极端的一条 … 机制上说得通:把腿收到身体中线附近能缩短 `hip_roll` 力臂、降低力矩需求”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §24 代价:MuJoCo 成功率 100% → 98%,病因是两腿卡住 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) 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 Removing a foot-spacing wall passed every simulated gate and made the feet collide on the real robot - nothing priced stance width in the two-foot phase, the policy narrowed to the simulator's self-collision floor, and real calibration offsets closed the last millimetres; the wall came back with a gate
removed-wall-returns-on-hardwareWhen a constraint is removed, name what will govern that quantity instead and add a gate for it; never let a simulator's collision floor be the margin, and when a gate is exceeded by a hair, record the exact numbers and hand the release decision to a person instead of quietly passing it.
Symptom
On the first real-robot try of oneleg_v0 (2026-09-16) the two feet collided; the user also judged the folded foot not high enough.
Context
The V0 reward table had dropped the feet_lateral_distance wall because it seemed to conflict with the hip adduction single support needs. In the two-foot command bucket no remaining term governed stance width, so the policy drifted narrower until the simulator's self-collision stopped it; the sim acceptance had no foot-spacing gate, so 40/40 said nothing about it. On the robot, calibration offsets consumed the margin.
Change
V0.1: the wall restored (-10, minimum 0.16 m), re-checked against measured numbers (a swing-phase lateral spacing of ~148 mm costs 0.12 per step, acceptable); fold weight 0.8 -> 2.0; a ninth gate: minimum foot spacing >= 100 mm and zero leg-contact frames. The removal was kept on record.
Outcome
oneleg_v0_1 (V0r2 model_2200) passed 39/40 with the spacing gate 40/40. The single miss (a 15.4 deg tilt transient against a < 15 deg limit during a side switch, steady 6.9 deg, everything else green) was recorded with its numbers and released for the user to overrule.
Mechanism
An unpriced degree of freedom drifts to wherever the simulator stops it; if that stop is the simulator's own collision model, the policy's margin on hardware is whatever the calibration error leaves.
Applies when
- dropping a reward term that looked redundant or conflicting
- hardware shows a failure no simulated gate measures
- a release candidate misses one gate row by a small amount
“V0 撤墙被真机证伪（2026-09-16）：双脚桶没有任何项管站宽，策略贴 sim 自碰撞底线收窄，真机标定偏差一吃**双脚相碰**。 … min ≥ 100 mm 且腿碰 0 帧（eval_straight 同判据）—— … V0 真机双脚相碰暴露 sim 门未看脚距的缺口 … L s2 标称 tilt 瞬态 15.4°（门限 <15, 超 0.4°, 稳态 6.9°, 该跑其余全绿）——换侧瞬态蹭线, 判定放行留档, 用户可否决。”
git:Lucen V2@origin/oneleg-line:train/ONELEG_V0_SPEC.md § §4 feet_lateral_distance 行 / §6 验收门 ⑨ / §8 核查单 7 A binary reward band on the swing knee had zero gradient everywhere below it, so the one-leg policy parked in an unloaded "fake touchdown" that Isaac's 5 N threshold scored as success and MuJoCo showed as real pressing - a capped constant-gradient ramp, retrained from scratch, passed 40/40
binary-band-reward-fake-touchdownShape approach-to-target rewards as capped ramps with gradient from the starting posture, never as bands or indicators; and compare contact-based terms across simulators, because a policy riding just under a force threshold looks perfect in one and wrong in the other.
Symptom
At iteration 1,000 of the first one-leg run the swing foot never lifted: the policy stood with the "raised" foot resting lightly on the ground. In Isaac the contact-match term paid 96% of full marks; the same policy in MuJoCo pressed that foot on the ground for 450 frames.
Context
The swing-leg goal was "shank folded fully back" (knee 1.5-1.95 rad), rewarded as a binary band: +0.8 inside [1.5, 1.95], zero elsewhere. From knee 0.05 to 1.5 rad the term was flat. Contact is judged at a 5 N force threshold, so a foot carrying less than 5 N counts as lifted. The walk line had hit the same disease with a binary indicator (v4) and fixed it with a capped ramp (knee_swing_amplitude).
Change
swing_knee_fold changed from the binary band to a ramp clamp(|q|/1.5, 0, 1) - a constant gradient capped near 86 deg - and the policy was retrained from scratch (V0r1). After the first real-robot try showed the fold still too low, its weight went 0.8 -> 2.0 (V0.1).
Outcome
V0r1 model_2300 passed the full acceptance 40/40 (swing knee 1.72 rad, about 98.5 deg) and was stamped as oneleg_v0.onnx; the cross-simulator disagreement is recorded as the thing that caught the cheat.
Mechanism
A reward that is flat until the target is reached gives no gradient to approach it, so the policy settles for the nearest state other terms reward - here, a foot that satisfies the contact threshold without lifting; a second simulator with different contact force resolution exposes such threshold-riding.
Applies when
- rewarding a posture target with an in-band / out-of-band indicator
- a contact threshold decides whether a foot counts as lifted
- trainer-side contact terms are near full marks while the video looks wrong
“初版二值带 [1.5,1.95] 在膝 0.05→1.5 全程零梯度，策略停在"卸力虚点地"（Isaac 5N 阈下 contact_match 96% 满分 / MuJoCo 同策略 450 帧实压——跨仿真器互证抓作弊）；v4 二值指示同型病，按 knee_swing_amplitude 判例改常数梯度封顶 ramp，从零重训 … **oneleg_v0.onnx = V0r1 model_2300, 40/40 PASS**”
git:Lucen V2@origin/oneleg-line:train/ONELEG_V0_SPEC.md § §4 奖励表 swing_knee_fold 行 / §8 核查单 5 FK-verify a borrowed reference's structure, then size its amplitude by the reference's job - it pins phase, the policy adds lift
reference-structure-fk-amplitude-divisionWhen borrowing a reference trajectory: verify its structural claim against your own kinematics (an invariant like flat-foot), assign it the phase-pinning job, and size amplitude low enough that the policy contributes the lift - moving toward a proven foreign value in halves, not jumps.
Symptom
walk_v4 had big knee swing (40-46 deg) but only 18-24 mm foot lift - amplitude without hip/knee/ankle phase coordination; later, walk_v5's real-robot swing ballooned to 73.6 deg (sim 55.7) with violent footfalls - amplitude over-driven by the reference.
Context
Structure first: Humanoid-Gym's 1:2:1 hip:knee:ankle reference was verified on the local model before adoption - the ratio exactly satisfies the locally derived flat-foot constraint hip - knee + ankle = 0, FK-tested at multiple amplitudes with sole pitch 0.00 deg throughout. Amplitude second, and here the first reasoning failed honestly: FK said shorter legs need LARGER reference scale (0.30 for 30 mm lift), and the FK was correct - but the premise was wrong ("FK 没错, 但前提错了"): it assumed foot lift must come from the reference. HighTorque Pi, same scale, uses 0.08 with a 0.02 m foot-height target - proof that lift is added by the policy ON TOP of the reference, whose actual job is pinning the phase relationship. Scale 0.30 made the reference the entire gait: over-constrained and over-driven. The correction went to 0.15, deliberately not Pi's 0.08: "一次只走一半, 留退路" (walk half the distance, keep a retreat).
Change
target_joint_pos_scale 0.30 -> 0.15 as one of v6-minimal's three changes, treating both the footfall force and the lateral kicking (yaw momentum scales with leg swing amplitude).
Outcome
v6 improved landing force 1.72x -> 1.55x, suspended tilt 45.9 -> 23.0 deg, turn-gain asymmetry 70% -> 19%; the later v6-halved-shaping experiment (35 mm -> 4 mm collapse) confirmed the reference still carries the gait's existence on this machine - the division of labor is real but machine-specific.
Mechanism
A joint-space reference plays two separable roles: encoding structure (phase relations that keep the foot flat) and injecting amplitude (energy). Structure transfers across robots and is checkable by FK against an invariant; amplitude is a negotiation with the policy, and over-assigning it to the reference removes the policy's freedom to modulate lift with state.
Applies when
- importing a reference gait / imitation target from another codebase
- reference amplitude reasoning based on leg length alone
- real swing amplitude far exceeds sim's under a strong reference
“FK 没错, 但前提错了。我默认抬脚必须由参考轨迹产生。HighTorque Pi 同尺度机器人 … 用 0.08, 而它 target_feet_height = 0.02 m —— 说明抬脚是策略在参考之上加出来的, 参考只负责钉住髋/膝/踝的相位配合。我们取 0.30 等于让参考本身就是整个步态, 过约束 + 过驱动”
train/WALK_V6_MINIMAL.md § ① target_joint_pos_scale 0.30 → 0.15 Drop the frozen policy into chosen configurations - a squat 2.7 cm lower than the stuck pose stood 52% of the time, the stuck W-sit 0%, and the interpolation between them showed a wall, not a slope
configuration-probe-wall-not-slopeWhen a policy is stuck, probe the frozen policy from a grid of hand-placed start configurations, including interpolations between the stuck state and a nearby state it escapes from; one read-only experiment separates height, torque, sampling and configuration and tells you whether to prevent entry or train the exit.
Symptom
After R0.3 the policy stood from 62% of starts and never from the W-sit it fell into; height, torque, missing samples and reward were all plausible suspects.
Context
A read-only probe placed the R0.3 policy directly into specified configurations. Squats (hip, knee, ankle) = (-.65,-1.3,-.65) stood 100%, (-1.0,-2.0,-1.0) 89.8%, (-1.2,-2.4,-1.2) at 0.176 m 52.3%; the measured W-sit at 0.203 m 0.0%; the account-(3) hand-over state (146 deg tilt) 34.4%; linear interpolations from the W-sit toward the squat at 25/50/75% stood 0.0/0.0/3.1%. The squat family's quasi-static torque is 16% of the limits, and the W-sit was visited ~9 s per episode in training. FK showed the squat family (-a,-2a,-a) keeps the torso vertical, the feet flat and the COM over the feet all the way from 0.146 m to 0.384 m.
Change
Height, torque and sampling were eliminated in one experiment; the next rungs targeted entering the W-sit (foot placement) instead of escaping it, and seeding the dead point itself was ruled out because it was already visited every episode.
Outcome
Pure configuration: the W-sit (hips externally rotated +/-47 deg, knees folded 110 deg, shins flat, feet beside the body) is a different place from the sagittal squat (feet flat under the COM). The policy's standing skill was bound to a narrow sagittal family, and the wall was confirmed by the interpolation. The foot-placement rungs that followed took prone from 0/159 to 158/159.
Mechanism
A learned skill covers the neighbourhood of the states it succeeded from; a start state outside that neighbourhood fails regardless of height or torque, and an interpolation that stays at zero until close to a working state shows the boundary is sharp.
Applies when
- a policy stalls in a specific posture and several causes are plausible
- deciding between reverse-curriculum seeding and entry-prevention shaping
- a feasibility account says a path exists but the policy does not take it
“**决定性对比:比死点矮 2.7 cm 的蹲姿站立 52.3%,死点 0.0%。** 所以不是高度、 不是力矩(蹲姿族准静态力矩膝 1.96/12、踝 1.24/17,只占 16%)、也不是训练采样 (死点每局被访问 ~9 s)。**是纯位形问题** … 插值实验进一步显示这**不是坡是墙** —— 走到 75% 仍只有 3.1%”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §15 死点位形实验(只读探针,同一个 R0.3 策略放进指定位形) 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) Calibrate a wall penalty by measuring healthy and sick policies - healthy pays ~0, the disease pays a wall
calibrate-threshold-between-healthy-and-sickCalibrate every threshold penalty by evaluating its exact formula on replays of at least one healthy and one sick policy: place the threshold between their distributions, size the weight so the sick policy pays a decisive fraction of tracking while the healthy one pays ~0, and pre-compute neighboring thresholds for cheap adjustment.
Symptom
Real walk_v7 occasionally clipped its own legs (stance narrowed to 133 mm mean vs nominal 214.5); a foot-distance penalty was needed, but an uncalibrated threshold/weight risked either doing nothing or becoming a reverse barrier.
Context
The term (relu(d_min - lateral foot distance), measured in the base yaw frame because world-frame y is meaningless after turning) was calibrated before training by replaying three known policies through the exact reward formula at the acceptance operating point: healthy v5 (183 mm) pays 0.6% of tracking - effectively free; narrowed v7 (133 mm) pays 22% - effective widening pressure; collapsed v8 (93 mm) pays 55% - a wall. d_min 0.16 was placed deliberately between healthy and sick, with alternative thresholds (0.14/0.18) pre-computed in the tool output for later adjustment. The shape self-check was named as a standing question: "先问'零代价的选项是什么'" - the zero-cost region must be exactly the desired behavior.
Change
feet_lateral_distance added at d_min 0.16 / weight -10, with telemetry expectation pre-registered (should decay toward 0 as stance learns >160 mm; if bow-legged over-widening >214 appears, only then discuss an upper bound).
Outcome
v9_probe on hardware: no leg contact ("没碰腿(N2 兑现)"), stance min 145/126 mm green; the term's zero-cost design left healthy gait untaxed.
Mechanism
A relu threshold penalty defines a free region and a priced region; its correctness is entirely in where the boundary sits relative to the healthy and pathological distributions. Replaying known-good and known-bad policies through the exact formula measures both distributions in the term's own currency, making the threshold and weight a placement decision instead of a guess.
Applies when
- adding any relu/threshold-style wall penalty
- a safety margin (foot distance, joint limit, clearance) needs enforcement without taxing normal behavior
- choosing between candidate thresholds for a new term
“形状自检 (v6 横向组/v8-B 的教训 —— 先问"零代价的选项是什么"): 标称站距付 0, 健康步态付 ~0, 收窄才付费 … v5(健康) 183 mm … 0.6% ≈ 免费 | v7(收窄) 133 mm … 22% —— 有效推宽 | v8(塌陷) 93 mm … 55% —— 墙 … d_min=0.16 恰在 v5(183)与 v7(133)之间”
train/WALK_V9_SPEC.md § 2. N2 —— 脚距惩罚(已完成权重预标定) Cutting swing amplitude 40% raised yaw-momentum demand 53% - the falsified fix is recorded so nobody walks that road again
amplitude-cut-falsified-yaw-fixTest gait fixes against the quantity the ground must actually supply (torque/force rates vs friction ceilings), not against kinematic proxies; record falsified fixes with their mechanism so the search space shrinks permanently.
Symptom
Support-foot yaw slip stayed at ~223 deg (vs 225 deg) after walk_v6 cut joint swing amplitudes by ~40% (hip_pitch 20.6->10.4 deg, knee 31.3->18.8 deg) - the change built on the theory "smaller swing = less yaw momentum to dump into the ground".
Context
Direct measurement inverted the theory: yaw-momentum amplitude ROSE 16% (+/-0.1000 -> +/-0.1159) and its rate of change rose 53% (1.86 -> 2.84 N*m demanded from the ground), pinned exactly at the foot's supply ceiling (2.8-3.3 N*m at mu 0.6-0.7) - so slip could not drop. The extra demand lives in higher harmonics: v6's crisper foot placement (better clearance 34 mm, lower landing force) shortens the momentum- exchange window, concentrating the same exchange into less time. The verdict was written as a closed road: "下一轮不要再走这个方向". An honest residue was also booked: WHY amplitude down but momentum up 16% remained unresolved, with the named next step (per-rigid-body decomposition of H_z, since per-joint RMS sensitivity ignores phase correlations).
Change
The "reduce amplitude to reduce yaw momentum" lever was removed from the planning space; future yaw-slip work redirected toward the supply side (friction) and momentum-rate mechanics.
Outcome
Slip unchanged (225 -> 223 deg); the falsification and its mechanism became a permanent constraint on the fix search space.
Mechanism
Ground yaw torque demand scales with the rate of change of angular momentum, not its amplitude; kinematic amplitude cuts that also sharpen contact timing can raise dH/dt while lowering range. When demand exceeds the friction-limited supply ceiling, slip is set by the ceiling, so demand-side changes below the ceiling do nothing visible.
Applies when
- attacking foot slip or yaw drift via gait shape changes
- a fix targets an amplitude while the constraint is a rate
- documenting a failed intervention after a version comparison
“walk_v6 | ±0.1159 (+16%) | 2.50 Hz | 2.84 N·m (+53%) … 脚的供给上限 2.8~3.3 N·m(μ 0.6~0.7)—— v6 正好顶在天花板上, 所以滑移一点没降。… 结论: "减小摆动幅度以降低偏航动量"这条被 v6 证伪 —— 砍 39% 幅度, 需求反升 53%。下一轮不要再走这个方向。”
train/WALK_DIAGNOSIS.md § ② 未生效的机理: 减小摆动幅度反而让偏航需求上升 A single-signal contact detector lied in both directions - foot height flagged 40% false flight on a walking gait, contact force alone flagged false flight during low-friction slip - so flight became force < 5 N AND sole height > 5 mm
contact-detector-single-signal-liesDefine contact and flight events from two independent signals (force and geometry) in conjunction, validate the detector on a behaviour known not to contain the event before using it as a gate, and match the trainer's threshold when comparing across simulators.
Symptom
The run line needed a flight-fraction gate. The first MuJoCo version, "sole higher than 2 mm", measured 40% flight on a walking policy that never flies. Five weeks later the one-leg gate, using contact force alone, reported support-foot "flight" segments at mu 0.4 for a policy that was not hopping.
Context
During a walking step the toe lifts or the heel strikes with the foot pitched, so the ankle-roll origin rises a few mm while part of the sole still touches - height alone calls that flight. Switching to contact force < 5 N (the same threshold Isaac's contact reward uses) zeroed the false flight on walking. In the one-leg re-test every force-only "flight" segment had a measured sole height of 0.0 mm: the normal force chattered while slip corrections played out on low friction.
Change
Run line: flight = contact force < 5 N ("lift-off must be judged by contact force"). One-leg line (2026-09-16): flight = force < 5 N AND sole height > 5 mm, recorded as the same measurement lesson in the opposite direction; the gate's behavioural meaning was unchanged.
Outcome
With force-based detection the walking policy read 0.0 flight and the run policy's zero flight was confirmed by two plants; with the conjunctive definition the one-leg support-foot gate stopped reporting false hops.
Mechanism
Each signal has its own failure: geometry moves without leaving the ground (foot pitch), and forces drop without leaving the ground (slip chatter); requiring both removes both families of false positives.
Applies when
- writing a flight, lift-off, hop or slip detector for a gate
- a gate reports an event the video does not show
- reusing a detector on a different gait or floor friction
“首版用**足底高度>2mm** 判离地, 在 omni_s1e 走路策略上测出 40% 假腾空 … 改用**接触力 <5N**(与 Isaac feet_contact_number 同源阈值)后 空检归零 (walk 策略 flight_frac 0.0)。**课文: 离地判定必须用接触力, 高度判 会把脚的俯仰当腾空**”
train/README.md § run R1 立项 (2026-08-09): Mac 侧新工具 + 一次空检抓获 Mirror augmentation over an asymmetric default injects systematic error - symmetrize the default first and verify the transform bit-exact
mirror-augmentation-needs-symmetric-defaultBefore enabling any symmetry augmentation, make every constant inside the observation encoding exactly symmetric, and validate the mirror transform against forward kinematics to machine precision - an unverified augmentation is a new error source, not a regularizer.
Symptom
Mirror data augmentation was about to be added while both default poses (standing_pose, walk nominal_pose) were asymmetric - stale hand-tuned compensations from before a ground re-calibration, with hip_yaw differing 2.40 deg between sides and the foot soles actually tilted (pitch 2.88/1.35 deg, roll -2.47/+0.25 deg).
Context
The observation encodes joint_pos_rel = q - default. Under mirroring q_l -> -q_r, the relation (q-default)_l -> -(q-default)_r holds only if default_l = -default_r; with an asymmetric default, augmentation produces observation pairs that are NOT mirror images, i.e. "default 不对称时做镜像增强会引入系统性错误,比不做还糟" (worse than not doing it). The fix: adopt model geometric zero as standing default (MuJoCo FK verified: sole pitch/roll exactly 0, asymmetry 0.00 deg) and a symmetric crouch for walk (hip -0.25/knee -0.5/ankle -0.25 satisfying hip - knee + ankle = 0 to keep soles flat). The mirror transform itself was verified bit-exact before use: pseudovector vs polar-vector sign patterns (ang vel [-1,1,-1], gravity [1,-1,1], cmd [1,-1,-1]), joint swap-and-negate; FK check that left-foot pose under q equals the mirror of right-foot pose under mirror(q), measured error 0.00e+00.
Change
Defaults symmetrized first (with init heights recomputed by FK), stand policy retrained on the new default so both policies share one default; augmentation enabled only after the FK mirror test passed.
Outcome
stand_v1 achieved exact left/right pairing (l_knee -0.1013 / r_knee +0.1013), six-pair asymmetry 0.0 deg, height fluctuation 7 -> 1 mm, 33% less mean |action|.
Mechanism
Augmentation asserts an equivariance of the observation encoding; any asymmetric constant inside the encoding (the default) breaks the asserted symmetry, so the augmented data teaches a false invariance. Verifying the transform against FK geometry tests the assertion end to end, independent of the training stack.
Applies when
- adding mirror/symmetry augmentation to locomotion training
- defaults or trims were hand-tuned per side at any point
- observations are expressed relative to a default pose
“观测里 joint_pos_rel = q − default。镜像下 q_l → −q_r,要让 (q−default)_l → −(q−default)_r 成立,必须 default_l = −default_r。default 不对称时做镜像增强会引入系统性错误,比不做还糟。… 位置误差与姿态矩阵误差实测均为 0.00e+00。”
train/RETRAIN_v2.md § 2. 前提:default 姿态必须先对称化(不是可选项) / 3. 镜像变换 Four in-lineage attempts to widen the standing stance failed - remove a tax, add a joint-space knife, change the target, add a task-space metric penalty - because the stance was the end state of the get-up path; trained from scratch with the right terms it grew right from day one
stance-decided-by-get-up-pathA posture a skill ends in is shaped by the path the policy takes to reach it; if several single-variable edits to the terminal-phase reward cannot move it, stop editing that phase and retrain with the terminal constraint present from the start.
Symptom
v2_6c stood with its feet 0.159 m apart (task-space) and its hips yawed 45-47 deg the same way, which split on the real robot. Standing-phase reward edits did not move it.
Context
V2.7-A removed the flat-feet tax on compensated stances (stance unchanged); V2.7b added a hip-roll lower-bound hinge (+5 deg in 3,000 iterations, yaw ratchet); V2.8 changed the stand_pose target to a wide flat stance (stance unchanged, yaw not unwound, feet nearly overlapping, mu 0.4 transfer 2%); V2.9 penalized lateral spacing in metres (the policy parked just outside the penalty's gate in a lunge, 0% success). The v2_6c get-up goes through a split and closes the feet together as it rises.
Change
In-lineage stance surgery was formally closed. V3.1 trained from scratch with task-space stance terms present from the first iteration (and, after P1, a positive width band instead of a penalty).
Outcome
V3.1 P1b: lateral stance 0.364 m, foot tilt 0.0 deg, all four categories 100%, MuJoCo mu 1.0 and 0.4 both 100% - with a symmetric toe-out the kinematic audit had not enumerated. P1c (with a yaw guard): 0.355 m, all six acceptance criteria passing, mu 1.0-0.4 all 100%; it became the product.
Mechanism
A converged policy does not rebuild the path that produced its terminal posture; a standing-phase gradient only finds the nearest hack around the posture the get-up delivers.
Applies when
- the final posture of a transition skill is wrong and resists terminal-phase shaping
- repeated continuation rungs produce hacks instead of the intended posture
- deciding between another in-lineage fix and a from-scratch retrain
“窄站距 + yaw 扭是 v2_6c 起身策略(劈叉起身 → 双脚并拢收势)的**结构性 终态**,不是站立段的孤立参数 —— 站立形态由起身路径决定,在血统内只动 站立段奖励改不动它。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §44 结果:V2.8 判 FAIL —— 血统内站姿手术第三次证伪 Prove an armless get-up exists before training it - a connected static domain, 25% torque on the cheapest path, an 8 mm hand-over gap, a static roll-over - and write down what each scan cannot represent
get-up-feasibility-accounts-before-trainingBefore training a get-up or any multi-contact skill, compute the quasi-static accounts - connectivity of the static domain, torque along the cheapest path, hand-over gaps, COM shift available for rolling - and state which configurations each scan cannot represent; when a policy gets stuck in one of those, extend the scan before blaming the reward.
Symptom
A torso-and-legs robot has no arms to push off the ground; whether it can get up from the floor at all was unknown when the line opened.
Context
recovery_feasibility.py ran three accounts before any training (the run line's "hard accounts first" discipline): a sagittal quasi-static scan (0.05 rad grid, 44,520 configurations, MuJoCo FK, flat-foot assumption). (1) The static standing domain (COM over the feet, torques in limit) has 29,586 cells, flood-fill connected with no islands, from a 0.097 m deepest squat to the 0.384 m stand. (2) The minimum-torque path peaks at 25% of the limits (knee 2.9/12, ankle 3.7/17 N*m) - a 4x margin. (3) All 508 ground-contact configurations have the contact behind the COM; the smallest gap to pure foot support is 8 mm. A roll-over account: swinging both straight legs to one side shifts the COM 96 mm against a 62 mm torso half-width - 1.6x, so rolling needs no momentum. Three conclusions were written down for later attribution: the legs are 80% of the mass (swinging them moves the COM), prone has no flat-foot hand-over face (merge into a supine/side sit first), and supine needs no sit-up (hip flexion is limited to 75 deg).
Change
The accounts gated opening the line and were cited in every later argument about what the robot can physically do.
Outcome
They held where they applied: in V1.0 every fall category was righted under a hard rate limit, which the spec records as the quasi-static roll-over account verified by training, and the 25% torque path was the basis for pursuing a slow get-up. They also misled once: account (3) is sagittal, and on 08-09 the spec corrected its scope - it cannot represent the splayed W-sit where the policy actually stalled. A follow-up prone hip-ROM scan (471,625 cells) found 3,912 two-foot-contact cells and none with both soles within 25 deg of level (best 40.2 deg): a flat-footed push-up from prone is infeasible on this robot, so the fix became where the feet go after sitting up.
Mechanism
A get-up needs a connected path through statically feasible configurations and enough torque along it; quasi-static accounts bound both cheaply, and momentum can only make the real problem easier. A reduced-dimensional scan, though, only speaks for the configurations it can express.
Conflicts
In R0.1-R0.2 the spec read account (3)'s "prone has no front hand-over" as "prone lacks the roll-over skill"; R0.3's confusion matrix showed prone had righted its torso 159/159, and the spec then restricted account (3) to the sagittal configurations it models.
Applies when
- opening a get-up, recovery or climbing skill on a new robot
- a robot lacks arms or other obvious contact options
- a policy stalls in a configuration a feasibility scan never modelled
“本机 **torso + legs、无手臂可撑地**,开训前先证明存在不依赖手臂的物理解 … 双腿同侧直腿摆最大横移 **96 mm = 1.6×** … **静态摆腿即可翻身,无需动量**”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §1 机械可行性判决(2026-08-09,recovery_feasibility.py 三笔账) 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 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） The median of a bimodal metric lands in the empty gap - check the distribution, and never judge swing on 3 seeds
median-hides-bimodal-distributionBefore quoting a median or mean, look at the distribution; report suspected-bimodal metrics as mode share plus per-mode ranges, use small-seed smoke runs only to screen trends, and size the seed count for decisions by the share resolution you need (here: 20).
Symptom
Years of "high swing variance" and undecidable 3-seed swing readings turned out to be one fact: the metric was bimodal all along - "历代 swing 高方差与 3-seed 不可判由此定性:一直在测双稳态系统" - and every median reported from it (e.g. 12.1 mm) described a value no seed ever produced.
Context
Concrete instances: s2e_pd-1400's 20 seeds split 2.6-4.9 mm vs 19.3-24.0 mm with zero seeds between; s1e-500 read 3.9 mm on seeds 0-2 but 23.1 mm median over 20 seeds; a 3-seed reading of 19.3 was logged as "double-peak optimism, lesson recurrence #4". The selection re-audit codified the sampling rule: "3-seed 的 swing 读数不可判点, 只能筛带,选点必须 20-seed" - 3 seeds may screen a band, only 20 seeds may pick a point.
Change
Swing (and any suspected multi-modal metric) reported as mode shares plus per-mode ranges instead of a bare median; 3-seed smoke numbers demoted to band-screening; all shipping/selection decisions moved to 20-seed batteries.
Outcome
The "swing debt" bookkeeping was reinterpreted as basin probability (see swing-bistability-damping-switch), and checkpoint selection stopped being whipsawed by which basin the first three seeds happened to fall into.
Mechanism
Central-tendency statistics presuppose unimodality; on a bimodal distribution the median tracks the mode SHARE, not any achievable behavior, and small samples alias the share entirely. Mode-aware reporting (share + per-mode stats) is the only faithful summary, and the needed sample size is set by the share resolution required.
Applies when
- a quality metric shows chronic high variance across seeds
- 3-seed smoke readings contradict 20-seed batteries
- reporting swing height, clearance, or any basin-prone metric
“中位数落在空档里,「swing 债 −11mm」实为「50% 概率掉进拖地吸引子」。历代 swing 高方差与 3-seed 不可判由此定性:一直在测双稳态系统。… swing 跨 seed 双峰 (500 在 seed0~2 只读 3.9mm, 20-seed 中位 23.1) —— 3-seed 的 swing 读数不可判点, 只能筛带, 选点必须 20-seed。”
train/README.md § swing 双稳态定性 / s1e 选点重审 The fallen-state reset was designed, not sampled from SO(3) - fixed category shares with jitter, a low drop that settles physically, equal left/right shares for mirror augmentation, and a numeric check before training
fallen-pose-reset-distributionBuild a fallen-start distribution from named, physically plausible categories with jitter and a settle phase, keep mirrored categories at equal probability, and check the realized shares and penetration numerically before spending a training run on it.
Symptom
A get-up policy can only learn from the fallen states its resets produce; uniformly random orientations produce ground-penetrating and limit-jammed states the robot can never be in.
Context
R0 reset_root_fallen: supine 30%, prone 30%, side_l 15%, side_r 15%, mid (random axis 50-125 deg) 10%, +/-15 deg jitter, full yaw, dropped from 0.28-0.40 m and left to settle under physics, joints uniform inside the soft limits with a 5% margin plus small random velocities. Random SO(3) was rejected (the advisor agreed). side_l and side_r must have equal probability because mirror augmentation turns a left fall into a right fall. The advisor had proposed supine and prone only for R0; the spec included side and mid because the feasibility accounts showed physical solutions for all of them, and wrote "narrow back to supine+prone" down as the first fallback. With no display on the training box the reset was checked numerically instead of by eye.
Change
Category mix as above; realized shares, settle height and penetration measured over 512 envs before the first run. A fallen-state bank (real falls, settled and stored) was pre-registered for R2.
Outcome
Realized shares 29.3/31.6/16.4/17.8% against the config, settle +0.262 m, final penetration 0/512 (a 0.10 m peak at the write instant, ankle links only, pushed out within 80 ms because the 0.28 m drop floor is shorter than a fully extended leg). R0's failure was a reward basin, not a reset artifact. The fallen-state bank stayed unbuilt through V3.1 (checklist item open); R0.3 later re-sliced the prone share into roll_l/roll_r bands, which is what forced the acceptance distribution to be frozen separately.
Mechanism
A category-structured, physically settled start distribution keeps training on states the robot can actually occupy, and equal mirrored shares keep mirror augmentation a pure doubling of data rather than a bias.
Applies when
- designing reset distributions for get-up, recovery or multi-contact skills
- mirror/symmetry augmentation is on and the task has chiral start states
- no viewport is available to inspect resets on the training machine
“角度 jitter ±15°、yaw 全域、0.28~0.40 m 低空放下由物理沉降,关节软限位内 均匀(留 5% 余量)+ 小随机速度。**不用 random SO(3)**(会采出穿地/极限卡死 等现实不可能状态,顾问同判) … side_l/side_r **概率必须相等**(镜像增强的样本同分布前提)”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §3 R0 任务定义 / §9 核查单 Teleop fed the sidewalk axis a command beyond its training band - feet clipped; give each axis its own speed setting
teleop-command-band-per-axisGive every command axis its own teleop scale, clamped to that axis's training band, and reproduce any hardware incident in sim with the exact deployed command values before touching training.
Symptom
Robot stepped on its own foot when sidewalking left under teleop - and only when going left.
Context
The teleop tool used one speed setting for all axes: --teleop-speed 0.20 applied to A/D sent cmd_vy = 0.20, above the training band's top (0.08-0.18) where foot-spacing margin is thinnest. Sim reproduction of the incident (product policy, pw0.8, 5 seeds x 20 s, true collision threshold = single foot width 104 mm): at vy 0.20 the minimum foot distance was 111-115 mm - 7-11 mm from self-collision - vs 147 mm at vy 0.10. Left was 4x more dangerous than right (25% vs 6% of time inside the 160 mm soft wall at vy 0.10), matching the left-only symptom; the margin did not degrade over time (pressing more just lengthened exposure).
Change
deploy_policy gained --teleop-side (default 0.10), separating the lateral speed from the forward speed so each axis's teleop command sits inside its own trained band.
Outcome
Command now inside the band with 43 mm margin at default; the incident became a quantified, reproduced, closed account rather than a mystery.
Mechanism
The policy's competence envelope is the training command distribution per axis; teleop mappings that share one scalar across axes silently command out-of-band inputs on the weakest axis. Asymmetric risk (left vs right) came from the policy's own chirality bias, so a symmetric command produced an asymmetric hazard.
Applies when
- wiring a joystick/teleop layer over a learned policy
- a hardware incident occurs on one command direction only
- training bands differ across command axes
“A/D 一直与 W/S 共用速度档，所以按 A 下发的是 vy = 0.20 —— 既超训练带（0.08~0.18）上沿 … 0.20（遥控实际值）| 111~115 mm | 7~11 mm … 且左比右危险 4 倍 … 处置：deploy_policy 新增 --teleop-side（默认 0.10），侧移与前进档分开。”
train/C_LADDER_RUN.md § 3p. 一 向左走踩到自己 → --teleop-speed 0.20 同时喂给了 vy Training the final recipe from scratch in one run - every mechanism the lineage had accumulated - produced 0% and a seated robot; the order in which the lineage acquired those mechanisms was part of why it worked
curriculum-history-is-part-of-the-productA recipe that ends a lineage is not a recipe for a from-scratch run: consolidate it as ordered curriculum phases matching how the lineage acquired its mechanisms, check that every curriculum criterion is reachable from the starting policy, and read the run's raw term values, not the total reward, before calling it green.
Symptom
V3.0 trained the lineage's whole final recipe from scratch in one 9,000 iteration run - full beta curriculum, prone-conditioned pull assist, friction DR, the 3 s zero gate on standing income, flat_feet - testing the proposition "the product is defined by its configuration, not by its training history". Training looked all green (reward 26.33, episode length 500, 100% time-outs).
Context
Read in raw units against v2_6c at the same weights, the green was a seated equilibrium: base_height 0.377 vs 0.640, stand_pose 0.205 vs 0.516, flat_feet 0.0000 (zero because it sits outside its height gate, not because the feet were flat). Acceptance: 0.0% in Isaac at the deployed authority, 0.0% on every MuJoCo friction level, and still 0.0% at the training-time authority (100% seated at 0.222 m, upright and still).
Change
The full stdout (316k lines) was read: the beta curriculum's criterion (standing share over 0.35) was met zero times, so beta never left the wide setting and the policy had no experience at the deployed authority; the pull curriculum was stuck on the same criterion. The lineage had escaped the seated basin with immediate income (V2.0-V2.2) and only then added the zero gate to cure rushing (V2.5); from scratch, the zero gate removed the early "stand fast, earn more" gradient needed to escape. Verdict "the curriculum history is part of the product", limited to n = 1. v2_6c stayed the product.
Outcome
V3.1 kept the order as explicit phases: P1 from scratch with immediate income (zero gate off) until the curricula advance, P2 adding the zero gate. P1b/P1c escaped the seated basin and passed; P2 was later judged net negative and dropped (time-gate-vs-wide-stance-retire-the-fix).
Mechanism
Mechanisms that refine a competent policy (time gates, tight authority) can delete the gradient a naive policy needs, and a curriculum whose advancement criterion the naive policy never meets freezes at its first level.
Conflicts
The spec limits the falsification to "this recipe + this curriculum criterion" (n = 1, no seed sweep, no criterion tuning). V3.1's phased run succeeding is consistent with the ordering reading but changed other terms too.
Applies when
- consolidating a long lineage of continuation fixes into one clean recipe
- a from-scratch run with all mechanisms enabled plateaus early
- curriculum state is not logged or never advances
“命题:产物由配置定义,而非训练史定义。 … 训练 log(完整 stdout 316k 行)`[beta_anchor]` 仅初始 1 行,**达标 0 次** … V2.0~V2.2 靠**即时计酬**爬出坐姿盆地(§33),站立巩固后 V2.5 才装归零门 治"过快"(§39)。**课程史是产品的一部分。**”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §42 V3.0 判决(2026-08-11):从零单 run 全机制 FAIL 于坐姿盆地 Foot dragging is an attractor, not a low amplitude - and joint damping is the mode switch, adjustable at deploy time
swing-bistability-damping-switchWhen a quality metric is bimodal, stop treating it as an amplitude to be trained up: map the modes against initial conditions and plant parameters, find the parameter that switches basins, apply it first as a deployment lever, and only then bake it into the training distribution (as a plant-family shift, never as an execution-mapping change).
Symptom
s2e_pd-1400's swing height "median 12.1 mm" hid a perfect bimodal distribution: 20 seeds split into a drag mode (2.6-4.9 mm) and a step mode (19.3-24.0 mm) with NOT ONE seed in between - the median sat in the empty gap, and "swing debt -11 mm" really meant "50% probability of falling into the drag attractor".
Context
Two designed experiments closed the mechanism. Test A (nominal plant, 40 seeds): step 42% / drag 58% / middle 0 - at nominal gains, initial conditions alone pick the mode, both modes 100% survivable. Test B (fixed init, kp x kd grid): kd is the mode SWITCH - at kd 1.3 all surviving cells step (13-22 mm), at kd 0.7 nearly all drag (2.7-4.3), only at kd 1.0 does init get a vote; kp >= 1.2 is dangerous (5/6 falls). Global verification at kd x1.3 (20-seed, delay 2): survival 20/20 at ZERO cost, step share 42 -> 80%, swing median 12.1 -> 18.4 mm, slip record low 334, thicker tilt margin - costs: vx 85 -> 78%, saturation +5 pp. A Pareto sweep then priced the knob: step share 42/72/75/88/82/90 across kd 1.00-1.30 with a linear vx tax of -2.3 pp per 0.1 kd - the basin gain is fully collected at kd 1.20 ("1.30 是 over-damping 纯多付税"). Mechanism: low damping leaves a landing micro-oscillation / ground-slide channel the policy can exploit to drag; damping plugs the channel.
Change
Deployment lever adopted: kd-scale 1.20 (conservative 1.15) as the legitimate successor to the power-0.8 crutch ("前者削幅度保稳,后者堵 拖地通道换步态,且不牺牲存活"); training-side prescription: move the DR band to nominal-1.2 x (0.9,1.1) = [1.08,1.32], deleting the [0.7,1.0) drag-teaching zone - a contract-level change requiring digest re-baselining, gated on measuring the real robot's actual kd dispersion first.
Outcome
The kd surgery rung (s2e_kd) delivered basin 8 -> 11/20, slip 405 -> 331, vx 81 -> 85% with no out-of-band fragility (below-band check 20/20) - "拐杖烧进分布的正确姿势", explicitly contrasted with the failed s1g amplitude version: this one changes the plant family the policy has seen, that one changed the execution mapping the policy would have to relearn.
Mechanism
The gait's swing behavior is a bistable dynamical system whose basin boundaries are set by plant parameters; a policy trained across a kd band that includes the drag basin has learned to inhabit it. Shifting the deployed (and then trained) damping moves the system into the step basin without touching the policy - a plant-side fix for what looked like a training deficiency.
Applies when
- a gait quality metric splits into distinct modes across seeds
- deciding between more training and a gain/damping change
- converting a deployment crutch into a training-distribution change
“20-seed 里拖地模式 2.6~4.9mm 与迈步模式 19.3~24.0mm 各半,中间一个不落 … kd 是模式开关——kd1.3 下 6/6 存活格全迈步 … kd0.7 下几乎全拖地 … swing 债的解(至少大半)在部署端阻尼档,不在训练端 … 机理:低阻尼下落脚微振荡/贴地滑给了策略顺势拖行的通道,加阻尼堵之。”
train/README.md § swing 双稳态定性 + kd 部署杠杆 (2026-08-07, 用户设计 Test A/B) A 10 s acceptance episode left 6-7 s of standing to observe - a narrow stance held for that window and split on hardware; a gate cannot see instability slower than its own horizon
episode-length-bounds-what-a-gate-seesSize the standing phase of an acceptance episode, and its disturbances and floor friction, to what deployment will impose; a pass on a short static window certifies only that window.
Symptom
v2_6 passed every simulation gate (success 99.6%, re-falls 0-1%) and then, on the real robot, stood up and slid into the splits several times; prone starts stood and then fell backwards.
Context
Acceptance ran 10 s episodes; a ~1-2 s get-up left roughly 6-7 s of static standing on a nominal floor with no disturbance. The narrow stance's lateral margin and the straight-knee stance's lack of any flex buffer are both failure modes that need time, disturbance or lower friction to show.
Change
The gap was booked as a known blind spot of the gate ("long-duration standing stability") alongside the task-space stance criterion; later rungs added MuJoCo friction sweeps at mu 0.4 to every checkpoint scan.
Outcome
The spec through §50 records the blind spot but no longer standing window or disturbance row in the recovery acceptance itself.
Mechanism
An acceptance episode observes only the dynamics that unfold within its horizon under its conditions; slow drifts and disturbance-triggered failures are outside it by construction.
Applies when
- a policy passes sim gates and fails on hardware after a delay
- acceptance episodes are short relative to deployment use
- stability is judged without pushes or friction variation
“**sim 门为什么没逮住**:10 s episode 起身后只站 ~6-7 s,静态窗口内窄站距 撑得住;真机站立时长/扰动谱在门口径之外 —— 长时站立稳定性记为口径缺口。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §43 判读:sim 门为什么没逮住 A plateau in a training curve was a population mix, not a half-learned skill - 60% standing at 0.372 m and 40% sitting at 0.19 m - and a category at hard zero stayed at zero through 3,000 more iterations
zero-partial-credit-is-not-an-iteration-problemBefore buying iterations for a plateau, split the metric by category and check whether it is bimodal; a category at hard zero with no partial credit is missing a capability or a reachable state, and more iterations under an unchanged config will only polish the categories that already work.
Symptom
After R0.1 the training-side base_height sat near 0.29 m and the curve was still climbing at the iteration cap, which read as "train it longer".
Context
Candidate A (user decision) was a child-run from R0.1's last checkpoint with zero config change - the logged env.yaml files differ only in log_dir - for 3,000 more iterations (R0.2). The per-category acceptance split was already available: prone had scored 0/156 with no partial credit.
Change
Continue training unchanged, then read the result by category rather than by the pooled curve.
Outcome
supine 91.5 -> 96.4%, side 82.2 -> 87.9%, get-up 0.96 -> 0.84 s, pose error 0.91 -> 0.54 - all improvements to categories that already stood. Prone stayed 0/153; mid 55.9 -> 41.2% was within noise (n=34). Height by category was binary - standing groups 0.372/0.373 m, seated groups 0.187/0.194 m, nothing between - so the pooled 0.29 was 0.61 x 0.372 + 0.39 x 0.19 = 0.30 (measured 0.307): six in ten standing, four in ten sitting. The rendered prone episode was still kneel-sitting at t = 8 s.
Mechanism
The pooled mean of a binary outcome only moves when the mix moves; PPO kept polishing the subpopulation that already succeeded while the failing one produced no advantage signal to follow.
Conflicts
R0.2 recorded the missing capability as "prone lacks rolling over"; R0.3's end-state confusion matrix retracted that - prone had righted its torso in 159/159 episodes and was failing to stand from the W-sit. The lesson that iterations could not fix it holds; the named cause was wrong.
Applies when
- a training curve plateaus while acceptance shows one category at zero
- deciding between "train longer" and "change something"
- pooled training metrics are read as the typical episode
“**分类别 h 中位把"平台 = 人口混合"钉死了**:数值是**二值**的 —— 站立组 0.372/0.373,坐姿组 0.187/0.194,**中间没有过渡态**。 … **这也是本仓此后读该指标的通用告诫:全体混合的期望会把 双峰分布平均成一个不存在的中间值,必须分类别看。** … **结论:A 不能过门,原因确定为 prone 缺"翻身"这一技能,不是迭代不够。**”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §13 R0.2(recovery_r0_2,child-run 续训):A 走完了 —— 推不动 prone 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 Audit rewards by realized contribution (weight x achieved value) - a weight of 2.0 was really paying 0.04
realized-contribution-auditEvaluate a reward table by each term's realized per-step contribution under the current policy, never by its weight column; if a term's realized value is ~0, escalating its weight is a no-op - change the term's structure instead.
Symptom
Foot dragging persisted through repeated weight escalation: feet_air_time had been raised 0.25 -> 1.0 -> 2.0 across versions with no behavioral change, and the training-side comment even recorded the fact ("几乎没有 单支撑相, 是拖着脚蹭") without the fix changing form.
Context
Computing realized per-term contributions in the trained state exposed the economy: feet_air_time contributed weight 2.0 x achieved 0.019 = 0.038 per step, against tracking's +1.20 - lifting the leg earned 3% of what tracking earned, so dragging was the rational optimum no matter the weight escalation. The same table acquitted the energy penalties (total -0.30 negative vs +1.74 positive) that a naive read of weights (-5.0 orientation!) would have blamed.
Change
Fix redirected from "raise the weight again" to "add a term whose realized contribution changes the optimum": a clearance penalty sized so its realized magnitude (~0.018/foot when dragging) is comparable to feet_air_time's, enough to flip the optimum without drowning tracking.
Outcome
With the term economy corrected (plus posture/range fixes), swing height reached 34 mm and tracking 87% by v6; weight escalation of the old term was abandoned.
Mechanism
A reward weight is only a multiplier on whatever the policy currently achieves on that term; when the achieved value is near zero (behavior absent), escalating the weight multiplies near-zero. Optimizer behavior is governed by realized per-step magnitudes, so audits must be conducted in that currency.
Applies when
- a behavior persists despite repeated weight increases
- auditing whether penalties are "too strong" or rewards "too weak"
- sizing a new reward term against existing ones
“把 feet_air_time 权重从 0.25 → 1.0 → 2.0 一路加,但没有加高度项。量级算下来:feet_air_time 权重 2.0 × 实得 0.019 = 0.038,而跟踪奖励是 1.2。抬腿的边际收益只有跟踪的 3%,拖地当然是最优解。… 正项 +1.74,负项 −0.30。能量惩罚不是瓶颈,抬腿没收益才是。”
train/WALK_DIAGNOSIS.md § 决定性证据(#1) / 各项奖励的实际量级 Before training a one-leg stand, the accounts and a probe showed the default gains could not hold it at all - kp 20 needs 0.39 rad of error to carry the static roll moment, more than the whole adduction range - so per-joint gains came first, and thermal limits set the session length
single-support-gain-authority-probeBefore training a posture that loads one joint statically, compute the steady tracking error load/kp and the series stiffness against m*g*h, and prove with a simple hand-written controller that the posture can be held under the deployment gains - change the gains first if it cannot; then size session length from the thermal account.
Symptom
The one-leg line (standing on one foot, the other folded back, no hopping) had to decide whether the existing gain profile could hold single support before any reward was designed.
Context
Hardware accounts (9.792 kg, COM 0.234 m high, 170 x 80 mm feet, legs 80% of the mass): moving the COM over one foot needs 107 mm of shift and the 20 deg hip-roll adduction range gives 131 mm - geometrically enough. The static frontal moment is 7.8-9 N*m, within RS02's 17 N*m - torque is enough. But at kp 20 carrying 7.8 N*m needs 0.39 rad of tracking error, more than the entire adduction range, and the real robot had already shown it: commanded +0.17, actual -0.04 (0.21 rad droop) under load, 0.0008 rad hanging - load, not the motor. A probe (probe_oneleg.py) then showed open-loop PD cannot hold single support on physics grounds, so the criterion became "an equilibrium exists and a hand-written 4-gain COM feedback can hold it": single-support roll stiffness is hip and ankle in series and must exceed m*g*h_com = 22.5 N*m/rad; ankle kp 12 in series with hip kp 80 gives only 10.4 (open loop 16/16 fell), ankle 60 with hip 80 gives 34.3 (52% margin).
Change
A per-joint gain profile (rl_oneleg: hip_roll kp 80, ankle_roll kp 60, the rest as rl_default) - which needed per-joint gain support in robot.yaml, the bridge, deploy and the trainer's actuator groups - decided before training. Thermal account: single support makes hip_roll the dominant heat load (about 7.8 N*m against a 7 N*m continuous rating), so acceptance and demos run in segments of at most 60 s with a temperature check.
Outcome
Under rl_oneleg the hand-written feedback held six cells cleanly for 6 s (hip_roll steady torque 2.1-3.4 N*m, half the thermal budget); under rl_default the same feedback on the same cells fell 0/4. The trained V0 policy then passed its 40-cell acceptance.
Mechanism
With PD position control, the steady error needed to carry a static load is load/kp; when that error exceeds the joint's range the posture is unreachable whatever the policy does, and series compliance between joints lowers the effective stiffness below the gravity stiffness that single support demands.
Applies when
- single-support, crouched or one-arm-load postures on PD actuators
- a joint "droops" under load on hardware but tracks well when hanging
- deciding whether a new skill needs its own gain profile
“但 kp=20 时撑住 7.8 N·m 需要 **0.39 rad 跟踪误差 > 整个内收行程**。真机已实测: 命令 +0.17 实际 −0.04（droop 0.21 rad），悬挂时 0.0008 rad——是负载不是电机。 … 单支撑滚转是 hip/ankle **串联**刚度，必须 > m·g·h_com = 22.5 N·m/rad；ankle kp12 串 hip80 只有 10.4（开环 16/16 全摔），60 串 80 = 34.3（裕 52%） … **rl_default 同反馈同格 0/4 全摔**（增益档必要性对照）”
git:Lucen V2@origin/oneleg-line:train/ONELEG_V0_SPEC.md § §1-1 单脚站: 几何可行，卡点是 hip_roll 增益权限 / §2 A 线增益 / §5 probe 定谳 A quadratic clearance reward stalled for 3500 iters near target - switch to an indicator on accumulated height
indicator-reward-avoids-gradient-decayWhen a shaped term plateaus near its target, check the gradient profile: replace vanishing-gradient forms with threshold/indicator forms for the final approach, and prefer delta-accumulation over absolute positions to immunize against frame offsets.
Symptom
The quadratic-error clearance term froze at -0.002 from iteration 2000 to 5500 - thousands of iterations with no progress on foot lift.
Context
Diagnosis: a quadratic penalty's gradient vanishes as the error approaches target, so exactly where the last millimeters must be earned the incentive fades to nothing. The replacement (Humanoid-Gym form): accumulate the swing-phase height climb per foot, reward a BINARY indicator |accumulated - target| < 0.01 masked to the planned swing window, reset on contact, weight +1.6 as a positive reward. Two properties: the indicator's incentive is constant until the threshold is crossed (no decay zone), and accumulating height DELTAS makes any constant sole-frame offset cancel automatically - which structurally sidesteps the earlier 0.0585 m zero-point bug ("顺带绕开我先前那个'忘了减 0.0585 导致惩罚恒为 0'的坑").
Change
Clearance reformulated from quadratic penalty on instantaneous height to indicator on per-swing accumulated climb (target 0.03 m by leg-length scaling, weight +1.6).
Outcome
Part of the v5 package under which lift finally moved (v5 29 mm, v6 34 mm vs the stalled 18-24 mm era); the offset-cancellation property removed one whole bug class from the term.
Mechanism
Policy-gradient learning follows the reward's local slope; quadratic shaping concentrates slope far from target and starves it near target, so convergence stalls precisely at the finish line. An indicator pays a constant bounty until the goal is met; formulating on deltas rather than absolutes removes sensitivity to reference- frame constants.
Applies when
- a reward term's value freezes short of target for thousands of iters
- designing clearance/height/precision terms
- reward code depends on absolute link positions
“现行二次型在接近 target 时梯度趋零 —— 这正是 clearance 从 iter 2000 到 5500 卡在 −0.002 不动的原因。… 二值指示在跨过阈值前梯度恒定,没有衰减区 … 累积 delta 让 SOLE_OFFSET 自动抵消”
train/WALK_V5_SPEC.md § 3. clearance 改峰值型(去掉二次型的梯度衰减) 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 A time gate that had cured one lineage's rushing made the from-scratch lineage trade away its stance width twice (0.364 -> 0.235 m, 0.355 -> 0.251 m) - its disease was absent there, so the fix was retired and the pre-gate checkpoint shipped
time-gate-vs-wide-stance-retire-the-fixCarry a fix into a new lineage only if its disease is present there; a mechanism that cured one lineage can be net negative in another, and when doubling a term's weight recovers almost nothing, treat the two objectives as structurally in conflict and remove the one whose purpose is gone.
Symptom
V3.1's phase 2 (the 3 s zero gate on standing income, continued from P1b) kept 100% success on every friction level and slowed the get-up, but the lateral stance drifted 0.364 -> 0.235 m and hip yaw crept to 57 deg against its 60 deg limit. With the width band's weight doubled (P2c, after P1c) it drifted again, 0.355 -> 0.251 m, below the pre-registered 0.30 m failure line.
Context
The zero gate had been introduced in V2.5/V2.5b to slow the old lineage's get-up. In V3.1 the rushing was already absent: P1c got up in 0.90-1.06 s with a worst torque ratio of 73.3%, better than the stamped v2_6c, because the full beta curriculum, second-difference smoothing and pull curriculum had cured the violence inside training.
Change
Recorded as a candidate law with two data points - the zero gate and a wide stance are mutually exclusive here - and the zero gate was removed from the V3.1 recipe. P1c (the pre-gate checkpoint) went through the full stamp-level acceptance instead.
Outcome
P1c passed everything: all six criteria, lateral stance 0.355 m, foot tilt P75 2.0 deg, mu {1.0, 0.8, 0.6, 0.4} x 10 seeds all 100%. recovery_v3_1p1c.onnx was stamped and pushed to the robot channel.
Mechanism
The zero gate moves the income toward "stay stable until the end", and under low-friction DR a wide stance has a slip tail, so survival outbids the width band; doubling the band's price bought back only 0.016 m - an auction that does not converge signals structural conflict, not an under-priced term.
Applies when
- porting reward mechanisms from an old lineage into a fresh recipe
- a width, margin or posture metric erodes during a late training phase
- a weight increase produces a negligible change in its target
“**定律候选(二实证):归零门 × 宽站互斥**。 … 加价翻倍只挽回 0.016,竞拍不收敛)。 … **归零门是 v2_5 血统的历史包袱,对 V3.1 配方是净负资产,P2 阶段除名 —— P1c 即终点形态**。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §49 终验(2026-08-14) Every gate on a penalty is an exit - to stop paying a stance tax the policy parked 2 deg outside a 30 deg uprightness gate (lunging), and, from scratch, just under a height gate (crouching); a positive band and an always-on guard fixed both
penalty-gate-is-an-escape-hatchNever gate a penalty on a state the policy can leave by getting worse; use always-on guards for what must never happen and positive, gated bands for what you want, and count every gate on a penalty as one more escape route to check in the logs.
Symptom
V2.9 added stance_width_task = relu(0.34 m - foot spacing) x standing gates, weight -10. Three checkpoints scored 0% on acceptance: standing height reached, feet on the ground, angular rate low, but the torso leaned 32.5/32.3/31.9 deg in a fore-aft lunge. The training dashboard read "tax paid off, base_height at full value".
Context
The penalty was gated by uprightness (tilt < 30 deg) and standing height; its tax had no time gate (500 steps x -1.65) while the standing income sat behind a 3 s zero gate (~325 steps), so leaning just past 30 deg lost a little gated income and saved the whole tax. base_height has no upright gate, so the lunge still collected it. The first metric also measured full horizontal spacing, so a staggered lunge counted as "wide".
Change
Two laws written down: a penalty may only carry gates the policy cannot escape by getting worse (make it an always-on guard) or it becomes a positive band ("not earned" is not "escaped"); and width is measured laterally in the base yaw frame. From scratch (V3.1 P1) with the lateral metric but the same gates, the policy parked just under the height gate instead (base_height 1.176/1.5, h ~ 0.30 m against a 0.3264 gate), the beta curriculum never advanced in 1,700 iterations, and the run was stopped early. P1b flipped the penalty into a positive band +2.0 x clamp(lateral / 0.34) x standing gates; P1c added yaw_guard = -5 x relu(|hip_yaw| - 30 deg), always on, no gate, no exemption.
Outcome
P1b: lateral stance 0.364 m, all four categories 100%, MuJoCo mu 1.0/0.4 both 100%. P1c: all six acceptance criteria passed for the first time on the line (hip-yaw saturation 1.2%), the guard's tax converging to -0.016 (almost never touched).
Mechanism
A gated tax that is not paid is saved, so the policy moves to the cheapest state just outside the gate; a gated income that is not earned is simply lost, so a positive band has no exit. HoST's style penalties are ungated or binary - the same law seen from the other side.
Applies when
- adding a penalty multiplied by an uprightness, height, phase or contact gate
- a policy settles just beyond a gate threshold
- training metrics look paid-up while acceptance collapses
“**罚项的门 = 策略的逃生门**。带直立门的负项可以靠"变得更差"(倾出门外) 全时免税;HoST 的 style 罚全部无门控/二值恰是同一律的反面实证。修律: 负项只许挂"变差逃不掉"的门(上限护栏 always-on),或改正向 band (收不到 ≠ 逃掉)。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §46 定案(血统内第四败 + 两条新律) Record-high training reward hid a fully-failing DR subgroup - aggregate metrics average over draws, gates must test per condition
aggregate-metrics-mask-subgroup-failureNever gate on metrics aggregated across DR draws: evaluate at fixed representative conditions (especially the deployment-critical stratum), and if a difficulty axis matters, ramp it on measured per-stratum success rather than sampling the full range from iteration zero.
Symptom
omni_s1e trained under constant-wide latency DR (0, 0.06 s) posted the lineage's highest-ever Isaac reward (129) - while the --delay 2 smoke evaluation showed 3/3 falls from iter 1500 onward, persisting to early stop; the usable checkpoint window shrank to iters 500-1000.
Context
Diagnosis written plainly: "聚合奖励掩盖重延迟尾部子群体失败" - the aggregated reward averages over latency draws, so the majority of light-delay environments can mask the total failure of the heavy-delay tail. The remedy for the training side was a survival-gated ratchet curriculum (survival_gated_latency): the sampling cap starts at 0.02 s and rises +0.01 only when a 4096-reset window's survival (time_out share) reaches >=90%, capped at 0.06, ratchet up-only - "增益与延迟耐受一起长,不升到策略撑不住的 地方" (gain and delay tolerance grow together; never raise past what the policy can hold). The detection side was already in place from the noise-crutch episode: the per-condition smoke curve, not the training reward, is the health readout.
Change
Latency exposure made curriculum-gated on measured subgroup survival instead of uniform-from-zero; per-condition (--delay 2) smoke evaluation kept as the authoritative curve; watcher scoring adjusted (survival weighted 3x) so recovery during hard phases is not early-stopped away.
Outcome
The failure mode was caught by the smoke curve within one generation; the follow-up redesign (deterministic staged latency) superseded the ratchet, but the aggregate-masking lesson held through both.
Mechanism
Expected-return training weights each DR draw by probability, so a subgroup can contribute bounded loss while being catastrophically failed; any scalar averaged over the randomization cannot distinguish "uniformly decent" from "great on easy draws, dead on hard ones". Only conditioning the evaluation on the stratum reveals the split, and curricula should raise difficulty on measured stratum success, not on schedule.
Applies when
- training reward hits records while a fixed-condition eval degrades
- wide DR on an axis where deployment sits at one known value
- designing curricula for difficulty axes (delay, push, terrain)
“常量 latency DR (0,0.06) 从零训被证伪——Isaac reward 129 历代最高,但 --delay 2 冒烟 iter1500 起 3/3 全摔持续到早停(聚合奖励掩盖重延迟尾部子群体失败,可用窗口只剩 500/1000)。… 采样上限 0.02 起步 … ≥90% 才 +0.01s,0.06 封顶,棘轮只升不降。”
train/OMNI_V0_SPEC.md § 3. S1.5(s1e 训练塌方复盘)