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
115 cards matching “per-step-income-drives-speed-time-gate”.
The get-up kept getting faster because standing earlier paid more every step - lowering torque authority barely slowed it, and only zeroing the standing income for the first 3 s moved the pace into the design band
per-step-income-drives-speed-time-gateWhen a skill is too fast, find the term that pays for finishing early and gate that income by time; keep the "get into position" term ungated so the policy does not learn to wait, use a ramp instead of a cliff, and confirm with a paired same-level experiment that the drift is motivational before changing it.
Symptom
The user judged the get-up too fast (Isaac medians about 0.7-1.6 s) and suspected path dependence: the policy seemed to get faster the longer it trained.
Context
Lowering the beta authority 0.40 -> 0.30 cut impact but moved supine only 1.70 -> 2.00 s: coordination-limited, not torque-limited. A paired experiment inside one beta level (checkpoint 15,600 vs 18,499, +2,900 iterations, same ruler) measured the drift: get-up medians -7 to -10%. The spec concluded the motive, not the path, was the cause - per-step standing income pays for every early step, and any lineage (even one from scratch) races toward the fastest solution inside its constraints.
Change
V2.5: the standing income (base_height, stand_pose, still, feet_on_ground) multiplied by w(t) = clamp(t/3 s, 0, 1); upright deliberately NOT gated, so righting and sitting up early still pay and the policy is not taught to lie flat and wait; a ramp, not a step. V2.5b: zero before t0 = 3 s, then a 1 s ramp.
Outcome
V2.5: Isaac 100%, get-up +17-43% slower, MuJoCo 100/100/98/100% (the best cross-simulator reading yet), still short of the 3.5-4.5 s design band - a linear ramp only discounts early income. V2.5b: MuJoCo supine 2.04 -> 4.10 s and prone 3.18 -> 4.04 s, inside the band; the Isaac pace barely moved (a lineage habit on a gradient-free plateau). Later the zero gate proved harmful when trained from scratch (curriculum-history-is-part-of-the-product) and in the V3.1 lineage (time-gate-vs-wide-stance-retire-the-fix).
Mechanism
Constraints on authority or velocity change how the fastest solution looks; the time structure of the task income decides how fast the fastest solution is.
Applies when
- a policy is faster or more aggressive than wanted and constraints do not slow it
- progress-style rewards pay every step spent at the goal
- performance drifts faster with more training at fixed settings
“**V2.5 机制(唯一)**:站立收入(base_height/stand_pose/still/feet_on_ground) 乘时间斜坡 w(t)=clamp(t/T_gate,0,1),T_gate=3.0 s;**upright 刻意不门控** (翻正/坐直早期照常拿钱,防"躺平等门开" … **用户假设量化 证实:逐步计酬动机在 β 包络内持续压缩时间,约束挡不住动机 —— V2.5 动机层 修法为正解。**”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §39 V2.5 预注册 / §39 补 配对实验 Narrowing the speed range to stop high-speed falls entrenched crouch-shuffling - judge gait quality at the speed that demands a gait
low-speed-commands-reward-draggingSet command ranges to include speeds that physically demand the target behavior, and evaluate behavior-quality gates at those speeds; when a restriction is added to suppress a failure, check what new optimum it creates at the remaining commands.
Symptom
After the command range was narrowed to (0.15, 0.35) m/s (to treat walk_v1's 134% overspeed and 8.3 s fall at 0.5), the policy settled into crouched foot-dragging; tracking rose monotonically with speed (63% at cmd 0.2, 76% at 0.3, 87% at 0.45), showing low speeds were where the degenerate gait was optimal.
Context
The narrowing advice was the author's own and is retracted in the file: it treated the symptom (falls at speed) while reinforcing the root cause (at 0.15-0.35 m/s, shuffling in a crouch is globally optimal - the Froude number is so low that even humans would not lift their feet). A zero-cost experiment confirmed the flip side: at cmd 0.5 the same policy met BOTH tracking (81%) and clearance (23.0/23.2 mm) standards.
Change
Speed range widened back toward (0.15, 0.5) - upper bound deliberately slightly above the mechanically feasible ~0.44 m/s so the policy finds the boundary itself; acceptance re-pointed: gait-quality criteria (tracking, clearance) judged at 0.45-0.5 m/s, low speed kept only as a survival check.
Outcome
v4 -> v6 progression under the widened range delivered 87% tracking with 34 mm clearance; the "low command = drag" account was confirmed by the monotone tracking-vs-speed curve.
Mechanism
Command distribution is part of the reward: physics prices gaits per speed, and at very low speed the energetic optimum is no swing phase at all. Restricting training to that regime makes the degenerate gait the correct answer to the posed problem - and grading a gait at a speed that does not require stepping measures nothing.
Applies when
- a gait degenerates after a command-range restriction
- quality metrics improve monotonically toward the range boundary
- writing acceptance criteria for gait quality vs survival
“现在看那个建议可能起了反作用:0.15~0.35 m/s 下蹲着蹭就是全局最优,抬腿反而亏。收窄治的是"高速摔倒"的症状,却强化了拖地的病根。… 验收标准里的 cmd 0.2 本身就是拖地速度(Froude 数极低,人在那个速度下也不抬脚)。accept_v2 应把速度跟踪与 clearance 的判定点改到 0.45~0.5 m/s”
train/WALK_DIAGNOSIS.md § ② 放宽速度区间 / ① 零成本实验 A joint-velocity penalty meant to slow the get-up cut joint speed 16% and left the get-up time unchanged - the knob never moved the variable, so the idea it was meant to test stayed untested
dof-vel-penalty-is-not-a-pacing-knobBefore reading a result as a test of an idea, check that the knob actually moved the independent variable; velocity regularizers smooth a schedule they do not set, and a schedule driven by per-step task income moves only when that income's time structure does.
Symptom
The get-up took 0.6-0.9 s in Isaac with large torque demand; the user proposed getting up more slowly so less torque would be needed.
Context
The idea had support in the accounts: the acceptance bound is an upper bound of 5 s (5-8x margin), the quasi-static squat path peaks at 25% of the limits, and rolling over needs no momentum. R3.2 raised dof_vel from -1e-3 to -5e-3 as the single variable.
Change
dof_vel -1e-3 -> -5e-3 (child-run from R3.1).
Outcome
Get-up medians moved +0.02-0.04 s (noise); raw joint velocity -16%; torque demand median got worse (hip_pitch 46-48% -> 63-67%) as the new term competed with torque_headroom on the same joints; MuJoCo 98 -> 96%. Verdict FAIL on the knob, not on the idea, and the rung was not adopted. When pace was later attacked through the income's time structure (V2.5/V2.5b), the MuJoCo get-up moved into the 3.5-4.5 s design band.
Mechanism
The pace was set by base_height_progress paying for every step spent high (stand earlier, earn more); a velocity regularizer only smooths motion along the same schedule and does not change when the robot stands up.
Applies when
- trying to make a skill slower or gentler with smoothness penalties
- an experiment's primary metric did not move and a verdict is being written
- two penalties act on the same joints
“**关键判读:`dof_vel` 罚只把关节速度压了 16%,而起身用时一点没变。** 也就是说**这一级根本没有把"慢下来"这个自变量推动起来** —— 所以它**不构成对 用户假说的检验** … 起身节奏由 `base_height_progress` 的逐步计酬决定(早站起来就多 拿),速度正则只在同一条时间轨迹上把动作抹匀,不改变何时站起来。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §25 R3.2(dof_vel −1e-3→−5e-3,慢一点起身) 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) 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) The 1.5 Hz step-frequency gate was retracted - the author had misread his own actuator data, and the limit fought pendulum dynamics
gate-threshold-retracted-frequencyEvery gate threshold must cite its measurement and survive a first-principles sanity check; when a gate keeps failing otherwise healthy behavior, re-derive the threshold from the raw data before enforcing it again - and retract wrong gates in writing.
Symptom
An acceptance criterion "step frequency <= 1.5 Hz" kept failing healthy policies (walk_v4 at 2.33 Hz), and an earlier attempt to force slower stepping (v3) had killed stepping altogether.
Context
The threshold had been derived from the author's own actuator frequency-response measurements - but re-reading the raw table showed the misread: amplitude ratio at 2.0 Hz is 0.88 (knee) / 0.83 (ankle), acceptable; the genuinely bad point was walk_v2's 3.8 Hz at 0.58. Mechanism check agreed: the leg as a compound pendulum (L ~ 0.30 m) has a natural frequency ~1.1 Hz, swing half-period 0.45 s - the observed 2.1-2.4 Hz sits near where the leg wants to swing, and forcing 1.5 Hz "是跟摆动动力学对着干" (fights the swing dynamics). The frequency definition itself was pinned by two independent methods (contact-event counting 2.39 Hz vs FFT 2.33 Hz, agreeing): reported numbers are cycle frequency = steps per leg per second.
Change
Gate retracted in writing: "步频 ≤1.5 Hz 应删除或放宽到 ≤2.5 Hz"; frequency definition standardized before entering any config.
Outcome
walk_v4/v6's 2.1-2.4 Hz reclassified from disease to normal; the v3 failure got its probable explanation (suppressing stepping to meet a wrong gate).
Mechanism
A gate is only as good as the measurement and the reading behind it; thresholds inherited from a misread plot become invisible design constraints that later training obeys at real cost. Cross-checking a threshold against first-principles dynamics (pendulum frequency) is a cheap way to catch such misreads.
Applies when
- an acceptance threshold repeatedly fails policies that look healthy
- thresholds were set from a single person's reading of raw data
- a forced compliance with a gate degrades the behavior it guards
“我当初依据自己测的执行器频响定的,但看错了区间。… 2.0~2.4 Hz 的幅值比 0.83~0.88 是可接受的;真正不行的是 walk_v2 的 3.8 Hz。… 腿按复摆算(L≈0.30 m)自然频率约 1.1 Hz … 把它压到 1.5 Hz 是跟摆动动力学对着干(walk_v3 把迈步压没了,可能正是这个原因)。”
train/WALK_DIAGNOSIS.md § ② 撤回"步频 ≤1.5 Hz"这条验收标准 —— 是我定错了 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 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 Three hardware accounts locked the run design point - and the knee's real speed ceiling is tau_limit/kd, not the firmware limit
feasibility-accounts-lock-design-pointBefore opening a dynamic-gait training line, compute the full account set - tau_limit/kd effective speed ceilings, joint ROM under the intended reference geometry, and thermal RMS at the duty cycle - and let the accounts lock the design point; move only to pre-registered in-table alternates, re-running the accounts first.
Symptom
The run line was believed to require a firmware raise of the RS06 speed limit (10 rad/s) as a hard precondition, and the feasibility script's motor-envelope scan had marked 80/100 mm foot-lift cells "physically feasible".
Context
Three added accounts re-decided everything. (1) Damping tax: in MIT mode tau = kp*(q_des-q) - kd*qd, so sustained rotation is capped at tau_limit/kd = 12/1.5 = 8 rad/s - below the firmware's 10; at peak speeds 6.7-7.9 rad/s the damping term alone eats 10.1-11.9 N*m (84-99% of the torque limit). "提固件 limit_spd 越不过这道税 —— 它是 kd 与限扭的比,不是固件旋钮." (2) Joint ROM: the feasibility script had checked motor envelopes but NOT joint range - the ankle-pitch ROM caps 1:2:1 leg-shortening lift at 62 mm (soft) / 77 mm (hard), so the 80/100 mm "feasible" cells were voided; also firmware-independent. (3) Ankle thermal: duty 0.40 puts ankle RMS at 87% of continuous rating (0.35 -> 93%); long-period big-stride cells hit both ankle torque peak and heat. Verdict: firmware raise DEQUEUED (50 mm design point needs knee 6.7-7.3 < the 8 rad/s effective ceiling < firmware 10); vel_limit stays 10 so sim == robot. The three accounts uniquely lock the design point - 50 mm lift / T 0.60 s / duty 0.40 - "三笔账 唯一锁定,不是调参空间", with pre-registered alternates allowed only inside the table and only after re-running the accounts.
Change
Design point frozen from accounts; hardware precondition reversed by arithmetic rather than by test; reference amplitude (0.84 rad = FK inverse of 50 mm) derived, per-joint action scales sized to the required travel (knee 0.9, hip_pitch 0.6, ankle deliberately NOT amplified - hard limit is adjacent).
Outcome
A firmware work item left the critical path; an infeasible region of the design space was closed before any training; the remaining risk (knee tracking lag from the damping tax) was pre-registered with its own criterion and in-table fallback (duty 0.35) - "这不是'奖励没调好', 是 plant 账".
Mechanism
PD actuators in MIT mode pay kd*velocity out of the same torque budget that tracks position, so the effective speed ceiling is a ratio of configuration constants, invisible to firmware settings; and feasibility is the intersection of ALL constraint families (torque envelope, joint ROM, thermal RMS) - a scan that omits one family certifies impossible cells.
Applies when
- planning running/jumping or any high-rate gait on PD actuators
- a firmware or hardware upgrade is assumed as a training precondition
- a feasibility scan covers motor limits but not ROM or heat
“膝的有效速度顶 = τ_limit/kd = 12/1.5 = 8 rad/s,不是固件的 10。… 提固件 limit_spd 越不过这道税 —— 它是 kd 与限扭的比,不是固件旋钮。… 可行性脚本只查了电机包络没查关节 ROM —— 其 80/100mm 的"物理可行"格作废。… 判决:RS06 提固件对 run v0 不是前置,出队”
train/RUN_V0_SPEC.md § 1. 硬件账判决 / 2. 步态设计点 Training at scale 0.4 is NOT the twin of deploying 0.5 at power 0.8 - the algebra matches, the learned policy does not
deploy-scaling-not-training-equivalentNever assume deploy-side scalings can be folded into training-time constants ("burning the crutch into training"): the learned optimum depends on the training-time authority, so treat such conversions as full experiments with pre-registered expectations and a sim2sim gate before any hardware.
Symptom
s1g (S1.6) trained from zero at action_scale 0.4 - meant as the "training twin" of the hardware-proven s1c-at-power-0.8 (0.8 x 0.5 = 0.4) - was all green in Isaac (zero falls, reward 117) yet scored 0/3 across all eight checkpoints and 0/20 at 20 seeds in the MuJoCo gate, falling forward at median 1.57 s with a 2.9x speed overshoot.
Context
The pre-registered expectation (survival gate should pass, since the conviction matrix showed s1c@0.8+delay2 all-survive) was cleanly falsified, and the harness was acquitted by controls: --delay 0 fell identically (not a delay fragility), check_contract all green, and s1c through the same harness survived 2/3. The verdict: "「s1c@0.8 = 0.4 训练孪生」的代数等价不成立" - a policy deployed with a derated output still LIVES in the 0.5 internal model it trained under (its value function, its expectations of its own authority), while a policy that starts training with reduced authority learns a different, clip-hugging gait with zero margin for plant differences ("部署端打折的策略活在 0.5 的内模里,训练起点收权限学出的是贴 clip 的 另一套步态,对 plant 差异零余量"). Result: the policy was withdrawn before hardware ("撤回——不上真机"), the lineage root moved back to the 0.5-contract s1c-5500, and this became the C ladder's cited fact-check ("s1g 是 0/20 证伪出局的那一代").
Change
The amplitude-surgery route abandoned; contract kept at scale 0.5; the deploy-side 0.8 crutch later retired on its own merits when the delay-complete s1e generation ran at full power.
Outcome
One training run bought a clean falsification of a plausible algebraic identity; no hardware time was spent on it because the sim2sim gate caught it.
Mechanism
Output scaling commutes with the network arithmetic but not with learning: the training-time scale shapes which gait solutions are reachable and how much clip headroom the optimum keeps. A derated mature policy retains the wide-authority solution executed softly; a from-zero narrow-authority policy finds a different optimum that saturates its smaller envelope - the two are not the same controller in different units.
Applies when
- proposing to move a deployment derating into a training constant
- a scaled-down contract policy hugs the action clip
- Isaac-green / cross-sim-zero results on a re-scaled lineage
“预注册 a) 证伪——Isaac 全绿(零摔/reward 117)但 MuJoCo --delay 2 八档 checkpoint 扫描全数 0/3、iter6500 20-seed 0/20 … 「s1c@0.8 = 0.4 训练孪生」的代数等价不成立: 部署端打折的策略活在 0.5 的内模里,训练起点收权限学出的是贴 clip 的另一套步态,对 plant 差异零余量。”
train/OMNI_V0_SPEC.md § 3. S1.6 判决(2026-08-07 验收) Slowing the gait clock at deployment is out-of-distribution and backfires - lower the commanded speed instead, or train the knob
cycle-time-override-is-oodAny deployment override must correspond to a dimension the policy was trained to handle; to make a parameter field-adjustable, randomize it in training and observe it - otherwise use the levers inside the trained envelope (commands) and leave the knob alone.
Symptom
Real-robot feedback "walks very fast and unstable" suggested slowing the gait; a deploy-side --cycle-time override existed, making "just slow the clock" a one-flag temptation.
Context
A sim sweep of the override on walk_v6 @cmd 0.3 showed monotone degradation away from the trained 0.40 s cycle: at 0.50 s tilt jumped 7.9 -> 13.2 deg and landing force 1.52x -> 2.24x; at 0.80 s (half speed) clearance collapsed to 3 mm - dragging again - with 20 deg tilt. Meanwhile the legitimate lever, lowering the commanded speed with the clock untouched, improved everything monotonically: cmd 0.1 gave 104% tracking, 6.7 deg tilt, minimum slip - the most stable operating point. The file distinguishes the two "slows" explicitly: lower command = smaller steps at the same 2.5 Hz rhythm; a slower rhythm itself requires retraining - randomize cycle_time (e.g. 0.40-0.65 s) during training and expose it as an observation, and only then does --cycle-time become a field-adjustable knob.
Change
Deployment guidance: never ship a cycle-time override the policy was not trained under; respond to "too fast/unstable" with lower commands; schedule clock variability as a training-time (contract-level) change if a field knob is wanted.
Outcome
The sweep quantified the trap before hardware paid for it (dragging and 2.2x landing force at slowed clocks); cmd 0.1 documented as the stable demo point.
Mechanism
The policy is a function fitted around the training distribution; a deploy-side override moves an input (phase rate) to values never seen, so behavior degrades unpredictably - the knob LOOKS like a capability because it exists in the code, but capability lives in the training distribution, not the interface.
Applies when
- a deploy tool exposes overrides (clock, scale, gains) beyond the training distribution
- hardware feels "too fast/aggressive" and a quick knob exists
- deciding between a deploy-side tweak and a retrain
“0.80s | 1.25Hz | 0.165 | 3mm(拖地) | 20.0° … 慢一半直接崩 … 策略按 0.40 训练, 别的周期属分布外。… 降指令速度才是有效杠杆 … cmd 0.1 是最稳的工作点。… 要节奏本身变慢必须重训 —— 训练期把 cycle_time 随机化(如 0.40~0.65s)并作为观测的一维, 部署时 --cycle-time 就成了现场可调的旋钮。”
train/WALK_DIAGNOSIS.md § 2026-08-01 追加: 调慢步态时钟(--cycle-time)在仿真里是反效果 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) A stand gate judged by survival passes a robot that wanders a meter - judge posture instead
stand-gate-posture-not-survivalFor every gate, ask what behavior the metric is a proxy for and validate its ordering against real observations; replace metrics whose ordering disagrees with reality, and demote them explicitly rather than silently.
Symptom
Real robot "standing" drifted 0.5-1.4 m across the floor while the sim stand gate scored a clean 20/20 - because the gate's metric was episode survival, which wandering does not violate.
Context
C2's stand condition was originally written as "survival regression <=2/20". A real-robot counter-example on 2026-08-08 forced the re-judgment: wandering robots survive. Cross-checking candidate sim metrics against real-robot feel showed max-tilt median ordering agreed with hands-on ranking, while displacement ordering was actually OPPOSITE to real impressions - so displacement was demoted to a reference quantity, not a gate.
Change
Stand PASS criterion rewritten from survival to posture: "stand tilt max median <= root baseline +1.5 deg"; displacement kept only as reference. Applied to all subsequent rungs (C4 and redo levels inherit it).
Outcome
Later rungs gated stand on tilt (e.g. C4 product: 7.7 deg vs parent 7.1 deg, +0.6 deg PASS); the wandering failure mode became visible to the battery instead of hidden by survival.
Mechanism
A gate metric is a proxy for an intended behavior; survival is a proxy for "did not fall", not "stood still". Metric choice must be validated against ground truth (real-robot feel/measurement), and a proxy whose ordering disagrees with reality on real data is worse than no metric - it steers selection backwards.
Applies when
- writing PASS conditions for stand/idle/hold behaviors
- a gate passes policies that visibly misbehave on hardware
- choosing between candidate metrics for an acceptance battery
“stand 必须用位姿判，不能用存活判（2026-08-08 真机反证改判）：站着乱走 0.5~1.4 m 时存活照样 20/20 —— C2 的 stand 条件原写「存活退化 ≤2/20」，选错了指标。改为: stand 倾角 max 中位 ≤ 根基线 +1.5°（倾角与真机手感排序一致；位移排序与真机相反，降为参考量）”
train/C_LADDER_RUN.md § 3b. PASS 条件 ⚠️ stand 必须用位姿判 When the training reset distribution changes, freeze the acceptance distribution separately and pin its seed - the same checkpoint measured twice differed by 3.8 and 10.2 points
frozen-acceptance-distribution-and-pinned-seedAcceptance distributions are frozen artifacts, decoupled from whatever the training distribution becomes and evaluated with a pinned seed, and every gate row carries its sample size so that a small-n row is never read as a regression.
Symptom
R0.3 added easier roll-arc start states to the training resets, and the acceptance script shared the training category table; separately, the same checkpoint scored side 3.8 points and mid 10.2 points differently on two runs of the same script.
Context
Acceptance ran 512 parallel envs drawn from the fall categories. Had it kept following the training table, 15% of acceptance samples would have landed on states easier than prone - inflated scores and generations that could not be compared. The two same-checkpoint runs had identical per-item height medians, so the ruler had not changed; the spread was reset resampling noise (side n ~ 169, sigma 2.3%; mid n ~ 35, sigma 8.3%). The spec's "20 seeds" had always meant controlled seeds.
Change
accept_recovery.ACCEPT_CATEGORIES pinned to the four R0-R0.2 categories and decoupled from the training FALL_CATEGORIES; --seed 20260809 pinned, after which two consecutive runs were bit-identical. The mid row (n ~ 35) was labelled the bluntest gate.
Outcome
Every later generation (R0.3 through V3.1) was scored on the frozen distribution and seed, which is what let R0.3's intermediate state be read as "no measurable gain" (62.7 -> 62.1%) and R0.2's mid drop be booked as noise rather than a regression.
Mechanism
An acceptance set that follows the training distribution measures a moving target, and an unpinned reset draw adds sampling noise that small-n rows cannot absorb.
Applies when
- the training reset or command distribution changes between generations
- repeated evaluations of one checkpoint disagree
- a small category drives a pass/fail decision
“**分布冻结**:`accept_recovery.ACCEPT_CATEGORIES` 钉死 §5 四类 … 与训练侧 `FALL_CATEGORIES` **解耦**。 … **side 差 3.8 点、mid 差 10.2 点**(h 中位逐项一致,证明不是尺子变了)—— 纯 reset 重采样噪声 … 钉死后两次连跑逐位相同。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §14 验收尺子的两处加固(R0.3 起生效,向后兼容) 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 Prove a new penalty actually fires - two ways a clearance term silently did nothing
inert-reward-term-auditBefore training with a new reward term, log its realized per-step value under the current policy and confirm it is nonzero where intended - check coordinate zero-points against FK and check who occupies the term's gate; and never weaken the term that creates the states your new term needs.
Symptom
A newly designed swing-height clearance penalty could have trained as a no-op twice over, and the companion advice to lower feet_air_time actively backfired when tried.
Context
Instance 1 (zero-point offset): the proposed code used body_pos_w of the foot link, but that is the ankle_roll_link frame origin, which sits 0.0585 m above the ground even with the foot flat on it - so (0.03 - 0.0585) is always negative and the penalty is永远 0; the 0.0585 offset must be subtracted (verified identical in MuJoCo FK and Isaac). Instance 2 (gate occupancy): the clearance penalty fires only in swing phase; a dragging policy keeps both feet in contact, so the penalty is constantly 0 for exactly the policy it was meant to fix - and worse, any slight lift immediately incurs it, a reverse threshold. Lowering feet_air_time to 0.5 on that advice measurably collapsed air time to 0.0002 (below v3). Corrected understanding: "clearance 是把已有的摆动相抬高, 造出摆动相仍要靠 air_time" - air_time creates the swing phase, clearance raises it.
Change
Fixed the height zero-point; kept feet_air_time as the swing-phase creator with clearance layered on top; both errors documented as corrections to the team's own earlier advice.
Outcome
With both fixed, swing height rose from 22-23 mm (v2) to 29 mm (v5) to 34 mm (v6); the inert-term failure class entered the standing checklist.
Mechanism
A penalty's gradient exists only where its gate is occupied and its argument crosses its threshold; frame offsets shift the threshold out of reach, and phase gates can have zero occupancy under exactly the policy being treated. Terms interact as an ecology - one term must create the states in which another can act.
Applies when
- adding any gated or thresholded penalty (clearance, impact, slip)
- a new term produces no behavioral change at any weight
- body-frame positions are used in reward code
“body_pos_w 是 ankle_roll_link 坐标系原点,平放触地时仍高出地面 0.0585 m。… (0.03 − 0.0585) 恒为负 → 惩罚永远是 0 … clearance 惩罚只在摆动相生效,拖地时两脚始终触地 → 惩罚恒 0;而一旦轻微抬脚就立刻扣分,对正在拖地的策略是反向门槛。… 正确认识:clearance 是"把已有的摆动相抬高",造出摆动相仍要靠 air_time。”
train/WALK_DIAGNOSIS.md § walk_v4 独立验收 — 本文档给的两处代码/建议是错的 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 Curriculum-gate a penalty to the phase where its disease occurs - early on it only taxes exploration
gate-penalties-to-the-disease-phaseFor penalties aimed at late-stage pathologies (freezing, saturation, degenerate attractors), ramp the weight in only after exploration noise has decayed; anchor the terminal weight to measured healthy-vs-sick raw values, and shift all related tripwires to after the ramp completes.
Symptom
The action_saturation penalty, applied from iteration 0 in v8a, taxed exploration itself: with init_noise_std 1.2 the sampled actions paid ~-2.45/step before any policy had formed - while the disease it targets (clamp freezing) is a LATE pathology (v9 froze at iteration ~2624).
Context
v10 re-introduced the same penalty behind a curriculum gate: weight 0 until iter 1000, ramping linearly to -1.0 by iter 2000 - present only when the disease can occur, absent while exploration noise dominates. The trust argument was evidence, not hope: in v8a the term, while active, had pulled joint_pos_ref from 0.041 up to 0.155 and climbing - proof it can extract a policy from the frozen pit. Weight magnitudes were anchored to measured raw values (healthy v5 0.310 / v6 0.106 vs frozen v7 1.145 / v9 1.22 per step: at -1.0 healthy pays 6-18% of tracking, frozen pays 60%+, standing ~0). v10c then isolated the gated term as THE anti-freeze mechanism by single variable, upgraded to untouchable status in v11: "S 的门控机制(v10c 单变量铁案:任何情况下 不许撤,只许调终值)" - and v11 dared to relax other penalties only because S stood guard.
Change
action_saturation gated 0 -> -1.0 over iters 1000-2000 (later terminal value tuned -1.0 -> -0.5 with the gate mechanism itself frozen); tripwires adjusted to respect the gate's timing (freeze check moved to iter 2500-3000 to give the ramped term its effect window).
Outcome
Freezing stopped recurring while early training kept full exploration; the mechanism graduated from experiment to invariant within two versions.
Mechanism
A penalty's incidence depends on who occupies its support: early in training that is exploration noise (whose suppression starves learning), late it is the converged pathology. Time-gating aligns the penalty's presence with its target's presence, buying the constraint without the exploration tax - and tripwire timing must then be computed from the gate schedule, not from ungated precedents.
Applies when
- a structural penalty punishes exploration in early training
- a late-onset pathology (freeze/saturation) needs a standing guard
- deciding when a curriculum ramp should engage
“v8a 实锤它的病根是"罚在采样动作上"——init_noise_std 1.2 的早期等于罚探索(~−2.45/步);而冻结是晚期病(v9 速率 2624 才死平)… 门控让它只在病发期在场。… v8a 里它在场时 joint_pos_ref 从 0.041 爬到 0.155 且仍在升——有从低谷爬出的实证。”
train/WALK_V10_SPEC.md § 2. S 保险 —— action_saturation 课程门控 Ideal PD is not enough - add a delay buffer and fit armature/friction/delay per joint
actuator-delay-buffer-fittingNever ship ideal PD to hardware: add a measured delay (in control steps) and per-joint armature/friction fitted from step and sine responses, and treat remaining actuator mismatch as your standing largest sim2real residual.
Symptom
Standard ideal PD actuator model transfers poorly; sim assumes targets take effect instantly and joints reach arbitrary acceleration.
Context
A developer with a successful on-hardware Isaac Lab biped modified the actuator model in two ways and calibrated it against the real robot: step-response plus sine-sweep tests (positive step, negative step, sine tracking), overlaying sim curves on measured curves and hand-tuning.
Change
(1) Delay buffer: action targets take effect after a uniform 6 time-step delay on all joints; (2) acceleration limiting so the actuator cannot reach arbitrary acceleration; (3) per-joint fit of armature / friction / delay - different joints genuinely needed different values.
Outcome
Hip joints fit worst, knee best; the developer rated the result "not perfect, the best I could do" and still listed actuator-model improvement as next work - i.e. even the fitted model remained the dominant residual.
Mechanism
Real actuation is a lagged, bandwidth-limited system; a delay buffer and acceleration cap are the two cheapest structures that reproduce its phase and magnitude response. Per-joint differences come from differing load, wiring, and friction states, so a single global constant underfits.
Applies when
- actuator model in sim is ideal PD with no delay
- step-response of real joint visibly lags or overshoots the sim's
- budgeting which sim2real gap to attack first
“标准 ideal PD actuator 不够用，他改了两处：延迟缓冲：目标不是立即生效，全部关节统一 6 个 time step 延迟 / 加速度曲线：执行器不能瞬间达到任意加速度 … 用 armature / friction / delay 三个参数逐关节拟合，标定方法是阶跃响应 + 正弦扫描 … 髋部关节偏差最大，膝关节最好。”
Experience.md § 执行器建模 —— 最值得抄的一条 (lines 50-59) Ungated phase shaping made standing 42x more expensive than stepping - and the stepping was cooking the hip motors
moving-gate-42x-stand-taxGate every phase/clock-driven shaping term on the command that justifies motion, price the cmd=0 case explicitly during design - and when a reward flaw is sim-only, book it with trigger conditions instead of operating immediately on a working lineage.
Symptom
At cmd = 0 the sim policy never stood still - it stepped in place and crept 0.98 m per 20 s; on the real robot the same lineage's stepping made hip_roll motors run 20 degC hotter than every other joint (43-48 degC vs 25-28).
Context
The arithmetic closed it: the three phase-shaping terms (joint_pos_ref 1.6, feet_contact_number 1.2, feet_clearance_swing 1.6) are driven by the gait clock with NO command gating, so standing at cmd=0 forfeits 3.32/step of shaping while honest standing earns only 0.078 of tracking - stepping wins 42x. The heat chain: perpetual stepping = perpetual single support = one hip_roll stalled at ~4.3 N*m (25% of torque limit) carrying the torso's frontal-plane moment - two hip_rolls = 90% of whole-machine steady-state I2R; measured stand-vs-step comparison: total heat -71% when actually standing. The suspended test acquitted the actuator (0.21 rad sag -> 0.0008 rad in air) and mechanics vetoed the easy fix ("降 kp 救不了热" - equilibrium torque equals the external load regardless of kp). The fix (moving_gate: hard-gate the three shaping terms on |cmd| > eps) was designed - then DEFERRED by the user because the real robot at the time stood fine: "真机不表现该问题, 为真机不存在的病改奖励表不划算", with written trigger conditions (C-ladder stand row persistently failing, or real robot starting to step/drift at cmd=0) and the known hazard tag (this is exactly the reward-change class that triggers the B-arm signature). When the real robot later DID step and cook, the booked trigger fired and moving_gate moved from debt to to-do with its benefit re-priced: "停止空烧 hip_roll,稳态发热降 ~71%".
Change
moving_gate designed with the gate_by_cmd convention; deferral, triggers, and expected heat recovery all pre-registered instead of patching the reward for a then-sim-only symptom.
Outcome
The cmd=0 stepping went from mystery to closed arithmetic; the thermal measurement (hip_roll +20 degC) quantitatively confirmed the 90%-of-heat prediction; the reward change waited for real-world justification instead of spending a risky revision early.
Mechanism
Clock-driven shaping terms define a perpetual-motion bounty unless gated by command; the resulting idle gait is not a training bug but the table's optimum. Its cost surfaces on hardware as stall-torque heating set by statics (mass x lateral offset), which no gain change can remove - only removing the motion (gating) or widening the stance can.
Applies when
- the policy steps in place or creeps at zero command
- specific joints run hot in idle behaviors
- deciding when a known reward flaw justifies a risky mid-lineage fix
“塑形合计 −3.32/步 … 净: 站定亏 42 倍 … 两颗 hip_roll 4.29 / 4.09 N·m(各占限扭 25%),占全机稳态 I²R 的 90% … 吊挂实测 hip_roll 跟踪误差 0.21 rad → 0.0008 rad … 降 kp 救不了热 … 真机不表现该问题, 为真机不存在的病改奖励表不划算”
train/README.md § C1 FAIL 节 (cmd=0 的 sim/真机分歧记账) / C2 真机 A/B 三b 发热定性 Gate a new reward term by its command so all old modes score pointwise identical
gate-new-reward-terms-by-commandWhen a reward term must be added mid-lineage, gate it on the condition that defines the new task so every pre-existing situation scores exactly as before - and still watch for value-rescale pathologies inside the new mode.
Symptom
Adding a lateral tracking reward (track_lin_vel_y_exp) ungated would have paid 0-2.0 per step even in modes with cmd_vy = 0 (healthy gait sway of vy ~0.1 already earns 1.28), shifting the whole reward table by a large bias and rescaling the value function - no longer "just adding one mode".
Context
C4 was the C ladder's only true reward surgery. Single-variable discipline required that the change be invisible to every existing mode. The chosen construction: gate_by_cmd=True - the term pays only when |cmd_vy| > 0.02, so for all modes with cmd_vy == 0 the term is pointwise zero, i.e. the reward is pointwise identical to before the change. The same trick appeared earlier in C1: replacing the vy L2 tax with a command-error version that is "对 cmd_vy≡0 逐点同值" (pointwise equal when cmd_vy is 0), explicitly classified as not-a-reward-change.
Change
track_lin_vel_y_exp added with gate_by_cmd=True (weight +2.0, std 0.15); the residual acknowledged honestly - inside the side bucket the values DO change, so the rung still watched the known reward-reshuffle pathology signature (s1c B-arm: scatter -> half-recover -> collapse) as a stop criterion.
Outcome
Old modes provably unaffected (pointwise-equal argument); attribution for any change in old-skill metrics stayed clean through the C4 redo series.
Mechanism
PPO's critic normalizes to the reward scale it sees; an ungated additive term shifts returns in every state and re-scales advantages globally, entangling the new skill with all old ones. Command-gating confines the new term's support to the new mode's state distribution, making "pointwise identical elsewhere" a provable property rather than a hope.
Applies when
- adding a tracking/shaping term for a new command or skill to a lineage that must not regress
- reward change proposed while other skills are still being gated
- reviewing whether a config diff counts as a reward change
“只在 |cmd_vy| > 0.02 时付。不门控的话它对 cmd_vy≡0 的老模式也给 0~2.0 分（健康摇摆 vy≈0.1 → 1.28），等于给整张奖励表加一个大偏置、值函数尺度全变 … 门控后老模式逐点得 0 = 与加项前逐点同值，单变量纪律成立。… 但 side 桶内的值确实变了 —— 这仍是奖励表改版，开级盯 s1c B 臂签名”
train/C_LADDER_RUN.md § 3d. gate_by_cmd=True（重要） action_rate weight is the sim2real bandwidth knob - re-tune it whenever a rate limiter is removed
action-rate-weight-vs-bandwidthSet action_rate weight relative to real actuator bandwidth, and re-tune it any time another smoothing/limiting element (filter, slew limiter, gain) changes - reward weights are load-bearing parts of the actuator model.
Symptom
With a low action_rate_l2 weight the policy learns fast actions; the unmodeled part of the actuator response is then excited hardest, and sim2real "直接崩" (collapses outright). With too high a weight, actions become so slow the robot cannot maintain balance.
Context
The reference developer called action_rate_l2 the single most important reward for transfer, with side-by-side video evidence that the high-penalty, slower policy is clearly better on hardware. Lucen context: the team had just removed the SOFT_SPD=1.0 velocity limiter, which had been an implicit actuator-bandwidth constraint - leaving action_rate as the only remaining constraint on action speed.
Change
Decision recorded: after removing SOFT_SPD, re-evaluate the action_rate weight rather than keep the old value, since its effective role changed from "additional smoother" to "sole bandwidth constraint".
Outcome
Logged as a priority follow-up ("重新评估 action_rate 权重 - 拆掉 SOFT_SPD 之后这一项的作用变了"); the failure mode it guards against is training high-frequency actions the real actuators cannot track.
Mechanism
Slower actions stay inside the frequency band where the ideal-PD sim actuator and the real actuator agree; fast actions probe the band where unmodeled delay, inductance, and bandwidth limits dominate, so model error is amplified in exact proportion to action speed. Any removed external rate limit transfers that constraint's entire job onto the action_rate penalty.
Conflicts
The low/high tradeoff evidence is the external developer's report (with video); the Lucen-side entry is a pre-registered risk and decision, not yet an on-robot A/B at the time of writing.
Applies when
- removing or adding an action filter, slew limiter, or low-level speed cap
- real robot shows high-frequency chatter or overheating absent in sim
- tuning smoothness rewards before a hardware deployment
“权重低 → 动作快 → 执行器模型不准的部分被放大，sim2real 直接崩 / 权重高 → 动作慢 → 好迁移，但可能慢到无法维持平衡 … 我们刚拆掉 SOFT_SPD=1.0 的限速器，等于把执行器带宽约束整个移除了。action_rate 惩罚现在是唯一还在约束动作速率的东西，需要重新评估权重”
Experience.md § action_rate_l2 是他认为最关键的 reward (lines 61-70) The deploy-side walk/recovery switch - into recovery at tilt > 65 deg held 0.3 s, back at tilt < 15 deg with angular rate < 1 rad/s and straight knees held 1 s, a 15 s timeout, last action cleared both ways - and the handoff steps the design required are only partly implemented
walk-recovery-fsm-handoffSpecify a deploy-time controller switch as hysteretic, time-filtered predicates the robot can measure (proxy what it cannot, e.g. straight knees for height), a timeout that ends in a safe stop, and a complete handoff (history, clock, last action, command ramp) - then test that the code performs every handoff step, because the design document is not the implementation.
Symptom
With a recovery policy and a locomotion policy as separate networks, the robot needs a switch: when is it "fallen", when is it "up", and what state must be reset so the next policy does not act on the previous one's history.
Context
The 08-09 design: enter recovery when fallen (tilt > 55 deg or height < 0.60 x 0.384 m) for 150 ms, leave for a stand-hold when upright (tilt < 12 deg, height > 0.85 x 0.384 m, feet steady, |omega| < 0.8) for 400 ms - wide entry, strict exit, hysteresis - then a mandatory handoff trio before walking resumes (reset the walking policy's observation history, restart its phase clock at 0, clear its previous action and latency buffer) and a command ramp instead of a jump. The 08-14 implementation in deploy_policy (--recovery-policy): each policy under its own manifest contract (walk: nominal + scale; recovery: beta-anchored), the same rl_default gains, the tilt cutoff disabled; RECOVERY at tilt > 65 deg for 0.3 s; LOCO again at tilt < 15 deg and |omega| < 1 and knees straight (< 0.35 rad) for 1 s - the robot's computer has no height estimate, and straight knees stand in for height so a V3.0-style upright kneel cannot pass as standing; RECOVERY longer than 15 s ends in a safe stop; last_action cleared on both switches; command forced to 0 during RECOVERY; power scaling applies to LOCO only.
Change
An open account was written down with it: the recovery end state (0.355 m stance, hip yaw -/+27 deg) is outside the walking policy's training start distribution, so the first test must use stand / zero command as LOCO, and the long-term fix is to widen the walking policy's initial states rather than bend recovery's stance to suit walking.
Outcome
The spec records only a successful compile (py_compile); the hang test was left to be done on site. The operator runbook carries the three-step procedure (hang with stand as LOCO, mat and push, then omni walk as LOCO) but no outcome.
Mechanism
Two policies trained separately each assume their own history, clock and last action; a switch that carries any of them across feeds the next policy a state it never saw - the design called this "the walking policy seeing a ghost history".
Conflicts
The 08-09 design requires resetting history, clock and previous action plus a command ramp; the 08-14 implementation records last_action clearing and a zero command during recovery; the one-leg spec of 2026-09-14 lists the handoff hygiene as specified but not implemented - reset_history() is called by nothing (a 215-dim policy would carry four frames of pre-fall history), the phase clock is not zeroed, and there is no command ramp back to LOCO. No hardware run of the FSM is recorded in either source.
Applies when
- switching between separately trained policies on hardware
- a policy with history or phase observations is re-enabled mid-run
- the robot lacks a sensor the switching criterion was designed around
“**判据**:进 RECOVERY = 倾角 >65°(`--fall-tilt-deg`)持续 0.3 s;回 LOCO = §5 真机可测子集:倾角 <15° ∧ |ω|<1 ∧ **膝直 <0.35 rad(NX 无高度观测, 高度门用膝直代理 —— 防 V3.0 型"跪坐但直立"误判)** 持续 1 s (`--recover-hold`);RECOVERY 单次 >15 s(`--recovery-max-time`)安全停。 … **切换卫生**:两向切换 last_action 清零;RECOVERY 态 cmd 强制 0”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §50 FSM 双策略调度(2026-08-14,用户令):deploy_policy --recovery-policy Two simulators disagreed 1.8x on one policy's torque demand - four explanations were eliminated with numbers, the surviving suspect was never tested, and neither reading was allowed to cancel the other
torque-disagreement-between-simulators-unresolvedWhen two simulators disagree on a safety-relevant quantity, eliminate the measurement explanations one at a time with numbers, name the survivor as a hypothesis, keep the pessimistic reading binding, and run the direct test (replay one action sequence open-loop through both plants) before the quantity is used to pass a gate for hardware.
Symptom
For R3.0, Isaac read hip_pitch torque demand at 75-80% of the limit (PASS); MuJoCo read the same policy at 148-152% (1.5x over the limit).
Context
Candidates were eliminated one by one: PD gains (identical, RS06 kp 30 / kd 1.5), the settle window (both skip the first 0.5 s), the statistic (worse-of-two-legs vs per-joint - explains ~15%), self-collision (the no-contact subset still read 152%), sampling (500 Hz peak vs 50 Hz sample - ~9%). About 1.8x remained. The prime suspect - Isaac's implicit actuator (PD solved inside the PhysX integration, chosen to mimic kHz firmware PD) against MuJoCo's 500 Hz explicit PD - was named but untested.
Change
The Isaac PASS was recorded as valid for the Isaac plant only; the prescribed decider was an open-loop replay of one action sequence through both plants, compared step by step, needing no training. It was upgraded to a precondition of R3.1.
Outcome
R3.1 ran in parallel with, not after, the replay, and the replay never appears as done. By §40 it was "the oldest open account" on the line, to be fed by real-robot logs - which the spec also never records. Meanwhile the 50 Hz sample under-read the peak by 34% as smoothing narrowed the spikes, so Isaac-side readings grew more optimistic exactly when the comparison mattered more.
Mechanism
Each measurement artifact explained a slice of the gap; what remained is a plant difference, and an untested plant hypothesis cannot license discarding the pessimistic simulator on a safety-relevant quantity.
Conflicts
The spec prescribes the open-loop replay (§23), upgrades it to an R3.1 precondition, then records that R3.1 ran in parallel without it (§24), and lists it as the oldest open account before hardware (§40); nothing through §50 (2026-08-14) records a result. Every later "torque gate PASS" on this line is an Isaac-plant reading plus a MuJoCo check, never a reconciled one.
Applies when
- Isaac and MuJoCo (or sim and hardware) report different torques, contacts or slips
- implicit vs explicit actuator models are in play
- a gate passes on one simulator only
“**同一个策略,一侧判 PASS 一侧判超限 1.5 倍。** … 口径项全部扣掉后仍剩 **~1.8×** 没有解释。 … 但**这条没有验证,不能拿它当结论去抵消 MuJoCo 的读数**。 … (做法:拿同一条 动作序列在两边开环回放,逐拍比 τ —— 不需要重训)”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §23 MuJoCo 复核门:成功率更稳,但 τ 与 Isaac 差 ~1.8× 没收敛 Cross-simulator gate (Isaac Lab to MuJoCo) comes before any hardware attempt
sim2sim-gate-before-sim2realGate every policy through a second simulator with an independently built plant before hardware; treat sim2sim failure as a contract or overfitting bug, and sim2real failure after a sim2sim pass as a plant/actuator gap.
Symptom
A policy that only ever ran in its training simulator carries untested dependencies on that simulator's solver, contact model, and defaults; the first place those dependencies surface should not be the real robot.
Context
Standing order of operations for the whole Lucen program, recorded as the opening line of the experience log: train in Isaac Lab, gate in MuJoCo, only then go to hardware. The MuJoCo side is the same plant used for evaluation batteries, so a sim2sim pass also validates the exported policy + contract (obs ordering, scales, defaults) outside the training stack.
Change
Pipeline rule adopted - every checkpoint must pass the MuJoCo evaluation battery (sim2sim) before it is considered for real deployment (sim2real).
Outcome
Used generation after generation as the cheap filter; hardware sessions only ever received policies that had already survived a second simulator.
Mechanism
Two simulators disagree exactly where a policy is overfit to simulator-specific artifacts (contact softness, integrator, default parameters, obs conventions); a cross-sim transfer catches contract bugs and solver overfitting at zero hardware risk, so real-robot failures that remain are attributable to genuine plant/actuator gaps.
Applies when
- planning the path from training to first hardware trial
- exported policy behaves differently outside the training framework
- triaging whether a real-robot failure is contract vs plant
“先sim2sim - 从isaaclab 到mujoco / 再sim2real”
Experience.md § opening lines (1-2) The trainer read a stale USD after the URDF mass update - regenerate derived assets and gate on an automated equality instrument
derived-asset-staleness-checkFor every derived plant artifact (USD from URDF, generated value files), pair the generation step with an automated source-vs-derived equality instrument, prove the instrument can fail, gate training on its PASS, and re-run physical audits after every regeneration.
Symptom
Measured link masses had been committed to URDF/MJCF (total 9.58 -> 9.792 kg, weighed values), but Isaac reads the derived USD asset - which still carried the old masses: a silent 2.2% mass fork between the training plant and the evaluation plant.
Context
The v12 checklist made USD regeneration a hard precondition ("硬性 前置") and, crucially, backed it with an instrument: check_usd_mass.py compares USD vs URDF per-link mass AND inertia trace, validated by showing it FAILs the stale asset naming 9 offending links, then PASSes after re-conversion (13/13 links consistent, total 9.7920). The self-collision filter audit was re-run after the regeneration (three poses, 0.00 N) because a regenerated asset invalidates physical audits done on the old one. This milestone was also where the three plants first aligned: "三边 plant 首次对齐(armature+摩擦+ 实称质量)就在这一代".
Change
convert_urdf re-run on the training machine, regenerated asset committed, check_usd_mass.py PASS required as an acceptance gate for the generation; dependent audits repeated post-regeneration.
Outcome
The 2.2% plant fork was closed before it could distort a generation's acceptance numbers; the staleness class of bug now has a permanent detector instead of a memory.
Mechanism
Source-of-truth edits do not propagate to derived binary assets by themselves; any consumer reading the derivative silently trains or evaluates on the old plant. An automated equality check between source and derivative - proven able to fail - turns an invisible staleness into a red gate, and regeneration invalidates every audit performed on the old artifact.
Applies when
- editing masses/inertia/geometry in URDF or MJCF sources
- a trainer or evaluator consumes converted/derived assets
- plant numbers differ between simulators for no visible reason
“25ba997 把连杆质量更新为实称值(总重 9.58→9.792 kg,URDF/MJCF 已改),但 Isaac 读的是 train/assets/laika_v2.usd —— 仍是旧质量。… 否则 Isaac(9.58)与 MuJoCo(9.79)质量分叉 2.2%,v12 验收数字失真。验收门:python tools/check_usd_mass.py 必须 PASS … 对旧资产实测 FAIL/9 连杆点名,仪器已验证”
train/WALK_V12_SPEC.md § 7. 核查单 (⚠️ 先重转 USD) 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) Borrow reward values from other robots as ratios (to tracking weight, to leg length) - never as absolute numbers
transfer-ratios-not-absolutesWhen importing any numeric from another robot's config or paper, identify its natural normalizer (tracking weight, leg length, sqrt(g*L), body mass) and transfer the dimensionless ratio; sanity-check against a same-scale robot when one exists.
Symptom
Published configs offered tempting absolute values (swing height target 0.06-0.08 m, weight -20) that would have been wrong for a robot with half the leg length and a different tracking weight.
Context
The cross-check normalized before transferring: G1's feet_swing_height weight -20 against tracking +1.0 is a 20x ratio, so with local tracking at 1.5 the equivalent is -30, not -20. G1's 0.06 m target on a ~0.70 m leg scales to ~28 mm on the local 0.325 m leg (Humanoid-Gym converts to ~23 mm), confirming the locally chosen 0.03 m and explicitly rejecting copying 0.05-0.08 absolutes. A same-class robot (Menlo's 16 kg) was used to sanity-check feet_air_time (+0.5 vs the local 2.0, flagged over-high). A nondimensional check of the same kind later validated the sidewalk speed target (v/sqrt(gL) = 0.081 vs hardware-verified 0.101 - 0.104 - inside the envelope, conservative).
Change
All borrowed values converted through ratios (weight/tracking-weight, height/leg-length, dimensionless speed) before entering the config.
Outcome
The scaled values worked (0.03 m target matched both the scaling law and measured 22-23 mm baseline); no cross-robot absolute was ever copied raw.
Mechanism
Reward economies are scale-relative (only ratios to the tracking term matter to the optimum) and kinematic quantities are morphology-relative (clearance scales with leg length, speed with sqrt(g*L)); absolutes encode the source robot's scale, ratios encode the design intent.
Applies when
- copying reward weights/targets from open-source configs or papers
- setting clearance heights, speed targets, or impact thresholds
- comparing your weights to published tables
“G1 的 feet_swing_height 是 tracking 的 20 倍(−20 vs +1.0)。我们 tracking 是 1.5,按同比例应为 −30 … G1 目标 0.06 m / 腿长 ~0.70 m,换算到我们 0.325 m 腿长约 28 mm;Humanoid-Gym 换算约 23 mm。故 target 取 0.03 m 是对的 … 不必抄 0.05~0.08 的绝对值。”
train/WALK_DIAGNOSIS.md § 修正 ②(权重放大) / 修正 ③(目标高度按腿长缩放) The hip_roll (l+r) asymmetry scalar predicted real-robot lateral drift - promote validated sim scalars into the gate
hip-roll-sum-predicts-lateral-driftHunt for cheap sim scalars that predict real-robot behaviors, validate them on direction AND ordering across multiple policies, then promote them into the acceptance battery; treat later violations as debt to justify in writing, not noise to ignore.
Symptom
A persistent hip_roll left/right asymmetry row in the sim2sim symmetry table had been dismissed as "calibration or mechanical asymmetry" noise; meanwhile real deployments drifted sideways by policy-dependent amounts.
Context
Forward-kinematics analysis reframed the scalar: both hip_rolls move the feet in +y for positive angle, so a same-signed (l+r) sum IS a lateral translation mode - the scalar is a direct lateral-drift bias estimate. Checked against real deployments: s1e (l+r = -0.0178, smallest magnitude) was the steadiest with least drift; 700 (+0.0253) drifted mildly left; A800 (+0.0267) drifted clearly left with the largest tilt 12.9 deg. Direction correct 3/3, ordering correct 3/3 (the log's heading calls it "四枚四中", four-for-four).
Change
The scalar was promoted into the acceptance battery as a posture-class criterion alongside tilt-max median: "hip_roll 左右不对称 |l+r| 不得比父代大" - doubling as a heat proxy (error ~ torque ~ heating).
Outcome
Used at every later gate; when the C4 product exceeded it by +0.005 rad (~+0.3 deg vs parent), the criterion was not silently waived - it was booked as explicit debt with a mechanism argument (the increment is task-required, far smaller than the sidewalk amplitude +/-2.2 deg) plus a related account (stand saturation 32.4% -> 37.2%).
Mechanism
A policy's static joint-angle bias in a translation-producing mode integrates into real-world drift; sim can measure that bias precisely and cheaply. A sim scalar earns gate status exactly when its predictions are validated against hardware in both direction and ordering - and a validated gate may only be exceeded with a written mechanism-level justification, never silently.
Conflicts
The log's heading says "四枚四中" (4/4) but the evidence table lists three policies and the text says "方向 3/3、排序 3/3"; the fourth instance is not shown in this file.
Applies when
- a real robot drifts or leans in a policy-dependent way
- deciding which sim measurements deserve gate status
- a validated gate criterion is marginally exceeded by a new product
“s1e | −0.0178（绝对值最小）| 微右、最不飘 | 三者中最稳、飘最小 ✓ … A800 | +0.0267 | 左、最飘 | 明显左飘、倾角最大 12.9° ✓ 方向 3/3、排序 3/3。 → 正式纳入验收表（与「倾角 max 中位」并列为姿态类判据）。”
train/C_LADDER_RUN.md § 3e. 顺带：hip_roll 左右不对称 (l+r) 就是横移偏置 —— 四枚四中 The shipped checkpoint was chosen by scanning checkpoints on the full gate - neighbours 100 iterations apart failed 1 and 38 cells, late checkpoints degraded - never by taking the last one, and training stopped on signals, not on a schedule
checkpoint-choice-is-a-full-gate-scanChoose a release checkpoint by running the full acceptance battery over a band of checkpoints (including the transfer axis), stop training on measured signals rather than a fixed iteration count, and expect adjacent checkpoints to differ sharply.
Symptom
Gate results moved sharply and non-monotonically between checkpoints of the same run, and the last checkpoint was often not the best.
Context
One-leg V0r1: the 2,000 neighbourhood was best; from 2,500 on the nominal gates degraded (late overtraining); 2,000 itself had one real micro-hop (17.7 mm over 5 frames); 2,300 was all green and shipped. V0r2: failed cells per checkpoint 2,000:19, 2,100:38, 2,200:1, 2,300:3, 2,400:27, 2,500:12, 3,000:18 - 2,200 shipped. The recovery line learned the same from the other side: stopping v2_6 early at a scheduled point left a policy whose re-fall rate had spiked to 9-22% before consolidation healed it ("stop on signals, not on the schedule"), and a continuation's transfer decayed checkpoint by checkpoint while Isaac stayed perfect.
Change
The acceptance rule "scan checkpoints, do not look only at the last one" is written into the one-leg gates (called the S1 discipline); release candidates are chosen from the scan.
Outcome
Both one-leg releases were mid-run checkpoints (2,300 and 2,200) chosen by the full 40-cell battery.
Mechanism
PPO keeps changing the policy after the gates saturate; with no gradient toward the gate's conditions, later checkpoints wander, so gate quality is a noisy function of iteration.
Applies when
- picking which checkpoint of a run to export and stamp
- a run is stopped at a fixed iteration budget
- final-checkpoint results are worse than mid-run smoke tests
“Isaac 侧 S1 纪律: 验收扫 checkpoint，不是只看最后一个。 … 扫描判决: 2000 邻域最优——2500+ 标称面退化（⑤③② 散挂, 晚期过训）, 2000 有一例真微跳（L s100 μ1.2, 17.7mm/5帧）, 2300 全绿。”
git:Lucen V2@origin/oneleg-line:train/ONELEG_V0_SPEC.md § §6 验收门 / §8 核查单 5 与 7 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 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 The best checkpoint to SHIP is not the best checkpoint to CONTINUE FROM - maturity is capital against adaptation shock
root-maturity-vs-product-qualityDecide shipping points and fork roots separately: gates rank products, but a root candidate must prove itself by surviving a continuation under the next rung's shift (dual-arm if in doubt) - and prefer the more-trained point as root when product metrics conflict with maturity.
Symptom
A band re-audit found s1e-300 beat the incumbent root s1e-500 on nearly every quality gate (stepping 19/20 vs 13/20 with historically-best 26.9 mm swing, speed gate 14/20 vs 2/20, heading 26 vs 54 deg/20 s) - suggesting the root had been mis-picked and the younger point should take over.
Context
The dual-arm control settled it the other way: continuing the S2 PD rung from s1e-500 adapted smoothly (3/3 smoke throughout), while the b300 control arm (same config, from s1e-300) fell into a survival valley under the PD shock (+100 iters: 1/3 -> 0/3), never climbed out within budget, and its 800-iter product scored 13/20 survival - eliminated. Verdict: "幼年点自身指标再好也扛不住新 DR 适应冲击, 成熟度是本钱,s1e-500 根被数据背书" - a young point's own metrics, however good, do not survive new-DR adaptation shock; maturity is capital. The audit still yielded value: the band scan (200-1000, per-100) mapped the lineage's arc (200 dragging -> 300 peak -> 400+ decay -> 900+ drift blowout), and 300 remains the better PRODUCT answer for shipping-as-is questions.
Change
Selection doctrine split into two questions with different answers: best-product point (quality gates at the point itself) vs best-root point (survives adaptation shocks; more training age = more capital), each decided by its own evidence - and root claims settled by a dual-arm continuation test, not by point metrics.
Outcome
s1e-500 kept the root role with data behind it; the S2e ladder built on it passed rung after rung, while the b300 line was closed at the cost of one control arm.
Mechanism
Early checkpoints sit near sharp optima with less accumulated robustness structure; their headline metrics reflect the narrow training distribution, not resilience to distribution shifts. A continuation rung is itself a distribution shift, so the root property being selected for is shock tolerance - observable only by actually continuing, never by static gates.
Applies when
- a younger checkpoint outscores the current root on quality gates
- choosing the base for a robustification or command ladder
- a continuation run stalls in an early survival valley
“b300 对照臂 … PD 冲击下存活谷(+100 起 1/3→0/3),预算尽未爬出,800 档 20-seed 存活 13/20 出局——幼年点自身指标再好也扛不住新 DR 适应冲击,成熟度是本钱,s1e-500 根被数据背书”
train/README.md § omni_s2e_pd (b300 对照臂) / s1e 选点重审 Before adding a command mode, compute what ignoring it costs - the lazy optimum must lose
reward-cost-of-ignoring-auditPrice the do-nothing policy for every new command or objective: compute reward-per-step for "comply" vs "ignore" from the actual table, and only train once ignoring is decisively unprofitable.
Symptom
A new command mode can be silently unlearnable if the reward table makes "ignore the command entirely" nearly free compared to the tracking reward available elsewhere.
Context
For each C rung the team computed the per-step cost of completely ignoring the new command versus ignoring forward: ignoring vx=0.25 costs 1.264/step; ignoring wz=0.20 costs 0.984/step (78% of forward - gradient sufficient, so C2 was certified "zero reward surgery"); but ignoring vy=0.10 cost only 0.020/step - 50-60x weaker, because vy entered the table only as an L2 tax, not a tracking term.
Change
Rule instituted: a rung may claim "no reward change needed" only after this arithmetic shows the ignore-cost is the same order as forward's. For C4 the audit failed, so track_lin_vel_y_exp (+2.0, std 0.15, same form as vx) was added - raising the ignore-cost at cmd_vy=0.10 from 0.020 to 0.718/step (36x).
Outcome
C1/C2/C3 proceeded with zero reward edits, keeping single-variable attribution clean; C4's needed surgery was identified before training instead of after a failed run.
Mechanism
PPO converges to whatever costs least; if the reward margin for obeying a new command is a rounding error against existing terms, the "ignore" policy is the optimum and no amount of training fixes it. The audit prices the lazy optimum explicitly before spending compute.
Applies when
- adding a command axis or task mode to an existing reward table
- a new skill trains flat while other skills stay healthy
- certifying a rung as "no reward change"
“cmd wz 0.20 → 0.984（coarse .473 + fine .491 + L2 .020）… 对照：忽略 vx=0.25 = 1.264（本级 78%，同量级）；忽略 vy=0.10 = 0.020（弱 50 倍——那才是 C4 必须加 track_lin_vel_y_exp 的原因）。本级不动奖励表。”
train/C_LADDER_RUN.md § 4. 原地转级（C2）② 奖励梯度已验够 A curriculum ramp keyed to the process step counter re-fires on every resume - and shipped policies never saw the penalty
curriculum-counter-lineage-stepsKey every curriculum/ramp schedule to lineage-cumulative progress, not per-process counters; and audit where your shipped checkpoints sit relative to every ramp - a penalty that no product ever experienced is not part of your training.
Symptom
vx+0.30 died at a fixed relative time in every resumed run: resume at 500 -> slide at 1100, zero at 1300; resume at 700 -> slide at 1300, zero at 1500 - absolute depths offset by exactly the resume offset, relative timetable identical.
Context
ramp_reward_weight (the saturation penalty ramp) read env.common_step_counter, which restarts at 0 for every run including --resume. So start_step=600 meant "600 iters after THIS resume", not "lineage iteration 600". The A/B arm pair was the clean proof: their env.yaml differed only in log_dir, only resume point distinguished them, and the omni CurriculumManager had exactly one active term - nothing else could produce that timetable. Second consequence: every shipped checkpoint (s1e-500 at +500, C2-700 at +200, A800 at +100) was selected before its run's +600, so the saturation penalty weight was 0.000 for every product ever shipped - explaining saturation 33% and raw |action| 1.9 against clip 1.0 (hip_roll in bang-bang), i.e. half the heat budget.
Change
Two independent recommendations recorded: (1) make the ramp count lineage steps (add the checkpoint's iteration offset on resume) or pin terminal weights in downstream rungs instead of ramping; (2) give the saturation penalty its own rung - never mixed into a skill-learning rung (that would be two variables again).
Outcome
Explained the recurring +600 death of the highest-amplitude command and the persistent actuator saturation of all shipped products with one root cause; honest caveat booked (at +800 the weight is only -0.086, small, but vx+0.30 is the command demanding the largest action amplitude, so it is squeezed first).
Mechanism
Resumable training splits "the lineage" from "the process"; any schedule keyed to process-local counters silently re-applies its transient to every descendant run, and any product-selection habit that picks checkpoints early systematically samples the pre-ramp regime - the curriculum exists in the config but never in any shipped policy.
Applies when
- resumed/forked training with any scheduled reward or DR ramp
- a metric dies at a fixed offset after each resume
- shipped policies show behavior a late-schedule penalty should prevent
“ramp_reward_weight 读的是 env.common_step_counter，它每个 run 从 0 开始，--resume 也不例外。… 原始 run / 臂B | 500 | 1100 = +600 | 1300 = +800；臂A | 700 | 1300 = +600 | 1500 = +800 … 所有出品其实从没见过饱和罚。… 这解释了 sat_max_pct 33%、raw |a| 最大 1.9（clip 是 1.0）—— hip_roll 一直在 bang-bang，而罚它的那一项权重恒 0。热账的一半在这里。”
train/C_LADDER_RUN.md § 3g. 系统性问题：saturation_ramp 每次 resume 归零 Run acceptance under measured contact parameters - honest condim/torsional-friction flipped a false PASS into a real-matching FAIL
eval-plant-honesty-contact-paramsPin the evaluation plant's contact model to measured values (contact dimension, torsional/rolling friction, mu) before trusting any gate that involves slip, impact, or drift - a gate can only fail a policy for physics its simulator contains.
Symptom
walk_v5 passed the old acceptance battery yet failed on the real robot (footfall force, drift, kicking) - the evaluation plant was flattering the policy.
Context
The battery was re-run under "honest contact parameters" - condim 4 (adding torsional contact) with measured torsional friction 0.035 - and v5 then FAILED exactly the rows corresponding to its real problems: heading 185 deg (limit 30), support-foot yaw slip 284 deg (limit 80), landing force 1.72x (limit 1.5x), suspended tilt 45.9 deg (limit 10). The slip physics depends on torsional friction, which the default contact model (condim 3) does not even simulate - a slip problem is invisible to an evaluator that cannot represent yaw friction at the foot. Term-sizing measurements for the new rewards were likewise taken under the same honest parameters (cmd 0.45, skipping the 5 s start transient).
Change
Acceptance harness pinned to condim 4 / torsion 0.035 (measured); verdicts issued under defaults declared non-citable for these rows.
Outcome
Sim acceptance verdicts began agreeing with hardware ("现在失败, 与真机一致"); the v6 fixes could be developed and validated against an evaluator that could actually see the disease.
Mechanism
An evaluator is a plant model too: contact dimensionality and friction values decide which failure modes exist in the simulation at all. Evaluating under default contact parameters tests the policy in a world where its real failure is physically impossible, producing structurally false PASSes.
Applies when
- sim acceptance passes policies that fail on hardware
- slip/drift/impact gates run under default simulator contact settings
- setting up a cross-simulator evaluation harness
“accept_v2.py 已加三条判据, walk_v5 在诚实的接触参数下(--condim 4 --torsion 0.035)现在失败, 与真机一致:直行 15s 航向累计 <30° | 185° ✗ … 落脚力峰值 <1.5× 体重 | 1.72× ✗”
train/WALK_V6_MINIMAL.md § 5. 验收 A constant-value plant rung passed every binary gate with record scores - and shipped 60% thinner posture margins that hardware exposed
constant-value-dr-overfits-marginRandomize deployment-critical axes over a narrow band spanning the measured real support - never a single value, never a fictitious tail - and report graded margin quantities (tilt margin) next to binary gates, because saturated gates rank thin-margin and thick-margin policies identically.
Symptom
s2_lag1 (trained at constant 1-frame latency) posted the strongest sim gate sheet in history (20/20 everywhere) yet was unstable on hardware, while s1e (trained across the full 0-3 frame band) was the every-run-stable SOTA at the same power.
Context
The sim autopsy (new --delay-jitter harness modeling the BusWorker's time-varying phase drift): 18 runs across constant and time-varying delays ALL survived - time variation alone does not kill - but the tilt-margin ordering reproduced hardware exactly: s1e 7.7-9.1 deg (thickest) < s2_lag1 10.7-15.0 < s1c 16.2-18.2. Attribution: constant-value training permits precise specialization to that one value; s1e's band diversity forced cross-value robustness - "恒定 1 帧训练 vs s1e 的 0~3 帧全带——分布多样性逼出跨值鲁棒,恒定值允许精确 特化" - so the constant-rung policy's margins were ~60% thinner, fine in sim's clean world, pushed over the line by real-world disturbances. Tool lesson booked: "存活门二值饱和后掩盖裕度差" - binary survival gates saturate and hide margin differences; graded margin columns (tilt-max) belong in the report. The synthesis with the opposite failure (wide tails cause drag-glide): the proposed resolution was a NARROW uniform band (0.02, 0.04) covering exactly the real 1-2 ticks - diversity inside the measured support, no tail, no single point. s1e's root selection later leaned on the same property: its full-band latency training "预装" the delay rungs and delivered "全工况稳定裕度" that survived power derating.
Change
DR-on-an-axis design refined to a three-way distinction: no wide fictitious tails (drag), no single constant values (thin margins), but a narrow band spanning the measured real support; acceptance reports gained graded margin columns alongside binary gates.
Outcome
The tilt-margin column entered the standard report; the s1e root (band-trained) carried the C ladder while the constant-value branch was archived with its three contributions credited.
Mechanism
Robustness margins are shaped by the diversity of the training distribution, not just its support: a point-mass distribution lets the optimizer trade margin for on-point performance, while a band forces solutions that keep margin across the band - and binary survival metrics cannot see the difference until the margin is spent on hardware.
Conflicts
The narrow-band (0.02,0.04) resolution was a pending recommendation ("裁决建议(待用户)") at the time of writing; the lineage instead moved root to s1e whose full-band training predated the staged ladder - the deterministic-staging card and this card record the two failure modes the final design must avoid simultaneously.
Applies when
- a rung trained at a fixed plant value aces sim but wobbles on hardware
- binary acceptance gates are all saturated across candidates
- choosing between constant, banded, and wide DR on one axis
“18 跑全活,时变性单独不足以击杀;但 tilt_max 裕度排序完整复现真机:s1e 7.7~9.1°(最厚)< s2_lag1 10.7~15.0 … 恒定 1 帧训练 vs s1e 的 0~3 帧全带——分布多样性逼出跨值鲁棒,恒定值允许精确特化 … 存活门二值饱和后掩盖裕度差(s2_lag1 sim 门 20/20 史上最强却真机不稳)”
train/README.md § s2_lag1 真机不稳 × s1e 稳的 sim 对拍(2026-08-07,时变延迟实验) Every acceptance run records video of the very rollout that produced the numbers - the seated basin, edge-standing feet, tangled legs and the narrow stance were all seen on video before, or instead of, a metric catching them
video-as-acceptance-recordMake video a default output of every acceptance run, rendered from the same rollout the metrics come from (fixed views including the feet), and watch it - posture failures are visible before any gate row exists for them; never let video replace or override the numeric gate.
Symptom
Posture failures in the recovery line kept arriving as things the numbers had no row for: a kneeling W-sit, feet standing on their outer edges, crossed legs, a stance too narrow to hold on hardware.
Context
From 2026-08-09 (user decision) accept_recovery renders offscreen by default, following one env for the whole episode and archiving the clip. The MuJoCo gate's --video renders three views (side, front, feet) of the same rollout the metrics come from, with all plant modelling (delay, push); the older replay-based renderer produced an independent trajectory without delay and was not used for acceptance. Video never gates: if rendering breaks, --no-video keeps the numeric gate running.
Change
Video as a default acceptance artifact, named per policy, category and view, reviewed by the user.
Outcome
The R0.1 prone clip showed the same kneel-sit as R0; the R3.1 failure clip showed the crossed legs; the v2_5 feet view showed edge standing and led to V2.6; the v2_6 videos led the user to order a real-robot A/B between v2_5b and v2_6. Once, the recorder did not start (P1c final acceptance) and the visual material had to be produced separately.
Mechanism
Metrics exist only for failure modes someone anticipated; video shows the unanticipated ones, and rendering the metric rollout itself guarantees the picture and the numbers describe the same episode.
Applies when
- setting up an acceptance pipeline for posture-sensitive skills
- numbers pass but a human reviewer is uneasy
- sim videos are rendered by a separate replay tool
“**验收存视频(用户定 2026-08-09)**:`accept_recovery.py` 默认开 Isaac 离屏 渲染,跟拍一个 env 的整局并归档 … 跪坐这类盆地在数字表出现前肉眼先看见,视频是验收的 定性存档,数字门不受它影响”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §5 R0 验收门(预注册)验收存视频(用户定 2026-08-09) A stronger action_rate penalty cut the median torque demand under the gate and left the p99 at 4x the limit - only a hinge on the pre-clip (computed) torque, weighted by comparison with a peer term, collapsed the tail
tail-torque-needs-hinge-on-computed-demandJudge actuator demand against the deployed limit, read the pre-clip demand (applied torque is censored and gives no gradient on the excess), use an L2 rate penalty for the median and a thresholded hinge on computed demand for the tail, and set a new term's weight from its measured steady magnitude next to a peer term rather than from a back-of-envelope estimate.
Symptom
After R0.5 hip_pitch delivered torque sat at its 12 N*m limit in a typical get-up (demand 119-125% of the limit, p99 4.2x) - zero control margin at exactly the moment modelling error matters.
Context
The 12/17/11 N*m limits are deployment limits written into robot.yaml by set_torque (RS06 at 33% of rated), and simulation uses the same effort_limit - so the gate is judged against them, not the 36 N*m rating (an early reading against the rating was retracted). Applied torque is clipped at the limit - censored data - so demand must be read from computed_torque. With the full-range action contract (hip_pitch scale 1.309, kp 30) a single-step action change of 0.306 already saturates hip_pitch, and action_rate penalizes exactly that change.
Change
R3.0: action_rate_l2 -0.01 -> -0.03 (child-run). R3.1: new torque_headroom = sum relu(|tau_computed|/limit - 0.9)^2, normalized so three motor types share a scale. Its weight was first estimated at -0.1, measured in a 12-iteration run at an effective -0.019 (12x smaller - the estimate had mixed a per-episode-peak p99 with a per-step p99, and at 1% of upright it would have been numerically absent), and set to -0.5 so its steady value (-0.095) matched action_rate's (-0.097).
Outcome
R3.0: sum |da|^2 -64%, success 99.6 -> 100%, delivered median = demand median (the clamp no longer fired in a typical episode), gate PASS at worst 79.9% - but p99 unchanged (hip_pitch 419-432% -> 427-436%). R3.1: p99 hip_pitch -> 148-189% (-57 to -65%), knee 422-439% -> 233-234%, saturation duty -60%, success 100%; the worst joint became hip_roll at 67.7%. Its cost appears in torque-penalty-bought-by-leg-bracing.
Mechanism
A squared-rate penalty presses the whole-episode sum and moves the typical step, not rare spikes; the spikes came from the kp term (large targets while a limb is blocked by the ground - velocity alone could not reach them under vel_limit), and a penalty on applied torque cannot see demand above the clip because every excess sample reads as exactly the limit.
Applies when
- torque demand saturates actuator limits in high-effort skills
- a smoothness penalty improves medians but not peaks
- a new reward term's weight is set by estimate alone
“**必须用 `computed_torque` 而不是 `applied_torque`**:后者被 `effort_limit` 削平, 是删失数据,超限样本全被压成"恰好等于限",对超限部分梯度恒为 0。 … 改按同侪定标取 **−0.5**(稳态 ≈ −0.095,与 `action_rate_l2` 的 −0.097 等量)。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §24 R3.1(torque_headroom 力矩需求越限罚) PPO's Gaussian noise cannot compose phase-locked oscillations - deliver them as feed-forward and let the policy learn the residual
feedforward-for-phase-locked-skillsIf a skill needs a temporally coherent (phase-locked) action component, do not expect step-wise exploration to find it: inject a verified feed-forward and train the policy as a residual stabilizer, keeping the feed-forward inside the deployment contract.
Symptom
Four different reward arrangements (no reference / wrong-sign reference / correct-sign reference / cage released) all failed to elicit sidewalk, while open-loop probes proved the behavior existed and was safe on the same platform with the same policy as base.
Context
Producing lateral velocity requires a phase-locked hip_roll oscillation synchronized to the gait clock. PPO's exploration is per-step, zero-mean, uncorrelated Gaussian noise - it can never compose a sustained phase-locked component, so the behavior is unreachable by exploration regardless of how it is rewarded. The fix changed the delivery channel: target = default + scale*action + lat_ff(cmd_vy, phi). The policy's action becomes a residual on top of the feed-forward, retaining full balance authority (it can even cancel the feed-forward); the feed-forward supplies exactly the component exploration cannot. This mirrors why the sagittal joint_pos_ref worked (it also delivered phase structure), just via a different channel.
Change
Contract-level change, done cleanly: new profile omni_ff (= omni + lat_ff_gain -0.5), existing omni profile bit-identical; feed-forward applied after the action delay stage; missing cmd/phase raises instead of silently dropping; deployment must use the same phi as build_obs (recomputing gives a one-tick phase misalignment).
Outcome
From C2-700, +100 iterations sufficed: product omni_c4_ff800 scored vy +120%/+125% (from +4%/-1%), 260/260 cells at 20/20 survival, zero old-skill regression, left/right gap 5 pp - the entire C4 saga resolved by changing the delivery mechanism, not the reward.
Mechanism
Exploration noise spans only the subspace its correlation structure can express; skills requiring coherent oscillation lie outside the span of i.i.d. per-step noise. Feed-forward moves the required structure into the action pipeline where it needs zero probability mass to appear, reducing the learning problem to stabilizing around a demonstrated behavior - which PPO does well.
Applies when
- a periodic/oscillatory skill trains flat under every reward variant
- open-loop injection of the behavior already works
- considering GRU/curriculum/exploration tricks for a rhythmic skill
“病因不在奖励，在探索形式：产生侧向速度需要相位锁定的 hip_roll 振荡，PPO 的逐步高斯噪声零均值无相关，合不出相位锁定分量。… target = default + scale·a + lat_ff(cmd_vy, φ)。策略动作因此是前馈之上的残差，保留全部平衡权限”
train/C_LADDER_RUN.md § 3j. C4-redo4：唯一变量 = 侧步参考改为前馈注入（契约级）