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
59 cards matching “reversible-single-variable-field-experiments”.
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） 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 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 Under continuous 3-axis uniform sampling, pure straight-line walking is a zero-measure event the policy never trained
zero-measure-commands-need-mode-samplingEnumerate the exact command points users will actually issue (straight, stop, in-place turn) and give each explicit probability mass via mode sampling with off-axes pinned to zero - never assume a continuous sampler covers its measure-zero subsets.
Symptom
"The robot drifts even in sim when told to walk straight" persisted across reward tunings - because with commands drawn as vx in [0.15,0.5] x vy ~ U(+/-0.2) x wz ~ U(+/-0.6), the event vy=0 AND wz=0 has probability zero: pure straight-line walking was never sampled even once.
Context
Restart evidence item #3: "纯直行是零测度点 … 'sim 里直行就漂'是分布的 必然,不是 reward 没调好" - the drift metric was legitimately drowned by commanded turning (v11's own comment self-documented this). The structural fix is discrete mode sampling: a custom ModeVelocityCommand that first draws a mode by share (stand/forward/back/turn/side/mixed), then draws values only on that mode's axes with all others pinned to exact zero - which is also what preserves single-variable discipline in the C ladder (native 3-axis uniform "采不出'离散模式桶' … 把 C1~C4 的单变量纪律直接毁掉"). The mixed mode later got an ellipsoid constraint rather than a cube for the same reason in reverse - corner combinations of a cube are unrepresentative extremes.
Change
Command generation moved from independent per-axis uniforms to mode-bucket sampling with pinned-zero off-axes (plus 20% rel_standing); acceptance likewise evaluates per mode.
Outcome
Straight-line behavior became a trained, testable mode instead of a measure-zero hope; the C ladder could add one mode per rung with provable isolation.
Mechanism
A policy optimizes expected reward under the command distribution; events of probability zero contribute nothing to the objective, so exact-zero-command behaviors (straight walk, stand, in-place turn) are only learned if the sampler gives them mass. Product-of-uniforms distributions concentrate mass on mixtures and give none to the pure behaviors users actually command.
Applies when
- a "simple" command (straight, stop) underperforms mixtures in sim
- designing command distributions for velocity-tracking tasks
- a ladder needs per-mode isolation for attribution
“纯直行是零测度点:最终 command 为 vx∈[0.15,0.5] × vy∈U(±0.2) × wz∈U(±0.6) 连续均匀,vy=0∧wz=0 从未被专门采样 —— "sim 里直行就漂"是分布的必然,不是 reward 没调好 … Isaac 原生 UniformVelocityCommand 是三轴各自 uniform,采不出"离散模式桶"”
train/OMNI_V0_SPEC.md § 0. 为什么从零 (3) / 三件前置 (1) A proposal in the runbook - torque and action limits as versioned safety tiers (classroom / research / expert) written to motor RAM and read back, separate from the reward's effort penalty - recorded as a proposal, its implementation unrecorded
safety-limits-are-a-layer-not-a-rewardKeep hardware limits as an explicit, versioned safety layer (tiers written and read back at start, the persisted default the safest one) and the effort penalty as a behaviour layer; when a skill needs more torque, change tier deliberately rather than trading one layer against the other.
Symptom
Running and jumping need more torque than the deployed limits allow, and the temptation is to trade the training-side effort penalty against the hardware limit, or to hand a new user a robot "tuned however the last person left it".
Context
A message pasted into the operator runbook (undated, citing Berkeley's practice of storing the full motor configuration as JSON with write and read-back scripts) proposes: configuration is a versioned artifact, not a verbal agreement; three safety tiers in robot.yaml beside the gain and policy profiles - classroom (RS06 limited to 10 N*m, lateral joints clamped: "however bad the policy, it only moves awkwardly"), research (14 N*m, clamps at twice the measured need, the default) and expert (the 36 N*m rating, joint limits only, requiring an explicit flag); deploy writes the tier to motor RAM at start and reads it back, while the stored copy stays classroom so a power cut returns to the safest state. It frames limits as the safety layer and the effort penalty as the behaviour layer - more torque for running means switching tier, not weakening the penalty.
Change
None recorded: the message ends by asking which to do first, a rollback or the tiers.
Outcome
The sources do not record the tiers being implemented; the deployed limits stayed at 12/17/11 N*m through the recovery and one-leg lines (the one-leg spec treats raising the RS06 limit as a separate, unapproved hardware decision). Related and recorded elsewhere: torque limits were written to RAM only in a scripted, self-reversing field experiment.
Mechanism
Hardware limits bound the damage any policy can do; reward terms shape what a policy prefers. Mixing them either weakens safety to buy behaviour or distorts behaviour to buy safety.
Applies when
- a new skill needs more torque than the deployed limits
- robots are handed to students or new users
- motor configuration lives in people's heads or in the firmware only
“配置是版本化的产物,不是口头约定。 … deploy_policy 启动时按档写进电机 RAM 并读回校验(落盘的那份永远保持 classroom,断电自动回到最安全状态)。 … 限幅是安全层,dof_torques_l2 是行为塑造层,它们在不同的层,不冲突。跑步要更大力矩就换档,而不是去动训练里的省力惩罚。”
RL系统/FOLLOW THIS copy 2.md § 面向 developer / 教育机构该怎么做 (pasted proposal, undated) Add a termination that makes the degenerate strategy fatal - no height cut-off meant crouch-shuffling could live forever
termination-closes-degenerate-basinFor each known degenerate strategy, check whether the termination set makes it fatal; if the robot can live indefinitely inside the degenerate posture, add a termination just past the intended operating envelope rather than escalating penalties.
Symptom
Crouched foot-dragging survived indefinitely because the termination set contained only bad_orientation (40 deg) and base contact - there was no height termination at all, so a deep squat was a viable long-term strategy.
Context
The hypothesis audit found the missing termination (hypothesis 7); the cross-check against published configs found the field practice: Booster terminates at 0.45 m (38% of body height) and the research warning is that the termination height must not be so low that crouching survives it. The proposed value: 0.32 m, just below the walk crouch base height 0.3739 - a deep squat terminates immediately, "断掉蹲着蹭的活路" (cutting off the crouch-shuffle's livelihood).
Change
Add height termination at 0.32 m as a second-priority item of the walk fix package, alongside restoring base_height_l2 to -10.
Outcome
Entered the v5/v6 fix package under which the crouch-shuffle optimum disappeared (34 mm clearance, 87% tracking by v6).
Mechanism
Termination conditions define which strategies exist at all: a reward penalty prices a behavior, but a termination deletes its future returns entirely. Degenerate basins that are merely penalized can remain optimal under enough tracking pressure; a termination placed between the degenerate posture and the intended one makes the basin unreachable as a steady state.
Applies when
- a degenerate but stable behavior persists across reward tunings
- auditing termination conditions for a locomotion task
- a policy exploits the gap between penalized and terminated states
“加终止高度:研究第 6 条"终止高度不能低到让蹲着也能活"。我们完全没有高度终止。建议 0.32 m(略低于 walk 蹲姿基座高 0.3739,深蹲即终止)。… 加终止高度 0.32 m(深蹲即终止,断掉蹲着蹭的活路)”
train/WALK_DIAGNOSIS.md § 修正 ④ / 最终改动清单 第二优先 Prone get-up sat at 0/159 until two gated hinge terms moved the seated feet - first sideways (561 -> 360 mm), then fore-aft (-168 -> +56 mm) - and success went to 158/159 with nothing else changed
prone-dead-end-is-foot-placementWhen a stuck state and a successful state differ geometrically, penalize the discriminating quantity with a gated hinge that is exactly zero in the state the policy actually reaches (measure it - not the nominal), then re-probe: flattening one axis can move the discriminant to another.
Symptom
Prone falls always righted and then sat with the feet splayed wide or tucked behind the hips, from where the policy never stood (0% for four generations).
Context
Four lines of evidence pointed at foot position: the configuration probe (ankles 215 mm apart stood 52.3%, 561 mm apart 0.0%); FK showing the action contract's nominal (a = 0) is itself a 465 mm straddle, so the action_rate and still terms were pulling toward the splits; biomechanics (feet tucked under the body cut peak hip-extension torque 148.8 -> 32.7 N*m, -78%); and HoST's foot-displacement term, which this reward table lacked. The earlier "not a reward hole" reading was corrected to "a gradient hole, not a level hole": at the dead point the heaviest term (upright) was saturated with zero gradient, still paid for not moving, and the one live gradient (base_height) pointed at the thigh-horizontal torque barrier. A prone ROM scan had already ruled out pushing up from prone.
Change
R0.4: feet_spread_excess = clamp(ankle distance - 0.215, 0, inf) x upright gate, weight -2.0, plus a height-decay factor added after measuring that the policy's real standing stance was 406 mm, not the 215 mm nominal (the plain version would have taxed every successful stand 0.38/s). R0.5: the same shape on the fore-aft axis, feet_fore_seated = |fore-aft offset - 0.05| x upright gate x height decay, target +50 mm (the measured natural offset of standing postures). One variable per rung.
Outcome
R0.4: seated ankle distance 561 -> 360 mm, supine/side exactly unchanged, prone 0 -> 1.9%, mid 45.9 -> 62.2%; a probe then showed the discriminant had moved to the fore-aft axis (standing starts +42 to +51 mm, the prone seat -168 mm). R0.5: supine 99.4, prone 99.4, side 100, mid 100%, re-falls 0%; both geometry terms collapsed to ~0 near iteration 13,100 as base_height rose, and the prone fore-aft offset went -168 -> +56 mm - the term's own target, closing the causal chain. The cost, unmeasured at the time: action jitter rose 33% (sum |da|^2 6.82 -> 9.06).
Mechanism
An upright-gated hinge is inert while the robot rolls and exactly zero in the achieved stance, so it adds gradient only inside the stuck basin; a seated robot with its feet behind or outside its COM must make a kinematically unfavourable transition to stand, and moving the feet under the body removes it.
Applies when
- a get-up or transition skill fails from one start category only
- successful and failed episodes differ in a measurable geometric quantity
- a shaping term might tax the posture successful episodes already use
“`recovery_r0_5`,唯一变量 = 追加 `feet_fore_seated`(与 R0.4 同形状,只换测量轴)。 … 对照 R0.4 的 prone(3.1%,360 mm,**−168 mm**):前后偏移从 −168 走到 +56, 正是这一项的目标量,**判别量被消掉后成功率随之到顶** —— 因果链完整。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §20 R0.5(前后向脚位置项):成功率门全过 —— prone 0/159 → 158/159 A 2-degree joint-zero calibration fix moved the whole runnable envelope - re-test old "cannot run" verdicts after recalibration
zero-offset-calibration-shifts-envelopeDate every hardware verdict with the calibration state; after any zero/mount recalibration, re-test previously condemned policy-power combinations and previously "unexplainable" posture offsets before attributing either to training or model.
Symptom
s1d was on record as "only runs at power 0.7" (kicked wildly at 0.8); after a calibration pass, the same policy ran 12 s at 0.8 with no kicking at all.
Context
The calibration had fixed a 2.08 deg zero offset on r_hip_roll - exactly the constant error source on the dominant joint of the kicking oscillation loop ("恰是乱踢振荡环主导关节的常值误差源"). The three-generation post-calibration hardware sweep also closed a second case: the robot's mysterious "backward lean" disappeared after calibration, and the sim-real posture difference collapsed from opposite-sign 5+ deg to same-sign ~2 deg ("后仰案实质了结") - the lean had been a sensing/zero artifact, not a mass-model error. Booked consequence: if the s1d recovery re-verifies, "真机可跑档整体 上移" - every policy's runnable power envelope shifts up, and downstream lineages' hardware expectations get revised.
Change
Joint-zero and mount calibration promoted from setup chore to a variable that dates hardware verdicts: verdicts about which power/scale levels a policy can run are conditioned on the calibration state they were measured under.
Outcome
One policy rehabilitated at a higher power level; one standing sim-real posture discrepancy closed without touching model or training; a pending re-verification booked rather than asserted.
Mechanism
A constant joint-zero error acts as a persistent disturbance injected at the feedback loop's most-loaded joint; near an oscillation threshold, removing a 2-degree bias is the difference between a stable and an unstable loop. Since the error is additive and machine-side, it shifts every policy's stability envelope simultaneously - which is why verdicts must carry their calibration date.
Conflicts
The s1d rehabilitation awaited one confirming re-run at the time of writing ("待复核一跑坐实") - the offset-as-cause reading is the head suspect, not a closed verdict.
Applies when
- a policy oscillates at a power level others tolerate
- sim and real disagree on a constant posture offset
- deciding whether to re-test old hardware verdicts after maintenance/calibration
“发现①:s1d@0.8 能跑了(旧账「只有 0.7 能跑」)——12s 无乱踢。头号嫌疑 = 标定修正:r_hip_roll offset 修 2.08°,恰是乱踢振荡环主导关节的常值误差源。… 发现②:「后仰」标定后消失 … sim-real 姿态差从反号 5°+ 收敛到同号 2°,后仰案实质了结。”
train/README.md § 真机 @0.8 三代横评(2026-08-07 标定后) 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 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) Anchoring the action on the measured joint angle (target = q + beta*a), with a beta curriculum down to tau_limit/kp, bounded torque by construction, removed the re-falls and later stood the robot up on hardware
beta-anchored-action-targetFor large-motion skills on position-controlled actuators, bound the action relative to the measured joint angle with a per-joint authority of tau_limit/kp, curriculum the authority down from full range, keep the curriculum state out of the observation and pin acceptance at the deployed authority - and make the deployment code refuse to run the anchored contract without a measured q.
Symptom
The V0 full-range absolute action produced violent targets; the V1 command-anchored rate limit made standing oscillate. Both failure modes came from how the action becomes a target.
Context
V2.0 (user approved, from scratch): BetaAnchorJointPositionAction, target = q_measured + beta_j(m)*a, memoryless per step. beta_j(m) = floor + m*(beta0 - floor), beta0 = the contract half-range (m = 1 reproduces V0 authority), floor = min(tau_limit/kp, beta0): hip_pitch 1.309 -> 0.40, knee 1.047 -> 0.40, hip_yaw -> 0.917, the other joints unchanged - the tightening lands exactly on the joints the V0 torque account convicted. m drops 0.1 per step when a standing-share EMA exceeds 0.35. beta is NOT in the observation, so the 45-dim contract is untouched; acceptance is pinned at m = 0 because the Python curriculum state is not saved in the checkpoint. The deployment chain got a new profile (recovery_v2: action_anchor current_q, explicit per-joint beta written into the contract, independent of the gain profile), and policy_io raises if q is missing rather than silently falling back to the absolute contract; the old profile's check reproduced its pre-change deviation bit for bit.
Change
New action term and beta curriculum; later the RS06 floor was lowered 0.40 -> 0.30 -> 0.25 (kp*beta 7.5 N*m) and the stamped deployment profile was synced to 0.25.
Outcome
First acceptance at m = 0 (v2_0b): re-falls 0% in every category, the torque gate passed for the first time on the line (worst 69.9%), knee jitter 0.004; supine 98.8 / side 88.8% with prone and mid still failing (fixed by the conditional pull curriculum). MuJoCo showed demand at or under the limits (hip_pitch 11.7/12 against V0's 26.8). Lowering beta cut impact (hip_pitch demand 9.7 -> 8.5 N*m) but barely slowed the get-up - it had become coordination-limited. Enabling the policy moves the target only +/-beta around the current pose, so there is no homing fling; the 08-11 real get-up and the later v3_1p1c both run on this contract.
Mechanism
kp*beta caps the proportional torque in a single step with no build-up delay and no memory, giving both a hard impact bound and full balance bandwidth.
Applies when
- a skill needs full joint range but hardware torque limits are low
- absolute position targets cause impacts or saturation
- changing the action semantics of a contract that deployed policies share
“**动作项** `BetaAnchorJointPositionAction`:`target = q_实测 + β_j(m)·a`, 逐步无记忆 … **Play/验收钉 m=0(= floor = 部署档)**:python 课程状态不进 checkpoint, Play cfg 显式 `beta_m_start=0` … 判读:**结构赌注兑现** —— 站姿零再摔 + 力矩账首过(kp·β 封顶按构造)”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §33 V2.0 预注册(2026-08-10,用户点头开工):β 锚定动作空间,从零训 A ratio metric flipped the verdict - spectral share rose while absolute high-frequency energy fell 16%
ratio-metrics-need-absolute-checkNever compare share/percentage/centroid metrics across conditions whose totals differ; pair every ratio with its absolute numerator before issuing a verdict, and log retracted judgments so they are not re-derived.
Symptom
walk_v6 was provisionally judged "more jittery" than v5 because the joint-velocity spectral centroid rose 2.91 -> 3.62 Hz and the >4 Hz energy share rose 12.6% -> 17.8%.
Context
Absolute measures said the opposite: first differences of actions fell 1.54 -> 1.31, second differences 2.55 -> 2.19, and absolute high-frequency energy fell 16% (0.490 -> 0.413). The shares and centroid rose only because low-frequency content fell even more - the denominator shrank. The interim judgment was retracted in writing so it would not be reused.
Change
Metric discipline noted: "占比类指标在总量变化时不能直接比较" - share/ratio metrics are not comparable across conditions when the total changes; verdicts about smoothness must cite absolute energies or difference norms.
Outcome
v6 correctly classified as smoother, not jitterier; the retracted judgment logged under "被推翻的一个中间判断(记下来免得复用)".
Mechanism
A ratio confounds numerator and denominator; any intervention that removes low-frequency content raises every high-frequency share without adding a single joule of jitter. Only absolute quantities support cross-condition comparison when totals move.
Applies when
- comparing smoothness/jitter/spectral metrics across versions
- any percentage-based metric moves after an intervention
- writing an eval report that includes normalized quantities
“我一度说"v6 动作更抖" … 错了: 动作一阶差 1.54→1.31、二阶差 2.55→2.19 都在降 … 谱质心升高只是因为低频成分掉得更多, 绝对高频能量实际下降 16%(0.490→0.413)。占比类指标在总量变化时不能直接比较。”
train/WALK_DIAGNOSIS.md § 过程中被推翻的一个中间判断(记下来免得复用) The first real-robot get-up was "very violent, kicking on the floor, dangerous" - a sim-perfect policy with no reason to be slow, unbounded absolute targets, no domain randomization and a rate limiter that filtered nothing; the task was restated as "safe, slow, transferable"
first-real-get-up-violent-stage-one-policyDo not put a get-up policy on hardware until its action is bounded (hard bound or state-anchored targets), smoothed, randomized and tested at the real pipeline's latency, and say explicitly that the task is "safe, slow and transferable" - a simulation-perfect policy optimizes only "gets up".
Symptom
On 2026-08-09 the user ran a V0-lineage recovery policy on the real robot and stopped it: very violent, kicking on the floor, dangerous. The planned next rung (a heavier torque_headroom) was never started.
Context
The spec had pre-registered that R0/R1 products stay in simulation and that the real-robot precondition was the R3 smoothing rungs plus a bridge-slew check plus a hanging protocol; the robustness (DR) rungs had not run. In simulation the policy passed 100% with a get-up of about a second. Which ONNX, which gain profile and whether a torque/joint log existed were left "to be recorded later" and never were.
Change
The V0 ladder was stopped at its best product (R3.1, sim only) and a re-rooting proposal was put to the user. The spec's four-layer account: style (the reward pays for standing early and nothing pays for slowness - HumanUP's "Stage I" get-up, "fast but unsafe ... infeasible for real-world deployment"); impact (full-range absolute targets with no hard bound, raw |a| up to 4.77, action saturation 100%, a single-step change of 0.306 saturating hip_pitch); transfer (zero DR, friction pinned at 1.0, the learned leg bracing); link (the bridge's RL slew equals vel_limit, 0.2-0.66 rad per step, while the real pipeline has 1-2 steps of time-varying latency and acceptance ran at delay 0).
Outcome
The line was re-rooted twice (training-side rate limit, then the beta-anchored action space) and gained a hang protocol before the next real attempt; on 08-11 a beta-anchored policy produced the line's first real get-up.
Mechanism
A task reward that pays for standing early selects the fastest feasible get-up; with absolute full-range targets every large target jump is a torque impulse bounded only by the clip; zero DR and braced-leg solutions do not transfer; and a limiter set at the velocity limit does nothing at 50 Hz.
Conflicts
The four layers are the spec's reconstruction from simulation probes and the literature; the real run's policy file, gain profile and log were never recorded, so no layer was confirmed against hardware data.
Applies when
- a first hardware trial of a high-effort skill is being scheduled
- sim success is high but the policy saturates actions or torques
- pre-registered hardware preconditions are not all met
“用户真机反馈:**非常猛、地上乱踢、危险**,叫停(R3.3 torque_headroom 加档已选型 weight −0.5→−1.5,未启动)。真机细节(哪个 onnx、什么档、有无 τ/q log)**待补记** … 任务从"能起来"变成 **"安全、慢、可迁移"** … **链路层**:桥层 slew RL 档 = vel_limit(10/20/33 rad/s ≈ 每拍 0.2~0.66 rad), 对 recovery 形同虚设;真机 1~2 拍时变延迟,验收默认 delay 0。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §26 真机叫停与换根判决(2026-08-09) 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) Close a question with an audit, then freeze the wording - later symptoms may not reopen it without new hard evidence
frozen-verdicts-semantic-boundariesWhen an audit closes a hardware-vs-policy question, record the closing evidence, freeze a citable wording for future recurrences, and set the reopening bar explicitly; separate robustness perturbations from plant-truth questions so a DR rung's failure can never silently reopen a closed measurement.
Symptom
Recurring directional bias on the robot kept re-suggesting "maybe the hardware/COM/mechanics are asymmetric", threatening to re-litigate questions that audits had already closed - burning attention each time a descendant policy leaned or drifted.
Context
Two boundary decisions were written as permanent: (1) semantic separation - "S2④ COM ±20mm = 纯鲁棒性扰动,不再承担「解释真机后仰」任务" - if the COM-DR rung degrades, the ONLY allowed conclusion is "policy insufficiently robust to COM uncertainty"; reopening "is the CAD COM wrong" is forbidden because the mass audit was completed and closed (@63f9212). (2) a frozen wording for chirality, to be quoted verbatim whenever left/right bias appears in later rungs: observed directional bias = policy-level spontaneous symmetry breaking; plant asymmetry = no supporting evidence after the mass + model symmetry audit; mitigation candidate pi_sym queued, not blocking. The evidential basis was quantitative: the root policy was perfectly symmetric under +/-6 N*s pushes (40/40) while descendants broke (17/40, 13/40) - "手性是 S2 训练中获得的, 根没有; 机械侧已双 PASS 关案, 不重开".
Change
Closed questions carry (a) the audit commit that closed them, (b) a frozen citable wording for recurrences, and (c) an explicit evidence bar for reopening ("无新硬证据不得重开").
Outcome
Later chirality observations (C2's 15 pp turn gap, hip_roll drift bias) were handled as policy-lineage properties with policy-side mitigations, without a single hardware re-audit cycle.
Mechanism
Symptom classes recur under different guises; without a frozen verdict each recurrence re-runs the same expensive investigation and risks a different (worse-informed) conclusion. Freezing verdict plus wording converts recurring symptoms into citations, while the evidence bar keeps the closure honest rather than dogmatic - the root/descendant symmetry comparison is what makes "it's the training, not the machine" checkable at any time.
Applies when
- a recurring symptom keeps suggesting an already-audited hardware cause
- writing conclusions for a completed calibration/audit
- a DR rung's degradation invites re-measuring the plant
“若 S2④ 退化,结论只能是「当前 policy 对 COM 不确定性不够鲁棒」,不得重开「CAD COM 是不是错了」… 手性冻结表述 … Plant asymmetry: no supporting evidence after mass + model symmetry audit … 无新硬证据不得重开机械不对称”
train/OMNI_V0_SPEC.md § 4. 语义分界与手性冻结表述(2026-08-07 用户定,永久) 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 Training-log reward values and fixed-command eval values live on different distributions - comparing them once claimed a 44% improvement that was really 6-10%
same-distribution-reward-comparisonQuote reward-term values only with their distribution attached (command range, DR on/off, environment), and compare across runs only when those match; re-measure in a common environment before claiming any improvement percentage.
Symptom
A v6-era analysis concluded slip had dropped 44% by comparing the training log's Episode_Reward against values calibrated in a fixed-command play environment; a same-condition re-measurement showed the true improvement was 6-10%.
Context
The training log's reward is an expectation over the training command distribution (vx 0.15-0.5, yaw +/-0.6, with pushes and domain randomization); play-environment calibrations are taken at a single fixed command with DR off. Subtracting one from the other compares apples to oranges - the warning was written into the v7 pre-flight: "奖励数值只能在同一指令分布下比较 … 拿它和固定 cmd 的 play 环境标定值相减会得出错误结论(v6 那轮已经栽过一次)".
Change
Rule adopted: any before/after reward-term comparison must hold the command distribution, DR state, and evaluation environment fixed; training-log values compare only against training-log values of runs with identical command/DR configs.
Outcome
The phantom 44% improvement was retracted; later term-level accounting (e.g. the C4 ignore-floor work) consistently specified its distribution before quoting numbers.
Mechanism
A reward term's expectation depends on the visited-state distribution as much as on the policy; changing the command distribution or DR moves every term's baseline. Cross-distribution differences therefore measure the distributions, not the policy change.
Applies when
- comparing reward telemetry across training runs or vs play evals
- claiming improvement percentages from training logs
- term-level reward accounting for diagnosis
“奖励数值只能在同一指令分布下比较。训练日志的 Episode_Reward 是在训练指令分布上算的(vx 0.15~0.5 / 偏航 ±0.6 / 带推力与域随机化), 拿它和固定 cmd 的 play 环境标定值相减会得出错误结论(v6 那轮已经栽过一次: 据此以为滑移降了 44%, 同条件对拍只有 6~10%)。”
train/WALK_V7_SPEC.md § 3. 开训自查 ⚠️ 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) 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