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
98 cards matching “reward-observability-limit”.
Cutting swing amplitude 40% raised yaw-momentum demand 53% - the falsified fix is recorded so nobody walks that road again
amplitude-cut-falsified-yaw-fixTest gait fixes against the quantity the ground must actually supply (torque/force rates vs friction ceilings), not against kinematic proxies; record falsified fixes with their mechanism so the search space shrinks permanently.
Symptom
Support-foot yaw slip stayed at ~223 deg (vs 225 deg) after walk_v6 cut joint swing amplitudes by ~40% (hip_pitch 20.6->10.4 deg, knee 31.3->18.8 deg) - the change built on the theory "smaller swing = less yaw momentum to dump into the ground".
Context
Direct measurement inverted the theory: yaw-momentum amplitude ROSE 16% (+/-0.1000 -> +/-0.1159) and its rate of change rose 53% (1.86 -> 2.84 N*m demanded from the ground), pinned exactly at the foot's supply ceiling (2.8-3.3 N*m at mu 0.6-0.7) - so slip could not drop. The extra demand lives in higher harmonics: v6's crisper foot placement (better clearance 34 mm, lower landing force) shortens the momentum- exchange window, concentrating the same exchange into less time. The verdict was written as a closed road: "下一轮不要再走这个方向". An honest residue was also booked: WHY amplitude down but momentum up 16% remained unresolved, with the named next step (per-rigid-body decomposition of H_z, since per-joint RMS sensitivity ignores phase correlations).
Change
The "reduce amplitude to reduce yaw momentum" lever was removed from the planning space; future yaw-slip work redirected toward the supply side (friction) and momentum-rate mechanics.
Outcome
Slip unchanged (225 -> 223 deg); the falsification and its mechanism became a permanent constraint on the fix search space.
Mechanism
Ground yaw torque demand scales with the rate of change of angular momentum, not its amplitude; kinematic amplitude cuts that also sharpen contact timing can raise dH/dt while lowering range. When demand exceeds the friction-limited supply ceiling, slip is set by the ceiling, so demand-side changes below the ceiling do nothing visible.
Applies when
- attacking foot slip or yaw drift via gait shape changes
- a fix targets an amplitude while the constraint is a rate
- documenting a failed intervention after a version comparison
“walk_v6 | ±0.1159 (+16%) | 2.50 Hz | 2.84 N·m (+53%) … 脚的供给上限 2.8~3.3 N·m(μ 0.6~0.7)—— v6 正好顶在天花板上, 所以滑移一点没降。… 结论: "减小摆动幅度以降低偏航动量"这条被 v6 证伪 —— 砍 39% 幅度, 需求反升 53%。下一轮不要再走这个方向。”
train/WALK_DIAGNOSIS.md § ② 未生效的机理: 减小摆动幅度反而让偏航需求上升 Drop the frozen policy into chosen configurations - a squat 2.7 cm lower than the stuck pose stood 52% of the time, the stuck W-sit 0%, and the interpolation between them showed a wall, not a slope
configuration-probe-wall-not-slopeWhen a policy is stuck, probe the frozen policy from a grid of hand-placed start configurations, including interpolations between the stuck state and a nearby state it escapes from; one read-only experiment separates height, torque, sampling and configuration and tells you whether to prevent entry or train the exit.
Symptom
After R0.3 the policy stood from 62% of starts and never from the W-sit it fell into; height, torque, missing samples and reward were all plausible suspects.
Context
A read-only probe placed the R0.3 policy directly into specified configurations. Squats (hip, knee, ankle) = (-.65,-1.3,-.65) stood 100%, (-1.0,-2.0,-1.0) 89.8%, (-1.2,-2.4,-1.2) at 0.176 m 52.3%; the measured W-sit at 0.203 m 0.0%; the account-(3) hand-over state (146 deg tilt) 34.4%; linear interpolations from the W-sit toward the squat at 25/50/75% stood 0.0/0.0/3.1%. The squat family's quasi-static torque is 16% of the limits, and the W-sit was visited ~9 s per episode in training. FK showed the squat family (-a,-2a,-a) keeps the torso vertical, the feet flat and the COM over the feet all the way from 0.146 m to 0.384 m.
Change
Height, torque and sampling were eliminated in one experiment; the next rungs targeted entering the W-sit (foot placement) instead of escaping it, and seeding the dead point itself was ruled out because it was already visited every episode.
Outcome
Pure configuration: the W-sit (hips externally rotated +/-47 deg, knees folded 110 deg, shins flat, feet beside the body) is a different place from the sagittal squat (feet flat under the COM). The policy's standing skill was bound to a narrow sagittal family, and the wall was confirmed by the interpolation. The foot-placement rungs that followed took prone from 0/159 to 158/159.
Mechanism
A learned skill covers the neighbourhood of the states it succeeded from; a start state outside that neighbourhood fails regardless of height or torque, and an interpolation that stays at zero until close to a working state shows the boundary is sharp.
Applies when
- a policy stalls in a specific posture and several causes are plausible
- deciding between reverse-curriculum seeding and entry-prevention shaping
- a feasibility account says a path exists but the policy does not take it
“**决定性对比:比死点矮 2.7 cm 的蹲姿站立 52.3%,死点 0.0%。** 所以不是高度、 不是力矩(蹲姿族准静态力矩膝 1.96/12、踝 1.24/17,只占 16%)、也不是训练采样 (死点每局被访问 ~9 s)。**是纯位形问题** … 插值实验进一步显示这**不是坡是墙** —— 走到 75% 仍只有 3.1%”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §15 死点位形实验(只读探针,同一个 R0.3 策略放进指定位形) An outer heading P-loop at deploy cut drift 10x because its output stays inside the trained command band - then training was aligned to it
deploy-heading-loop-and-align-trainingFix drift-class problems first with an outer loop whose output provably stays inside the trained command band; when adopting it permanently, align the training command generator to the deployment's actual command mixture (feedback-driven AND constant), matching law, gain, and clip exactly.
Symptom
Persistent heading drift on straight-line walking (v6 net yaw 60.3 deg over 15 s) that reward-side fixes had only partially tamed.
Context
The deploy stack added --heading: an external P loop wz = clip(0.5 * wrap_to_pi(theta0 - theta), +/-0.6), recomputed each frame and fed into the policy's ordinary wz command slot. Measured: net yaw walk_v6 60.3 -> 5.8 deg, walk_v5 17.4 -> 4.2 deg. A run-level audit later corrected the mechanism story: training had heading_command=False since v1 - the policy had NEVER seen heading-error feedback, so the loop works purely because its output lands inside the trained command distribution wz ~ U(+/-0.6): "收益真实,当时的机理解释写错了" (the benefit is real; the mechanism explanation had been wrong). v8 then closed the loop properly: training-side heading command enabled with rel_heading_envs=0.5 - half the envs get heading-error-driven wz, half get explicit constant wz, because deployment feeds wz BOTH ways (straight-line = heading feedback, turning = constant command) and rel=1.0 would have made constant-wz turning out-of-distribution. The law, gain, and clip were aligned item-by-item between trainer and deploy tool.
Change
Deploy-side outer loop first (no retrain needed); then v8-D enabled the matching training-side heading command at rel=0.5 with identical gain (0.5) and clip (+/-0.6), contract unchanged (wz slot carries the computed value).
Outcome
Drift handled at deploy (5.8 deg) generations before training caught up; the alignment removed the residual train/deploy distribution mismatch, with the accepted cost booked (open-loop straight walking becomes more OOD for heading-envs - irrelevant since acceptance and deployment always run the loop).
Mechanism
A learned velocity-tracking policy is a valid inner loop for any outer controller whose commands stay within the trained command distribution - the policy needs no knowledge of the outer objective. Full alignment then requires training on the same mixture of command sources the deployment actually uses, in the observed proportions.
Applies when
- heading/position drift on a velocity-tracking policy
- designing outer loops over learned locomotion controllers
- training command distribution differs from how deployment feeds commands
“审计更正(2026-08-02,run 级 env.yaml):训练侧自 v1 复盘起就是 heading_command=False … 策略从未见过航向误差反馈。--heading 是评估/部署侧外加的航向 P 环(wz=clip(0.5·err,±0.6), 落在训练分布 wz~U(±0.6) 内)。实测净偏航 walk_v6 60.3° → 5.8° … 收益真实,当时的机理解释写错了”
train/WALK_V7_SPEC.md § 0. 本轮之前已经改掉 (航向闭环, 含审计更正) 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) Design the next run to complete the 2x2 - either outcome then convicts or acquits a factor cleanly
fill-the-missing-factorial-cellWhen two config factors are jointly suspected, lay out the factorial of existing evidence, spend one run on the missing cell with both interpretations and an early-abort tripwire written in advance - and treat either outcome as a verdict, not a disappointment.
Symptom
Hip joints froze at the action clamp in v7, but the history could not say whether the culprit was the raised action_rate (-0.2) or the halved reference amplitude (scale 0.15): existing versions covered only three corners of the (rate x scale) space - v5 (-0.03, 0.30) healthy, v6 (-0.03, 0.15) healthy, v7 (-0.2, 0.15) frozen.
Context
v9 was designed explicitly as the missing cell (-0.2, 0.30), with the readings pre-registered: v9 not frozen -> the real anti-freeze force was always the reference amplitude and -0.2 may stay; v9 frozen -> -0.2 is convicted beyond appeal (freezes at both amplitudes) and the next version goes straight to a structural fix. "两个结局都是干净的信息" - both endings are clean information.
Change
One training run allocated purely to complete the factorial, with freeze tripwires (joint_pos_ref telemetry <0.1 at iter 1000-1500 -> abort, do not run to 6000) so a conviction costs the minimum compute.
Outcome
v9 froze - the rate weight was convicted at both amplitudes ("−0.2 铁案定罪"), and v10 moved to the structural saturation fix with the weight question closed instead of re-litigated.
Mechanism
Three corners of a 2x2 leave the two factors confounded in the failure corner; the fourth observation makes each factor's marginal effect identifiable. Pre-registering both readings turns the run into a guaranteed-informative experiment regardless of outcome.
Applies when
- two config changes are confounded in a failure
- version history already covers some corners of a factor grid
- deciding what single experiment buys the most attribution
“这恰好补齐一个 2×2 实验矩阵的缺格 … v9 不冻 → 真正的抗冻结主力一直是参考摆幅,−0.2 可以留;v9 仍冻 → −0.2 铁案定罪(两种摆幅下都冻),v10 直接上结构修复 … 两个结局都是干净的信息。”
train/WALK_V9_SPEC.md § 0. 设计原则 (2×2 实验矩阵) 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 training a one-leg stand, the accounts and a probe showed the default gains could not hold it at all - kp 20 needs 0.39 rad of error to carry the static roll moment, more than the whole adduction range - so per-joint gains came first, and thermal limits set the session length
single-support-gain-authority-probeBefore training a posture that loads one joint statically, compute the steady tracking error load/kp and the series stiffness against m*g*h, and prove with a simple hand-written controller that the posture can be held under the deployment gains - change the gains first if it cannot; then size session length from the thermal account.
Symptom
The one-leg line (standing on one foot, the other folded back, no hopping) had to decide whether the existing gain profile could hold single support before any reward was designed.
Context
Hardware accounts (9.792 kg, COM 0.234 m high, 170 x 80 mm feet, legs 80% of the mass): moving the COM over one foot needs 107 mm of shift and the 20 deg hip-roll adduction range gives 131 mm - geometrically enough. The static frontal moment is 7.8-9 N*m, within RS02's 17 N*m - torque is enough. But at kp 20 carrying 7.8 N*m needs 0.39 rad of tracking error, more than the entire adduction range, and the real robot had already shown it: commanded +0.17, actual -0.04 (0.21 rad droop) under load, 0.0008 rad hanging - load, not the motor. A probe (probe_oneleg.py) then showed open-loop PD cannot hold single support on physics grounds, so the criterion became "an equilibrium exists and a hand-written 4-gain COM feedback can hold it": single-support roll stiffness is hip and ankle in series and must exceed m*g*h_com = 22.5 N*m/rad; ankle kp 12 in series with hip kp 80 gives only 10.4 (open loop 16/16 fell), ankle 60 with hip 80 gives 34.3 (52% margin).
Change
A per-joint gain profile (rl_oneleg: hip_roll kp 80, ankle_roll kp 60, the rest as rl_default) - which needed per-joint gain support in robot.yaml, the bridge, deploy and the trainer's actuator groups - decided before training. Thermal account: single support makes hip_roll the dominant heat load (about 7.8 N*m against a 7 N*m continuous rating), so acceptance and demos run in segments of at most 60 s with a temperature check.
Outcome
Under rl_oneleg the hand-written feedback held six cells cleanly for 6 s (hip_roll steady torque 2.1-3.4 N*m, half the thermal budget); under rl_default the same feedback on the same cells fell 0/4. The trained V0 policy then passed its 40-cell acceptance.
Mechanism
With PD position control, the steady error needed to carry a static load is load/kp; when that error exceeds the joint's range the posture is unreachable whatever the policy does, and series compliance between joints lowers the effective stiffness below the gravity stiffness that single support demands.
Applies when
- single-support, crouched or one-arm-load postures on PD actuators
- a joint "droops" under load on hardware but tracks well when hanging
- deciding whether a new skill needs its own gain profile
“但 kp=20 时撑住 7.8 N·m 需要 **0.39 rad 跟踪误差 > 整个内收行程**。真机已实测: 命令 +0.17 实际 −0.04（droop 0.21 rad），悬挂时 0.0008 rad——是负载不是电机。 … 单支撑滚转是 hip/ankle **串联**刚度，必须 > m·g·h_com = 22.5 N·m/rad；ankle kp12 串 hip80 只有 10.4（开环 16/16 全摔），60 串 80 = 34.3（裕 52%） … **rl_default 同反馈同格 0/4 全摔**（增益档必要性对照）”
git:Lucen V2@origin/oneleg-line:train/ONELEG_V0_SPEC.md § §1-1 单脚站: 几何可行，卡点是 hip_roll 增益权限 / §2 A 线增益 / §5 probe 定谳 A torque-tail penalty was paid for by bracing the legs against each other - the second simulator's leg-contact count caught it, and the first explanation ("the trainer can't see self-collision") was retracted from the run's own config
torque-penalty-bought-by-leg-bracingWhen a penalty lowers a demand metric, look for what the policy traded to get there - keep self-contact frames and foot spacing as standing sim2sim readouts - and check any "the trainer cannot see X" explanation against the run's resolved config before it enters the record.
Symptom
After R3.1's torque_headroom term collapsed the demand tail, MuJoCo success fell 100 -> 98% and leg-leg contact frames at mu 1.0 rose 750 -> 2,190 (worst rollout 177 -> 450). The one failure (prone seed 2) had the legs crossed, one foot on the other leg, trapped at 0.067 m - visible on video.
Context
Across the ten prone seeds, foot spacing and leg-leg contact frames were monotonically anti-correlated, and the failure was the extreme of the series. Pulling the legs toward the midline shortens the hip_roll lever arm and lowers torque demand. At the time the spec explained it as Isaac training without self-collisions ("a free lunch in a simulator without self-collision").
Change
Leg-leg contact frames and foot spacing were tracked in every MuJoCo gate; R3.2's candidates were "train with self-collision on" or "a minimum leg spacing term" - not stacked.
Outcome
The next rung's joint-velocity penalty incidentally erased the dependency (2,190 -> 86 frames). On 08-10 the runs' logged env.yaml showed enabled_self_collisions true in both r3_1 and v2_2 (inherited from walk v10): the tangle was physically learned bracing, visible to both simulators, and the Isaac/MuJoCo contact-count gap was mesh and contact fidelity. The "self-collision debt" narrative was withdrawn for the whole line.
Mechanism
A penalty on demand rewards any configuration that lowers demand; legs pressed together act as a mutual support that fails when contact geometry shifts slightly.
Conflicts
§24 attributes the dependency to self-collisions being disabled in training; §36 retracts that from the runs' env.yaml ("§24's mechanism explanation was wrong") and keeps the older sections unedited as history.
Applies when
- a torque, impact or energy penalty improves its metric and cross-sim success drops
- legs or links approach each other after a regularization change
- an explanation relies on a simulator setting nobody checked in the run config
“prone 十个 seed 逐条看,脚距与腿-腿接触帧数单调反相关, 而唯一失败的那条正是最极端的一条 … 机制上说得通:把腿收到身体中线附近能缩短 `hip_roll` 力臂、降低力矩需求”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §24 代价:MuJoCo 成功率 100% → 98%,病因是两腿卡住 Push-test protocol - positive side first, fragile side spotted, axes aligned in the log, and cross-machine push counts stay qualitative
push-test-chirality-protocolOrder disturbance tests from the robust side to the fragile side with protection scaled to sim-measured asymmetry, align and log frame conventions before testing, and treat cross-domain disturbance counts as qualitative evidence only.
Symptom
Hand-push testing on hardware risked falls on a side sim had already flagged as fragile, and push counts invited apples-to-oranges comparison with sim numbers.
Context
Sim chirality was explicit: descendants were far more fragile in -y (fric-3000@kd1.2: +6 N*s survived 15/20 vs -6 N*s only 3-9/20) while the s1e control was perfectly symmetric (40/40). The protocol therefore: push the positive direction first, keep a spotter for the negative side; before any push, record which real-robot side corresponds to sim's +y in the log ("上机前对一次坐标"); and - citing the chaos lesson ("混沌课文") - real push results are used only as qualitative corroboration, never compared numerically with sim survival counts across machines.
Change
Push testing became a scripted, chirality-aware protocol with frame alignment as a logged precondition and an explicit epistemic limit on cross-domain count comparison.
Outcome
The fragile side was tested with protection informed by sim's quantified asymmetry; logs stayed interpretable because the frame correspondence was recorded before the first push.
Mechanism
Disturbance-response chirality is a real, quantifiable lineage property, so test order should follow measured fragility; and perturbation outcomes are chaotic in the details (divergent trajectories from tiny differences), so counts do not transfer across domains even when qualitative rankings do.
Applies when
- planning push/disturbance tests on hardware
- sim shows directional asymmetry in disturbance survival
- someone proposes comparing real push counts to sim counts
“先正向后负向, 负向留人扶 —— sim 手性明确: 后代在负 y 向显著更脆 (fric-3000 @kd1.2: +6 N·s 15/20 vs −6 N·s 3~9/20), 而 s1e@0.8 两向 40/40 完全对称。上机前对一次坐标 … 跨机不做二值结论 (混沌课文): 真机推力只作定性对照, 不与 sim 计数对比。”
train/REAL_RUN_S2.md § 3. 抗推 (可选, 人手推; 做则按此协议) No parameter tuning on the floor - a failing config retries once, then it is out; anomalies go back to sim
no-field-tuning-protocolHardware time is for executing and measuring the pre-registered matrix, never for tuning: failing configs get one retry then elimination, anomalies get recorded and reproduced in sim, and contract-check bypass flags stay unused.
Symptom
Hardware sessions create pressure to fix problems live - nudge a gain, tweak a scale - which destroys attribution and risks the robot.
Context
The anomaly-handling section of the acceptance sheet is three fixed plays: (1) falls at start -> retry once at the same settings; falls again -> that configuration is eliminated, "不现场调参" (no on-site parameter tuning); (2) limit cycle or motor screech -> stop immediately, record the gain level and the joint, reproduce in sim before any discussion; (3) systematic disagreement with sim -> record it as a finding (hardware outranks sim) rather than adjusting anything to force agreement. Related guardrails elsewhere in the sheet: never pass --allow-unstamped / --allow-plant-drift to bypass manifest checks - if it errors, something real is wrong, stop and look.
Change
Field sessions restricted to executing the pre-written matrix; every fix path routed through sim reproduction and the normal config/rung process.
Outcome
Sessions stayed interpretable (each run matched a documented config) and safety overrides never became habit; anomalies arrived back in sim as reproducible cases instead of half-remembered floor stories.
Mechanism
Field-tuned values are measured under adrenaline on one floor with no logging or baselines - they contaminate the config lineage and are unattributable afterwards; and every bypass flag that skips a contract check converts a designed safety property into an operator promise.
Applies when
- a config fails or oscillates during a hardware session
- someone reaches for a live gain tweak or a bypass flag
- writing the anomaly-handling section of a deployment runbook
“起步即摔 → 换档重试一次, 仍摔则该档出局, 不现场调参。出现极限环/啸叫 → 立刻停, 记录档位与关节, 回 sim 复现再议。… 不要给 --allow-unstamped / --allow-plant-drift —— 三枚 ONNX 都已盖章 … 真要报错说明有别的问题, 停下来看。”
train/REAL_RUN_S2.md § 4. 异常处置 The walking lines' safety setting, power-scale 0.8, broke the recovery policy's full-range contract - it cut the ends of the joint travel (4/50 could not get up) and left the torque spikes untouched; a kp x 0.9 gain profile inside the trained kp band did the job
power-derating-cuts-full-range-contractA deployment derating knob means something only relative to the action contract: before reusing a line's "safe setting" on a new skill, check what it does to that skill's reachable range and to the term that makes the spikes, prefer a gain change inside the band the policy was randomized over, verify it in simulation, and re-decide when the contract changes.
Symptom
After the violent first real get-up (2026-08-09), the recovery policy needed a gentler setting for its next hardware test, and the walking and omni lines' standard derating - deploying at power-scale 0.8 - was the obvious candidate.
Context
The V0 recovery contract maps actions to absolute targets over the full joint range: a = +/-1 lands exactly on the URDF limits, and standing puts the knee at the clip. Candidates were compared on R3.1 in MuJoCo (5 categories x 10 seeds) on 2026-08-10 before any hardware time was spent.
Change
A new gain profile, rl_kp090 (kp x 0.9, kd unchanged), recorded in robot.yaml as the recovery hardware-test setting, with power-scale 0.8 explicitly banned for recovery.
Outcome
kp x 0.9: 48/50 got up; median torque demand on hip_pitch/knee fell from 120-125% to 100-104% of the deployment limit; leg-leg contact frames 2,152 -> 1,095; the change sits inside the +/-10% kp randomization the policy trained with. power-scale 0.8: 4/50 could not get up, because under the full-range contract it removes the ends of the travel (the deep squat's tucked legs, the straight standing knee), and the torque spikes (kp x error) did not fall at all. When the line moved to the beta-anchored contract, rl_kp090 was declared a V0-era choice that does not fit (beta is calibrated at kp 30) and deployment returned to rl_default; the deploy switch applies power scaling to the walking side only.
Mechanism
A power scale multiplies the action, which under an absolute full-range mapping shrinks the reachable workspace instead of softening the actuator; the spikes come from the proportional term on large errors, which only a gain change reduces - and a gain change inside the trained randomization band stays in distribution.
Conflicts
The undated operator runbook still carries an R3.1 "B comparison" command at power-scale 0.8 beside the rl_default baseline; the sources do not say whether it was written before the ban or was ever run.
Applies when
- reusing a power, torque or action scale from one skill on another
- a policy whose actions map to absolute targets over the full joint range
- choosing a gentler setting for a first or second hardware trial
“kp×0.9 / kd 不动 —— recovery_r3_1 成功 48/50, τ 需求中位 hip_pitch/knee 120~125% -> 100~104% 部署限, 腿-腿接触 2152 -> 1095 帧; ±10% 在训练 kp DR 带内. ⚠️ power-scale 0.8 对 recovery **禁用**: 全 ROM 契约下 0.8 砍的是行程 端点 (深蹲收腿/站直够不到), 实测 4/50 起不来, 且尖峰 (kp·err) 一点不降 —— 它是 walk/omni 的安全档, 不是 recovery 的.”
git:Lucen-recovery@origin/recovery:robot.yaml § gain_profiles 注释: recovery 真机测试安全档 (2026-08-10) / rl_kp090 A field experiment is allowed when it is pre-scripted, single-variable, and self-reversing (RAM-only writes)
reversible-single-variable-field-experimentsPermit hardware-side experiments only when scripted in advance with one variable, a log, and automatic reversion (volatile writes, git restore); keep every config mirror (yaml vs firmware) changed and restored as a unit.
Symptom
A hypothesis needed a hardware test - v5's "wild kicking" might trace to the RS00 torque cap being deployed at 11 N*m vs its trained 14 (-21%), on exactly the ankle-roll/hip-yaw joints doing lateral-yaw work - but changing limits in the field is the classic way to lose track of robot state.
Context
The experiment was written to be safe by construction: change exactly one number (robot.yaml RS00 tau_limit 11.0 -> 14.0), write to firmware RAM only (--write without --save, so a power cycle automatically rolls back to the saved 12/17/11), run the single logged trial, then restore both yaml (git checkout) and RAM immediately. The consistency requirement is explicit: the deploy tool's torque self-check compares against robot.yaml, so yaml and firmware must change and restore together; and the hypothesis scoping is itself single-variable - RS06's 67% cut was measured irrelevant (gait uses only 16% of rated) and hip torque was left alone for safety.
Change
Field experimentation policy refined: not "never touch hardware settings" but "only pre-scripted, one-variable, logged, auto-reverting changes with config/firmware kept consistent".
Outcome
The sub-experiment could answer the torque-cap hypothesis without any risk of the robot persisting in an undocumented state - forgetting to restore costs nothing but a git checkout.
Mechanism
The danger of field changes is state divergence (robot config drifting from the repo's record), not the change itself; volatile (RAM-only) writes bound the divergence lifetime to one power cycle, and single-variable scoping preserves attributability even in a field setting.
Applies when
- a hypothesis requires changing firmware limits or gains on the robot
- field debugging tempts persistent config writes
- designing safe escape hatches for deployment tooling
“程序(--write 不带 --save = 只写 RAM,断电自动回滚)… deploy 的限扭自检是对 robot.yaml 比对的,所以 yaml 和固件必须同改同还原;忘了还原也没事,断电重启即回 12/17/11(上次 --save 的值),但 yaml 要 git checkout。”
train/REAL_SWEEP_V5_V8.md § 4. 限扭子实验(可选二期,只对 v5,单变量) The +/-50 mm lateral COM randomization meant to spread the legs coincided with legs pulling IN - rolled back per its own pre-registered contract
com-dr-rollback-on-symptomWhen adopting a DR value that covers no local measurement, write its intent and rollback trigger into the config at adoption time; roll it back as the control arm the moment the symptom contradicts the intent, and promote the symptom's metric into the acceptance battery.
Symptom
After v7 adopted the reference developer's oversized lateral COM randomization (+/-50 mm) explicitly to force leg spread, the real robot's legs narrowed instead - lateral mean 154 mm / closest 107 mm in sim (nominal 214.5), narrower still on hardware with occasional leg contact.
Context
The rollback was clean because the adoption had been honest: the robot.yaml comment recorded the intent AND that the +/-50 value covered no local measurement (only a 16/7 mm measured offset existed; even the prior widening to +/-20 was subjective), plus the reference's own reported side effect (base sway) and the note "这一项要单独跑、 单独归因". When the opposite symptom appeared, v8 returned y to +/-20 mm as the control arm ("要么没起作用、要么帮了倒忙 … 按约定退回做 对照"), kept x/z untouched (a noise-level difference not worth another variable), and named the second suspect: the landing penalty itself, via the reference's own three-link chain (landing penalty -> stance narrows -> spacing penalty needed). A gate lesson was booked in the same table: v7's sim numbers had ALREADY crossed the line (154/107 vs v5's 182/147) - "这个指标本可拦下 v7" - so foot-distance became a standing acceptance row (min >120 mm, zero leg-leg contacts).
Change
base_com_offset_m y: 0.050 -> 0.020 (x/z kept), regenerated through the export tool rather than hand-editing derived files; foot-distance acceptance row added.
Outcome
A borrowed DR lever with no local measurement basis was retired the moment its symptom contradicted its purpose, at single-variable cost; the metric that would have caught it pre-hardware entered the gate.
Mechanism
DR ranges shape behavior through the policy's robustness strategy, which is jointly determined with every reward term; a lever that forces stance width on one robot can be dominated by a stronger narrowing pressure (landing softness) on another. Levers adopted without local measurement must carry their own rollback trigger, because there is no nominal to argue from when they misbehave.
Conflicts
Causality is not fully closed in the source: the narrowing may come from the landing penalty rather than the COM lever ("腿距的第二嫌疑人是 ④ 本身"); the rollback is the pre-agreed control experiment, not a verdict that the lever caused the narrowing.
Applies when
- importing DR ranges or behavioral-forcing randomizations from references
- a DR lever's observed effect contradicts its documented purpose
- a sim metric existed that would have caught a shipped regression
“⑥ 的本意 … 是逼策略把脚分开;真机结果是脚向内收且偶发相碰——要么没起作用、要么帮了倒忙。… 注释当时就写了"这一项要单独跑、单独归因"。现在症状出现了,按约定退回做对照。”
train/WALK_V8_SPEC.md § 3. 改动 C — 质心随机化退回(撤销 v7-⑥ 的 y 项) Deployment power derating damages non-forward axes far more than forward - sweep it in sim before deploying
power-scale-hurts-nonforward-axesTreat deployment power/torque scaling as a plant parameter: evaluate the policy in sim at the exact deployment scale, expect non-dominant axes to degrade first under derating, and either deploy at the training power or train with power randomization.
Symptom
Policies deployed at power-scale 0.8 (a safety derating of commanded torque) looked fine walking forward but were weak at backward and turning, inviting the wrong diagnosis "the skill was not trained well".
Context
Measured repeatedly: on s1e, going 1.0 -> 0.8 cost forward 18% but backward 58%; on C4-ff800, turn tracking was +25%/+40% at pw0.8 vs +75%/+58% at pw1.0, backward 51-52% vs 97-103%, while forward stayed 96-98% at both. Sim evaluation numbers in the plan were all pw1.0, but the robot was being run at 0.8.
Change
Pre-deploy protocol added: sweep the exported policy across power in sim (for PW in 0.8 0.9 1.0: eval_c_matrix --power $PW --seeds 20) and deploy at the first level where both turn directions reach >=50%. For C4 the recommendation was raise the robot to pw1.0 - the sweep showed it nearly free (saturation 47%->33%, left foot-clipping danger zone 25%->6%, cost only tilt 6.7->8.3 deg).
Outcome
Turning "weakness" resolved without any retraining; the sim sweep correctly predicted the real-robot signature at both power levels.
Mechanism
Forward walking is the reward-dominant, torque-cheapest skill with the most margin; backward/turn/sidewalk live closer to the torque envelope, so a uniform torque derating consumes their margin first. Training ran at power 1.0 (the trainer does no power scaling), so deploying at 0.8 is a systematic underactuation the policy never experienced.
Applies when
- deploying with any torque/power derating or safety scale
- secondary skills (backward, turn, lateral) underperform on hardware while forward walking looks fine
- choosing the deployment power level for a new policy
“power 衰减对非前进轴的伤害远大于前进轴（s1e：前进 1.0→0.8 掉 18%，后退掉 58%）。转向是非前进轴，0.8 下很可能明显跟不动。”
train/C_LADDER_RUN.md § 3c. A-2 上机前先定部署力度档 / 3p. 二 Fall recovery was defined as the whole chain - any fallen pose, a stable stand, a clean hand-back to walking - and built as a second policy behind a deploy-side switch, not folded into the walking PPO
recovery-two-policies-and-a-state-machineDefine a recovery skill by the whole chain it must complete, including the hand-back to the next controller; if it is built as a separate policy, make the switching logic and its handoff contract a deliverable of their own, and keep the recovery observation contract a subset of the locomotion one so a unified policy stays possible later.
Symptom
A walking robot that falls needs a human to stand it back up. The design question on 2026-08-09 was whether to teach getting up inside the existing omni walking policy or beside it.
Context
The user set the goal as "any fallen pose -> stand up alone -> stand stably", and the spec named the real difficulty as the full chain fall -> recovery -> stable stand -> correctly initialised walking history and clock -> walking, making the deploy state machine a first-class deliverable. A unified single policy had a real-robot precedent (arXiv:2605.18611, a state-dependent gate near 37 deg tilt) but was deferred until a recovery policy and an omni policy were each reliable. The line ran on its own branch and worktree with every walk/stand/omni/run config path untouched. The development path copied the G1 learned get-up logic (arXiv:2502.12152): first find any feasible get-up (ugly accepted), then add smoothing, torque and real-robot constraints. The recovery contract kept the base 45-dim observation (command slice held at 0, no gait phase, no frame history - their reasons do not apply to a skill without a clock or a velocity task), so it stays a prefix of the 215-dim omni contract and a later merge is not foreclosed.
Change
Two policies and a deploy-side switch instead of one retrained walking policy; recovery got its own minimal contract (45 dims, full-range action, later the beta-anchored profile) and its own acceptance battery.
Outcome
The split held for the whole line: on 08-14 deploy_policy gained a second (PolicyIO, ONNX) pair behind --recovery-policy, each loaded under its own manifest contract, and the runbook runs stand_v1b or omni_c4_ff800 as the locomotion side with recovery_v3_1p1c. The literature scan of 08-10 found that every verified get-up implementation deploys one end-to-end policy (or softly gated experts) and stages only on the training side - so the runtime state machine here is the walk/recovery switch, not a staged get-up.
Mechanism
A separate policy keeps each reward table single-purpose and lets a proven walking lineage stay byte-frozen; the cost moves to the handoff, where every piece of state one policy leaves behind (history, clock, last action, command) must be reset for the other.
Applies when
- adding fall recovery or get-up to a robot that already walks
- choosing between one unified policy and a switched pair of policies
- designing the observation/action contract of a secondary skill
“先做 recovery policy + omni policy 两个策略,部署侧状态机切换;不把 recovery 硬塞进现有 omni PPO。 … 任务定义:**任意跌倒姿态 → 自己站起来 → 稳定站立**。真正的难点不只是"起身", … omni walk**(§6 部署状态机是本 spec 的一等公民,不是附录)”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §0 目标口径与架构判决(用户 2026-08-09 定) Choose the fork root by which candidate's shortfalls are recoverable, not by headline score
fork-root-recoverable-shortfallWhen picking a checkpoint to fork from, rank candidates by whether their weaknesses are trainable-back, not by current headline metrics; prefer the candidate whose deficits the upcoming training directly pays for.
Symptom
Multiple candidate checkpoints for the omni-command ladder root, each best at something different: fric-3000 had the best tracking precision (vx 88-91%) and hardened plant robustness; s1e-500 had lower precision (vx 82%) but was the only candidate that could still walk backward.
Context
Root selection ran as a data probe, not a preference vote: 6 candidates x 8 out-of-distribution omni commands x 20 seeds = 960 cells (probe_omni_0808.json). s1e-500 @pw1.0 survived 20/20 in all eight conditions including backward at 67% tracking; the deep-trained fric lineage scored backward 0-3/20 despite better forward precision.
Change
Decision criterion made explicit: list what each candidate exclusively wins at, then ask which of those wins the loser could train back. fric-3000's exclusive wins (precision, plant robustness) are both retrainable - precision is directly optimized by the reward, plant hardening is a planned later pass. s1e-500's exclusive wins (backward plasticity 20/20 vs 2/20, disturbance margin 159/160 vs 125/160, push chirality symmetry 40/40 vs 17/40) had all been shown unrecoverable - push-level rungs failed twice, chirality never recovered even with mirror augmentation on. Root = s1e-500.
Outcome
s1e-500 carried the whole C ladder; its backward skill was preserved through C2/C4 gates (regress budget <=2/20 enforced), and the final C4 product passed a 260-cell battery at 20/20 everywhere.
Mechanism
Training can re-earn anything the objective directly pays for, but capabilities that earlier training destroyed and never restored (plasticity, symmetry, robustness margins) are empirically one-way doors. The information-bearing comparison is therefore recoverability of each candidate's deficit, which the team stated as "独占项的可恢复性正好相反 —— 这就是判据" (the exclusive items' recoverability is exactly opposite - that is the criterion).
Applies when
- selecting a resume/fork root among several checkpoints
- one candidate is more precise but another retains a skill the rest lost
- planning a task-extension ladder from an existing lineage
“fric-3000 赢在精度（vx 88~91%…）与 plant 鲁棒性 → 两样都训得回来…；s1e-500 赢在可塑性（C1 20/20 vs 2/20）、抗扰余量（159/160 vs 125/160）、手性对称（推 ±6 N·s 40/40 vs 17/40）→ 三样都训不回来 … 独占项的可恢复性正好相反 —— 这就是判据。”
train/C_LADDER_RUN.md § 0. 为什么根是 s1e-500（数据，不是偏好） 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 The run policy never left the ground and fell in the second simulator from the frontal plane - its DR (gains and latency only) covered the actuator axis, not the frontal-plane contact and inertia disturbances the doubled stride amplified; "is DR on" is the wrong question
thin-dr-judged-by-channel-coverageJudge a DR recipe by whether its randomized terms cover the channel where the skill can lose stability, not by whether DR is enabled; when a new skill lengthens single support or enlarges motion in one plane, add disturbances in the plane it destabilizes before training.
Symptom
run R1 (6,000 iterations, 78 min): no flight phase ever appeared, and every one of 13 checkpoints failed the eight-gate MuJoCo smoke. In Isaac: zero terminations in 6,000 iterations, 4.2 deg tilt. In MuJoCo at delay 2: 1/6 survived, falls within 1.9-6.2 s at 50.8-58.7 deg, the most saturated joints all roll joints.
Context
The run contract doubled sagittal travel (knee action scale 0.9, knee swing peak 1.14 rad) with a 0.60 s period and 0.40 duty - long single support - while roll/yaw scales were deliberately left at 0.5. DR copied the s1e recipe: kp/kd (0.9, 1.1) and latency on; mass, COM, joint friction and push all off; ground friction pinned at (1.0, 1.0). Flight was read two independent ways: Isaac's per-foot contact reward stayed 0.845-0.857, never above 0.87 - the arithmetic ceiling of a gait with zero flight - and 30 of 36 MuJoCo seeds had flight fraction exactly 0 (the nonzero six were all tumbling falls). Foot lift itself worked (46-59 mm against a 50 mm design point): the walk-era "not enough travel" failure did not recur.
Change
Verdict FAIL, with the pre-registered first knob (exploration noise 1.0 -> 1.2) explicitly rejected as aimed at a different axis. The lesson was generalized and applied at the next line's design review: the one-leg spec made push, body mass, base COM and friction DR mandatory for its permanent single support and banned the thin recipe.
Outcome
The run line did not continue past R1 in the sources. The one-leg V0 with the wider DR passed its friction-variant gate (mu 0.4 and 1.2) inside a 40/40 acceptance.
Mechanism
Randomizing gains and latency covers the actuator's axis; a skill whose failure lives in frontal-plane contact and inertia needs randomization on that channel (push, mass, COM, friction), or the trainer's exact plant becomes the only one the policy can stand on - the omni_s1 transfer trap a second time, this time with DR switched on.
Applies when
- a policy is flawless in the trainer and falls immediately in a second simulator
- reusing a DR recipe from a skill with a different support pattern
- failures concentrate on one axis (roll, yaw) the DR does not touch
“**机理**: 矢状面行程翻倍 (膝摆动峰 1.14 rad) + T 0.60 + duty 0.40 的长单支撑, 把额状面扰动放大了一个量级; 而 roll/yaw 通道按 §3 **刻意没有放大** (仍 0.5), DR 又是 s1e 复刻的薄配方 (mass/COM/关节摩擦/push **四关全关**, 地面摩擦钉死 (1.0, 1.0))。 … 说明**薄 DR 的判据不能只看"有没有开 DR"**, 要看**开的那几项 是否覆盖失稳所在的通道** —— kp/kd 与延迟是执行器轴向的, 对额状面接触/惯性 扰动零覆盖。 … 0.87 正是「零腾空的走路步态」的天花板算术”
git:Lucen V2@origin/run-line:train/README.md § run R1 FAIL (2026-08-09, run 21-30-30_run_r1): 腾空零, 但病根在额状面不在探索