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
150 cards matching “video-as-acceptance-record”.
A time gate that had cured one lineage's rushing made the from-scratch lineage trade away its stance width twice (0.364 -> 0.235 m, 0.355 -> 0.251 m) - its disease was absent there, so the fix was retired and the pre-gate checkpoint shipped
time-gate-vs-wide-stance-retire-the-fixCarry a fix into a new lineage only if its disease is present there; a mechanism that cured one lineage can be net negative in another, and when doubling a term's weight recovers almost nothing, treat the two objectives as structurally in conflict and remove the one whose purpose is gone.
Symptom
V3.1's phase 2 (the 3 s zero gate on standing income, continued from P1b) kept 100% success on every friction level and slowed the get-up, but the lateral stance drifted 0.364 -> 0.235 m and hip yaw crept to 57 deg against its 60 deg limit. With the width band's weight doubled (P2c, after P1c) it drifted again, 0.355 -> 0.251 m, below the pre-registered 0.30 m failure line.
Context
The zero gate had been introduced in V2.5/V2.5b to slow the old lineage's get-up. In V3.1 the rushing was already absent: P1c got up in 0.90-1.06 s with a worst torque ratio of 73.3%, better than the stamped v2_6c, because the full beta curriculum, second-difference smoothing and pull curriculum had cured the violence inside training.
Change
Recorded as a candidate law with two data points - the zero gate and a wide stance are mutually exclusive here - and the zero gate was removed from the V3.1 recipe. P1c (the pre-gate checkpoint) went through the full stamp-level acceptance instead.
Outcome
P1c passed everything: all six criteria, lateral stance 0.355 m, foot tilt P75 2.0 deg, mu {1.0, 0.8, 0.6, 0.4} x 10 seeds all 100%. recovery_v3_1p1c.onnx was stamped and pushed to the robot channel.
Mechanism
The zero gate moves the income toward "stay stable until the end", and under low-friction DR a wide stance has a slip tail, so survival outbids the width band; doubling the band's price bought back only 0.016 m - an auction that does not converge signals structural conflict, not an under-priced term.
Applies when
- porting reward mechanisms from an old lineage into a fresh recipe
- a width, margin or posture metric erodes during a late training phase
- a weight increase produces a negligible change in its target
“**定律候选(二实证):归零门 × 宽站互斥**。 … 加价翻倍只挽回 0.016,竞拍不收敛)。 … **归零门是 v2_5 血统的历史包袱,对 V3.1 配方是净负资产,P2 阶段除名 —— P1c 即终点形态**。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §49 终验(2026-08-14) A joint-velocity penalty meant to slow the get-up cut joint speed 16% and left the get-up time unchanged - the knob never moved the variable, so the idea it was meant to test stayed untested
dof-vel-penalty-is-not-a-pacing-knobBefore reading a result as a test of an idea, check that the knob actually moved the independent variable; velocity regularizers smooth a schedule they do not set, and a schedule driven by per-step task income moves only when that income's time structure does.
Symptom
The get-up took 0.6-0.9 s in Isaac with large torque demand; the user proposed getting up more slowly so less torque would be needed.
Context
The idea had support in the accounts: the acceptance bound is an upper bound of 5 s (5-8x margin), the quasi-static squat path peaks at 25% of the limits, and rolling over needs no momentum. R3.2 raised dof_vel from -1e-3 to -5e-3 as the single variable.
Change
dof_vel -1e-3 -> -5e-3 (child-run from R3.1).
Outcome
Get-up medians moved +0.02-0.04 s (noise); raw joint velocity -16%; torque demand median got worse (hip_pitch 46-48% -> 63-67%) as the new term competed with torque_headroom on the same joints; MuJoCo 98 -> 96%. Verdict FAIL on the knob, not on the idea, and the rung was not adopted. When pace was later attacked through the income's time structure (V2.5/V2.5b), the MuJoCo get-up moved into the 3.5-4.5 s design band.
Mechanism
The pace was set by base_height_progress paying for every step spent high (stand earlier, earn more); a velocity regularizer only smooths motion along the same schedule and does not change when the robot stands up.
Applies when
- trying to make a skill slower or gentler with smoothness penalties
- an experiment's primary metric did not move and a verdict is being written
- two penalties act on the same joints
“**关键判读:`dof_vel` 罚只把关节速度压了 16%,而起身用时一点没变。** 也就是说**这一级根本没有把"慢下来"这个自变量推动起来** —— 所以它**不构成对 用户假说的检验** … 起身节奏由 `base_height_progress` 的逐步计酬决定(早站起来就多 拿),速度正则只在同一条时间轨迹上把动作抹匀,不改变何时站起来。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §25 R3.2(dof_vel −1e-3→−5e-3,慢一点起身) A single run's drift direction may be a limit cycle, not a policy bias - check the sign distribution across seeds
multiseed-sign-test-for-driftDistinguish "bias" from "broken symmetry limit cycle" by the sign distribution over many seeds; report drift as (mean, sign split), and never compare single-run drift magnitudes across versions.
Symptom
Net yaw over 15 s appeared to worsen from -41 deg (v2) to -84 deg (v4), inviting the conclusion that the new version drifted more.
Context
The Isaac-side view across 32 environments told a different story: per-env yaw was mixed-sign (20 negative / 12 positive) with mean ~0 - the drift is a limit cycle whose direction depends on initial conditions, not a systematic policy bias. The single MuJoCo run had sampled one draw from that distribution, so its magnitude could not be compared across versions as if it were a property.
Change
Evaluation rule: before classifying drift as systematic, run multiple seeds and examine the sign distribution; single-trajectory drift magnitudes are samples, not properties.
Outcome
The v2-vs-v4 drift "regression" was reclassified as not-established; later drift work (hip_roll l+r bias) used cross-policy, cross-seed evidence instead.
Mechanism
Symmetric dynamical systems can settle into either of two mirrored limit cycles; the selected cycle is decided by noise and initial state. A statistic whose sign is initial-condition-dependent has no meaning as a single sample - only its distribution does.
Applies when
- comparing heading drift or lateral drift across policy versions
- a symmetric-looking behavior shows a consistent direction in one run
- deciding whether to fix "drift" in reward or calibration
“偏航反而变差(−41° → −84°):注意 Isaac 侧 32 env 的逐 env 偏航是正负混合(20/12)、均值 ≈0,说明这是极限环性质(方向随初值)而非策略偏置 —— MuJoCo 单次跑测到的是分布里的一个样本,不能当作系统性偏差。要判断需多种子统计。”
train/WALK_DIAGNOSIS.md § walk_v4 独立验收 读法 (偏航) Borrow reward values from other robots as ratios (to tracking weight, to leg length) - never as absolute numbers
transfer-ratios-not-absolutesWhen importing any numeric from another robot's config or paper, identify its natural normalizer (tracking weight, leg length, sqrt(g*L), body mass) and transfer the dimensionless ratio; sanity-check against a same-scale robot when one exists.
Symptom
Published configs offered tempting absolute values (swing height target 0.06-0.08 m, weight -20) that would have been wrong for a robot with half the leg length and a different tracking weight.
Context
The cross-check normalized before transferring: G1's feet_swing_height weight -20 against tracking +1.0 is a 20x ratio, so with local tracking at 1.5 the equivalent is -30, not -20. G1's 0.06 m target on a ~0.70 m leg scales to ~28 mm on the local 0.325 m leg (Humanoid-Gym converts to ~23 mm), confirming the locally chosen 0.03 m and explicitly rejecting copying 0.05-0.08 absolutes. A same-class robot (Menlo's 16 kg) was used to sanity-check feet_air_time (+0.5 vs the local 2.0, flagged over-high). A nondimensional check of the same kind later validated the sidewalk speed target (v/sqrt(gL) = 0.081 vs hardware-verified 0.101 - 0.104 - inside the envelope, conservative).
Change
All borrowed values converted through ratios (weight/tracking-weight, height/leg-length, dimensionless speed) before entering the config.
Outcome
The scaled values worked (0.03 m target matched both the scaling law and measured 22-23 mm baseline); no cross-robot absolute was ever copied raw.
Mechanism
Reward economies are scale-relative (only ratios to the tracking term matter to the optimum) and kinematic quantities are morphology-relative (clearance scales with leg length, speed with sqrt(g*L)); absolutes encode the source robot's scale, ratios encode the design intent.
Applies when
- copying reward weights/targets from open-source configs or papers
- setting clearance heights, speed targets, or impact thresholds
- comparing your weights to published tables
“G1 的 feet_swing_height 是 tracking 的 20 倍(−20 vs +1.0)。我们 tracking 是 1.5,按同比例应为 −30 … G1 目标 0.06 m / 腿长 ~0.70 m,换算到我们 0.325 m 腿长约 28 mm;Humanoid-Gym 换算约 23 mm。故 target 取 0.03 m 是对的 … 不必抄 0.05~0.08 的绝对值。”
train/WALK_DIAGNOSIS.md § 修正 ②(权重放大) / 修正 ③(目标高度按腿长缩放) The standing-pose reward had been pulling toward the narrow stance the whole line was fighting - a zero-training kinematic audit of the target vector found it, after first auditing the wrong nominal
pose-target-geometric-auditBefore training on a pose target, audit it with forward kinematics - is it geometrically consistent (feet flat, intended stance, intended width) and is it the frame you think it is (action nominal vs standing default)? A posture term's target may itself be the attractor you are fighting.
Symptom
Several rungs aimed at widening the stance failed; the stance stayed narrow as if something kept pulling it back.
Context
The audit (MuJoCo forward kinematics, no training): every 5 deg of hip roll widens the stance ~5.5 cm (0.271 m at 5 deg, 0.383 m at 15 deg); at 47 deg of hip yaw a wide stance cannot be flat-footed (residual foot tilt ~0.7 x hip roll), which explained the stalled rungs. The first report also said the stand_pose nominal (hip roll 25, knee 60) has a 63 deg residual foot tilt - but that was the action frame's nominal (the limit-midpoint squat), which stand_pose never used, despite a docstring warning not to mix them. stand_pose's real target was DEFAULT_JOINT_POS: the contract's all-zero pose, legs parallel, ~0.22 m apart.
Change
The disease statement was corrected in writing: the narrow stance was not an accidental by-product of proxy traps but the target stand_pose had been actively rewarding. A stored "narrow the stance" knife was marked toxic. V2.8 moved the target to a flat 15-deg stance (sigma 3 -> 1.5, flat_feet margin 5 -> 20 deg).
Outcome
V2.8 still failed in-lineage (stance unchanged, feet nearly overlapping, mu 0.4 transfer 2%) - see stance-decided-by-get-up-path - and the from-scratch V3.1 removed both roll joints from stand_pose and put width into a task-space term, which is what finally produced a 0.355 m flat stance. The 63 deg finding was kept as a warning: an action nominal used as a standing target would be a ready-made pit.
Mechanism
A posture term with a sharp kernel around the wrong target is an active attractor; every other term fighting it pays twice.
Applies when
- a posture keeps returning despite penalties against it
- a reward uses a default or nominal pose as its target
- the contract has more than one "nominal" (action frame vs standing pose)
“上文"stand_pose 的 nominal (hip25/knee60) 残倾 63°"**审计错了对象**:那是 **动作参考系 nominal**(限位中点蹲),stand_pose 从未指向它(函数 docstring 原文即警告"两者别混",还是混了 —— 记档)。 … **修正后的病根陈述:窄站距不是代理陷阱的意外副产物,而是 stand_pose 一直在主动奖励的目标本身**”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §44 勘误(实现时抓到):审计混了两个 nominal —— 真病根比误诊的更直白 Training the final recipe from scratch in one run - every mechanism the lineage had accumulated - produced 0% and a seated robot; the order in which the lineage acquired those mechanisms was part of why it worked
curriculum-history-is-part-of-the-productA recipe that ends a lineage is not a recipe for a from-scratch run: consolidate it as ordered curriculum phases matching how the lineage acquired its mechanisms, check that every curriculum criterion is reachable from the starting policy, and read the run's raw term values, not the total reward, before calling it green.
Symptom
V3.0 trained the lineage's whole final recipe from scratch in one 9,000 iteration run - full beta curriculum, prone-conditioned pull assist, friction DR, the 3 s zero gate on standing income, flat_feet - testing the proposition "the product is defined by its configuration, not by its training history". Training looked all green (reward 26.33, episode length 500, 100% time-outs).
Context
Read in raw units against v2_6c at the same weights, the green was a seated equilibrium: base_height 0.377 vs 0.640, stand_pose 0.205 vs 0.516, flat_feet 0.0000 (zero because it sits outside its height gate, not because the feet were flat). Acceptance: 0.0% in Isaac at the deployed authority, 0.0% on every MuJoCo friction level, and still 0.0% at the training-time authority (100% seated at 0.222 m, upright and still).
Change
The full stdout (316k lines) was read: the beta curriculum's criterion (standing share over 0.35) was met zero times, so beta never left the wide setting and the policy had no experience at the deployed authority; the pull curriculum was stuck on the same criterion. The lineage had escaped the seated basin with immediate income (V2.0-V2.2) and only then added the zero gate to cure rushing (V2.5); from scratch, the zero gate removed the early "stand fast, earn more" gradient needed to escape. Verdict "the curriculum history is part of the product", limited to n = 1. v2_6c stayed the product.
Outcome
V3.1 kept the order as explicit phases: P1 from scratch with immediate income (zero gate off) until the curricula advance, P2 adding the zero gate. P1b/P1c escaped the seated basin and passed; P2 was later judged net negative and dropped (time-gate-vs-wide-stance-retire-the-fix).
Mechanism
Mechanisms that refine a competent policy (time gates, tight authority) can delete the gradient a naive policy needs, and a curriculum whose advancement criterion the naive policy never meets freezes at its first level.
Conflicts
The spec limits the falsification to "this recipe + this curriculum criterion" (n = 1, no seed sweep, no criterion tuning). V3.1's phased run succeeding is consistent with the ordering reading but changed other terms too.
Applies when
- consolidating a long lineage of continuation fixes into one clean recipe
- a from-scratch run with all mechanisms enabled plateaus early
- curriculum state is not logged or never advances
“命题:产物由配置定义,而非训练史定义。 … 训练 log(完整 stdout 316k 行)`[beta_anchor]` 仅初始 1 行,**达标 0 次** … V2.0~V2.2 靠**即时计酬**爬出坐姿盆地(§33),站立巩固后 V2.5 才装归零门 治"过快"(§39)。**课程史是产品的一部分。**”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §42 V3.0 判决(2026-08-11):从零单 run 全机制 FAIL 于坐姿盆地 Oversized lateral COM randomization (+/-5 cm) deliberately forces leg spread
com-randomization-forces-leg-spreadDR ranges can be behavior-shaping tools, not just robustness padding: oversize a randomization axis to force a strategy the reward struggles to express - and expect a compensating behavior to appear as the cost.
Symptom
Feet drift toward the centerline and even collide; policy has no incentive to keep a lateral support base.
Context
COM randomization ranges were chosen asymmetrically by axis: lateral +/-5 cm ("比常规大，故意的" - larger than usual, on purpose), fore-aft +/-2 cm, vertical +/-2 cm. The oversized lateral range is not robustness padding but a behavioral forcing function. Lucen logged it as directly relevant to its own roll-channel / sideways leg-kick symptom.
Change
Set COM randomization to lateral +/-5 cm, fore-aft +/-2 cm, vertical +/-2 cm, with the lateral band intentionally oversized to make narrow stances fail during training.
Outcome
Effective at separating the feet on the reference robot; side effect - the base began swaying left-right, which then required a foot-centerline distance penalty (see reward-chain-foot-height-landing-spacing).
Mechanism
Randomizing COM laterally makes narrow-stance policies fall for some draws, so PPO discovers wide stances as the only strategy robust across the band - DR used as an implicit reward. The sway side effect appears because the policy hedges against unknown COM by active lateral correction.
Applies when
- feet too close / self-collision in a learned gait
- roll-axis instability suspected to come from narrow stance
- choosing COM or mass-offset DR ranges
“两脚太近甚至互撞 → 先试质心横向随机化 ±5 cm，逼迫策略把脚分开；有效但引发新问题——基座开始左右摇摆 … 横向 ±5 cm（比常规大，故意的，用来逼出分腿）/ 前后 ±2 cm / 垂直 ±2 cm”
Experience.md § 质心随机化范围 (lines 75, 84-86) The trainer read a stale USD after the URDF mass update - regenerate derived assets and gate on an automated equality instrument
derived-asset-staleness-checkFor every derived plant artifact (USD from URDF, generated value files), pair the generation step with an automated source-vs-derived equality instrument, prove the instrument can fail, gate training on its PASS, and re-run physical audits after every regeneration.
Symptom
Measured link masses had been committed to URDF/MJCF (total 9.58 -> 9.792 kg, weighed values), but Isaac reads the derived USD asset - which still carried the old masses: a silent 2.2% mass fork between the training plant and the evaluation plant.
Context
The v12 checklist made USD regeneration a hard precondition ("硬性 前置") and, crucially, backed it with an instrument: check_usd_mass.py compares USD vs URDF per-link mass AND inertia trace, validated by showing it FAILs the stale asset naming 9 offending links, then PASSes after re-conversion (13/13 links consistent, total 9.7920). The self-collision filter audit was re-run after the regeneration (three poses, 0.00 N) because a regenerated asset invalidates physical audits done on the old one. This milestone was also where the three plants first aligned: "三边 plant 首次对齐(armature+摩擦+ 实称质量)就在这一代".
Change
convert_urdf re-run on the training machine, regenerated asset committed, check_usd_mass.py PASS required as an acceptance gate for the generation; dependent audits repeated post-regeneration.
Outcome
The 2.2% plant fork was closed before it could distort a generation's acceptance numbers; the staleness class of bug now has a permanent detector instead of a memory.
Mechanism
Source-of-truth edits do not propagate to derived binary assets by themselves; any consumer reading the derivative silently trains or evaluates on the old plant. An automated equality check between source and derivative - proven able to fail - turns an invisible staleness into a red gate, and regeneration invalidates every audit performed on the old artifact.
Applies when
- editing masses/inertia/geometry in URDF or MJCF sources
- a trainer or evaluator consumes converted/derived assets
- plant numbers differ between simulators for no visible reason
“25ba997 把连杆质量更新为实称值(总重 9.58→9.792 kg,URDF/MJCF 已改),但 Isaac 读的是 train/assets/laika_v2.usd —— 仍是旧质量。… 否则 Isaac(9.58)与 MuJoCo(9.79)质量分叉 2.2%,v12 验收数字失真。验收门:python tools/check_usd_mass.py 必须 PASS … 对旧资产实测 FAIL/9 连杆点名,仪器已验证”
train/WALK_V12_SPEC.md § 7. 核查单 (⚠️ 先重转 USD) 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 Fix the task first, harden the plant second - DR budget spent on a dying task is wasted
task-shaping-before-plant-hardeningFreeze the task/command distribution before spending DR budget on plant robustness; if the task will still change, schedule plant hardening as a final pass and book the interim robustness gap explicitly.
Symptom
Tempting default ordering was to keep the plant-hardened (S2) lineage and teach it new commands; but the S2 plant adaptation had been earned on the straight-walk task, and the new omni tasks (sidewalk, in-place turn) use completely different contact patterns.
Context
The team had direct evidence that DR robustness is a budget that gets reallocated when the data distribution changes ("push/μ 两轮已实证 DR 预算有限且会被重分配") - robustness trained under one task/command distribution does not persist when training continues under another.
Change
Ladder order set to: first C (task shaping - add command modes until the task family is final), then a second S2 pass (plant hardening) on the C product. The plant-robustness gap this creates mid-ladder is accepted and booked explicitly ("此处不欠账" - the debt is assigned to the second S2 pass, not denied).
Outcome
The first S2 pass was not wasted: its laws (kd bandwidth <-> low mu, push need not be trained, ground mu need not be trained, bistability) let the second pass drop from five rungs to three. The C ladder itself ran on the softer plant band without incident.
Mechanism
DR robustness is carried by the policy's visited-state distribution; changing the task changes that distribution, so robustness bought under the old task partially dissolves. Hardening before the task is final means paying for robustness on states that will no longer be visited - "给一个即将不存在的任务花预算" (spending budget on a soon-to-not-exist task).
Applies when
- deciding ordering between skill/command expansion and DR hardening
- a hardened lineage is proposed as the root for a task change
- robustness regressions appear after adding new command modes
“S2 的 plant 适应是为直行步态调的，C4 侧走/C3 原地转是完全不同的接触模式，先硬化再改任务 = 给一个即将不存在的任务花预算（push/μ 两轮已实证 DR 预算有限且会被重分配）。故顺序改为 先 C（任务定型）→ 再 S2（plant 硬化）。”
train/C_LADDER_RUN.md § 0. 决策逻辑 = 短板可不可恢复 (末段) Freeze the deployment contract, stamp every export, and let an automated checker catch wiring bugs
contract-freeze-and-checkerFreeze and fingerprint the policy I/O contract; ship contract changes as new versioned profiles that leave old artifacts bit-identical; and extend the automated contract checker with every pipeline change, forcing the new path to execute in the check.
Symptom
Contract-level changes (observation layout, action pipeline) are where silent sim/real divergence is born; two real wiring bugs appeared the one time the action pipeline was extended.
Context
The 215-dim observation contract was frozen ("纪元 3，三机 digest" - an era number plus a digest agreed across three machines); proposals that would break it (e.g. a GRU memory) were rejected on contract grounds. Every exported ONNX is stamped and verified with a manifest (onnx_manifest --stamp / --verify), and deployment refuses mismatched combinations. When C4 added the lateral feed-forward, it went in as a NEW profile (omni_ff) leaving the existing omni profile's behavior bit-identical; the checker (check_contract) was extended to force the feed-forward path to actually execute (cmd_vy=0.13) and promptly caught two genuine bugs: (1) re-clamping with soft_joint_pos_limits after the feed-forward (0.23 rad deviation) instead of reusing the parent's clip; (2) indexing processed actions by asset.joint_names instead of the action term's own contract-ordered _joint_names, which landed the feed-forward on the wrong joints (l_hip_yaw / r_ankle_pitch).
Change
Contract discipline as implemented: frozen dims + digest; manifest stamping and refusal; contract changes only via new versioned profiles; checker updated in the same commit as any pipeline change, with inputs chosen so new code paths are exercised.
Outcome
Both wiring bugs caught before any training or deployment ("两个都是 check_contract 当场抓出来的 —— 这次它值回票价"); old deployments provably unaffected by the new profile.
Mechanism
The contract is the only interface the policy and robot share; freezing plus fingerprinting makes divergence detectable, and an executable checker turns "the contract holds" from a belief into a test - but only if its inputs actually drive the new code path.
Applies when
- modifying the action or observation pipeline of a deployed policy
- exporting policies for hardware
- proposals that would change observation dims or history structure
“契约校验抓到的两个真错误（记账，别再犯）：1. 前馈后误用 soft_joint_pos_limits（URDF 限位 ×0.9）重钳 → 0.23 rad 偏差 … 2. 用 asset.joint_names 索引 _processed_actions → 前馈落到 l_hip_yaw/r_ankle_pitch 上 … 两个都是 check_contract 当场抓出来的 —— 这次它值回票价。”
train/C_LADDER_RUN.md § 3j. 契约级改动 / 契约校验抓到的两个真错误 The smoke watcher panicked at the wrong operating point and its score goes blind when gates saturate - treat it as a survival sentinel, not a judge
smoke-watcher-operating-pointConfigure every automated evaluator at the lineage's declared deployment operating point, give it graded metrics that cannot saturate, and until then scope its authority to catastrophe-detection - never let a mis-configured watcher stop or rank a rung on its own.
Symptom
Two watcher misfires in one ladder: (1) during the friction rung the watcher (evaluating at kd 1.0) reported panic-level 1/3 survival from iter 3300 - falsified by the official kd 1.2 scan, because the lineage's design operating point was kd 1.2 and the watcher lacked the --kd-scale passthrough; (2) during the PD rung the watcher's early-stop score froze at iter 1050 despite ongoing drift improvements, because with all eight gates passing (constant 0/3 failures) the score has no gradient left - "八门全过恒 0/3 时 score 对漂移改善盲, s1f 课文三现".
Context
Both are the same category: the in-training smoke loop is an instrument with its own configuration (operating point, score design), and its verdicts are only as aligned as that configuration. The booked doctrine: "冒烟只当存活哨兵" - until the watcher evaluates at the deployment operating point with graded metrics, its role is detecting catastrophes, not ranking checkpoints; ranking belongs to the full battery at the design operating point (and the watcher's scoring was separately patched to weight survival 3x so recoveries during hard phases are not early-stopped away).
Change
Watcher debt booked (--kd-scale passthrough); score saturation acknowledged with graded columns planned; selection authority kept with 20-seed batteries at the declared operating point.
Outcome
A false panic did not abort a rung that was actually passing at its design point; a frozen score did not hide real drift gains; the instrument's authority was scoped to what its configuration can actually see.
Mechanism
An evaluator is itself configured (gain profile, delay, metrics); evaluating a policy away from its design operating point measures a counterfactual robot, and bounded scores saturate once binary gates pass, losing all sensitivity. Instruments need the same operating-point discipline as deployments and graded outputs to retain gradient.
Applies when
- an automated smoke loop contradicts the official battery
- early-stop scores freeze while graded metrics still improve
- lineages with non-default deployment gain/delay profiles
“watcher (kd1.0 口径) 3300 起 1/3 恐慌被 kd1.2 正式扫描证伪为考纲外假象 —— 工作点评测口径教训: watch_ckpt 缺 --kd-scale 透传 (待补), 冒烟只当存活哨兵。… watcher score 饱和误停 @1050(八门全过恒 0/3 时 score 对漂移改善盲, s1f 课文三现)”
train/README.md § omni_s2e_fric (watcher 恐慌被证伪) / omni_s2e_pd (500 臂) A training-side rate limit anchored on the last commanded target is an integrator inside the balance loop - two unrelated lineages converged to the same 34-43% re-fall rate, a soft penalty could not fix it, and the bandwidth arithmetic said safety and standing could not coexist
slew-anchor-is-an-integratorIf you constrain actions in training, anchor the constraint on the measured state, not on the previous command - a limiter with memory adds lag inside the balance loop; and when two different lineages converge to the same failure rate, treat the cause as structural and stop adding soft penalties.
Symptom
With the rate limit moved into training (V1), policies either could not stand up or stood up and kept falling again: the stand oscillated, fell and climbed back, 34-43% of the time.
Context
V1.0-B (user decision: explore new postures from scratch, hard constraint in training): target <- prev + clip(target - prev, +/-S*dt) at the TIGHT rates, anchored on the last issued target like the bridge. Four runs: v1_0 from scratch 0.8% - every category righted under the limit, then knelt (the limit also damped the exploration that had escaped the seated basin in V0); v1_0c continued from R3.1 99.8% get-up but 36-43% re-fall and knee jitter 0.604 rad/s; v1_0p with a pull-assist curriculum (56 N -> 0, fully withdrawn) 39.1% unassisted, re-fall 34-42%; v1_0w with a windup-gap penalty 25.4%, re-fall 18-43%. Removing the limiter from v1_0p gave 0.0%: the policy had co-adapted with it.
Change
The rate-limit route was declared dead after four runs and the line was re-rooted on a beta-anchored action space (V2, user approval required).
Outcome
V2.0's first acceptance at full authority already showed re-falls of 0-1% (V1: 34-43%), the structural bet paying off before any tuning.
Mechanism
Anchored on the previous command, a saturated policy becomes a rate controller - one more integrator in the loop - and active balance through that lag oscillates, while a kneeling sit needs no active control and is stable. The arithmetic: kp 30 needs 0.4 rad of error for 12 N*m; at 4 rad/s that takes 0.1 s, half the pendulum time constant sqrt(0.38/9.8) ~ 0.2 s; keeping standing bandwidth needs S of at least ~8 rad/s, within 20% of the 10 rad/s velocity limit - no bound at all. Anchoring on the measured angle (q + beta*a) makes the full kp*beta authority available in one step, with no memory, and caps the impact at the same time.
Applies when
- adding rate limits, slew limits or target filters to a policy's action path
- a policy stands but oscillates and re-falls after a constraint was added
- different lineages or curricula land on the same failure signature
“v1_0p(拉力课程):会站(prone 79.9%),再摔 34~42% —— **两条完全不同 血统、不同学习路径,收敛到同一失败率**。 … 动作饱和时它退化为 速率控制 = 环内多一个积分器;主动站姿平衡穿过该滞后必振荡(v1_0 的跪坐不需 主动控制,所以稳)。对照:**HoST 的 β 锚在当前实测 q,无记忆、无积分器**”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §31 结构病定案:slew 的目标锚 = 控制环里的积分器 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) The median of a bimodal metric lands in the empty gap - check the distribution, and never judge swing on 3 seeds
median-hides-bimodal-distributionBefore quoting a median or mean, look at the distribution; report suspected-bimodal metrics as mode share plus per-mode ranges, use small-seed smoke runs only to screen trends, and size the seed count for decisions by the share resolution you need (here: 20).
Symptom
Years of "high swing variance" and undecidable 3-seed swing readings turned out to be one fact: the metric was bimodal all along - "历代 swing 高方差与 3-seed 不可判由此定性:一直在测双稳态系统" - and every median reported from it (e.g. 12.1 mm) described a value no seed ever produced.
Context
Concrete instances: s2e_pd-1400's 20 seeds split 2.6-4.9 mm vs 19.3-24.0 mm with zero seeds between; s1e-500 read 3.9 mm on seeds 0-2 but 23.1 mm median over 20 seeds; a 3-seed reading of 19.3 was logged as "double-peak optimism, lesson recurrence #4". The selection re-audit codified the sampling rule: "3-seed 的 swing 读数不可判点, 只能筛带,选点必须 20-seed" - 3 seeds may screen a band, only 20 seeds may pick a point.
Change
Swing (and any suspected multi-modal metric) reported as mode shares plus per-mode ranges instead of a bare median; 3-seed smoke numbers demoted to band-screening; all shipping/selection decisions moved to 20-seed batteries.
Outcome
The "swing debt" bookkeeping was reinterpreted as basin probability (see swing-bistability-damping-switch), and checkpoint selection stopped being whipsawed by which basin the first three seeds happened to fall into.
Mechanism
Central-tendency statistics presuppose unimodality; on a bimodal distribution the median tracks the mode SHARE, not any achievable behavior, and small samples alias the share entirely. Mode-aware reporting (share + per-mode stats) is the only faithful summary, and the needed sample size is set by the share resolution required.
Applies when
- a quality metric shows chronic high variance across seeds
- 3-seed smoke readings contradict 20-seed batteries
- reporting swing height, clearance, or any basin-prone metric
“中位数落在空档里,「swing 债 −11mm」实为「50% 概率掉进拖地吸引子」。历代 swing 高方差与 3-seed 不可判由此定性:一直在测双稳态系统。… swing 跨 seed 双峰 (500 在 seed0~2 只读 3.9mm, 20-seed 中位 23.1) —— 3-seed 的 swing 读数不可判点, 只能筛带, 选点必须 20-seed。”
train/README.md § swing 双稳态定性 / s1e 选点重审 Two simulators disagreed 1.8x on one policy's torque demand - four explanations were eliminated with numbers, the surviving suspect was never tested, and neither reading was allowed to cancel the other
torque-disagreement-between-simulators-unresolvedWhen two simulators disagree on a safety-relevant quantity, eliminate the measurement explanations one at a time with numbers, name the survivor as a hypothesis, keep the pessimistic reading binding, and run the direct test (replay one action sequence open-loop through both plants) before the quantity is used to pass a gate for hardware.
Symptom
For R3.0, Isaac read hip_pitch torque demand at 75-80% of the limit (PASS); MuJoCo read the same policy at 148-152% (1.5x over the limit).
Context
Candidates were eliminated one by one: PD gains (identical, RS06 kp 30 / kd 1.5), the settle window (both skip the first 0.5 s), the statistic (worse-of-two-legs vs per-joint - explains ~15%), self-collision (the no-contact subset still read 152%), sampling (500 Hz peak vs 50 Hz sample - ~9%). About 1.8x remained. The prime suspect - Isaac's implicit actuator (PD solved inside the PhysX integration, chosen to mimic kHz firmware PD) against MuJoCo's 500 Hz explicit PD - was named but untested.
Change
The Isaac PASS was recorded as valid for the Isaac plant only; the prescribed decider was an open-loop replay of one action sequence through both plants, compared step by step, needing no training. It was upgraded to a precondition of R3.1.
Outcome
R3.1 ran in parallel with, not after, the replay, and the replay never appears as done. By §40 it was "the oldest open account" on the line, to be fed by real-robot logs - which the spec also never records. Meanwhile the 50 Hz sample under-read the peak by 34% as smoothing narrowed the spikes, so Isaac-side readings grew more optimistic exactly when the comparison mattered more.
Mechanism
Each measurement artifact explained a slice of the gap; what remained is a plant difference, and an untested plant hypothesis cannot license discarding the pessimistic simulator on a safety-relevant quantity.
Conflicts
The spec prescribes the open-loop replay (§23), upgrades it to an R3.1 precondition, then records that R3.1 ran in parallel without it (§24), and lists it as the oldest open account before hardware (§40); nothing through §50 (2026-08-14) records a result. Every later "torque gate PASS" on this line is an Isaac-plant reading plus a MuJoCo check, never a reconciled one.
Applies when
- Isaac and MuJoCo (or sim and hardware) report different torques, contacts or slips
- implicit vs explicit actuator models are in play
- a gate passes on one simulator only
“**同一个策略,一侧判 PASS 一侧判超限 1.5 倍。** … 口径项全部扣掉后仍剩 **~1.8×** 没有解释。 … 但**这条没有验证,不能拿它当结论去抵消 MuJoCo 的读数**。 … (做法:拿同一条 动作序列在两边开环回放,逐拍比 τ —— 不需要重训)”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §23 MuJoCo 复核门:成功率更稳,但 τ 与 Isaac 差 ~1.8× 没收敛 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) Prove a new penalty actually fires - two ways a clearance term silently did nothing
inert-reward-term-auditBefore training with a new reward term, log its realized per-step value under the current policy and confirm it is nonzero where intended - check coordinate zero-points against FK and check who occupies the term's gate; and never weaken the term that creates the states your new term needs.
Symptom
A newly designed swing-height clearance penalty could have trained as a no-op twice over, and the companion advice to lower feet_air_time actively backfired when tried.
Context
Instance 1 (zero-point offset): the proposed code used body_pos_w of the foot link, but that is the ankle_roll_link frame origin, which sits 0.0585 m above the ground even with the foot flat on it - so (0.03 - 0.0585) is always negative and the penalty is永远 0; the 0.0585 offset must be subtracted (verified identical in MuJoCo FK and Isaac). Instance 2 (gate occupancy): the clearance penalty fires only in swing phase; a dragging policy keeps both feet in contact, so the penalty is constantly 0 for exactly the policy it was meant to fix - and worse, any slight lift immediately incurs it, a reverse threshold. Lowering feet_air_time to 0.5 on that advice measurably collapsed air time to 0.0002 (below v3). Corrected understanding: "clearance 是把已有的摆动相抬高, 造出摆动相仍要靠 air_time" - air_time creates the swing phase, clearance raises it.
Change
Fixed the height zero-point; kept feet_air_time as the swing-phase creator with clearance layered on top; both errors documented as corrections to the team's own earlier advice.
Outcome
With both fixed, swing height rose from 22-23 mm (v2) to 29 mm (v5) to 34 mm (v6); the inert-term failure class entered the standing checklist.
Mechanism
A penalty's gradient exists only where its gate is occupied and its argument crosses its threshold; frame offsets shift the threshold out of reach, and phase gates can have zero occupancy under exactly the policy being treated. Terms interact as an ecology - one term must create the states in which another can act.
Applies when
- adding any gated or thresholded penalty (clearance, impact, slip)
- a new term produces no behavioral change at any weight
- body-frame positions are used in reward code
“body_pos_w 是 ankle_roll_link 坐标系原点,平放触地时仍高出地面 0.0585 m。… (0.03 − 0.0585) 恒为负 → 惩罚永远是 0 … clearance 惩罚只在摆动相生效,拖地时两脚始终触地 → 惩罚恒 0;而一旦轻微抬脚就立刻扣分,对正在拖地的策略是反向门槛。… 正确认识:clearance 是"把已有的摆动相抬高",造出摆动相仍要靠 air_time。”
train/WALK_DIAGNOSIS.md § walk_v4 独立验收 — 本文档给的两处代码/建议是错的 Three hardware accounts locked the run design point - and the knee's real speed ceiling is tau_limit/kd, not the firmware limit
feasibility-accounts-lock-design-pointBefore opening a dynamic-gait training line, compute the full account set - tau_limit/kd effective speed ceilings, joint ROM under the intended reference geometry, and thermal RMS at the duty cycle - and let the accounts lock the design point; move only to pre-registered in-table alternates, re-running the accounts first.
Symptom
The run line was believed to require a firmware raise of the RS06 speed limit (10 rad/s) as a hard precondition, and the feasibility script's motor-envelope scan had marked 80/100 mm foot-lift cells "physically feasible".
Context
Three added accounts re-decided everything. (1) Damping tax: in MIT mode tau = kp*(q_des-q) - kd*qd, so sustained rotation is capped at tau_limit/kd = 12/1.5 = 8 rad/s - below the firmware's 10; at peak speeds 6.7-7.9 rad/s the damping term alone eats 10.1-11.9 N*m (84-99% of the torque limit). "提固件 limit_spd 越不过这道税 —— 它是 kd 与限扭的比,不是固件旋钮." (2) Joint ROM: the feasibility script had checked motor envelopes but NOT joint range - the ankle-pitch ROM caps 1:2:1 leg-shortening lift at 62 mm (soft) / 77 mm (hard), so the 80/100 mm "feasible" cells were voided; also firmware-independent. (3) Ankle thermal: duty 0.40 puts ankle RMS at 87% of continuous rating (0.35 -> 93%); long-period big-stride cells hit both ankle torque peak and heat. Verdict: firmware raise DEQUEUED (50 mm design point needs knee 6.7-7.3 < the 8 rad/s effective ceiling < firmware 10); vel_limit stays 10 so sim == robot. The three accounts uniquely lock the design point - 50 mm lift / T 0.60 s / duty 0.40 - "三笔账 唯一锁定,不是调参空间", with pre-registered alternates allowed only inside the table and only after re-running the accounts.
Change
Design point frozen from accounts; hardware precondition reversed by arithmetic rather than by test; reference amplitude (0.84 rad = FK inverse of 50 mm) derived, per-joint action scales sized to the required travel (knee 0.9, hip_pitch 0.6, ankle deliberately NOT amplified - hard limit is adjacent).
Outcome
A firmware work item left the critical path; an infeasible region of the design space was closed before any training; the remaining risk (knee tracking lag from the damping tax) was pre-registered with its own criterion and in-table fallback (duty 0.35) - "这不是'奖励没调好', 是 plant 账".
Mechanism
PD actuators in MIT mode pay kd*velocity out of the same torque budget that tracks position, so the effective speed ceiling is a ratio of configuration constants, invisible to firmware settings; and feasibility is the intersection of ALL constraint families (torque envelope, joint ROM, thermal RMS) - a scan that omits one family certifies impossible cells.
Applies when
- planning running/jumping or any high-rate gait on PD actuators
- a firmware or hardware upgrade is assumed as a training precondition
- a feasibility scan covers motor limits but not ROM or heat
“膝的有效速度顶 = τ_limit/kd = 12/1.5 = 8 rad/s,不是固件的 10。… 提固件 limit_spd 越不过这道税 —— 它是 kd 与限扭的比,不是固件旋钮。… 可行性脚本只查了电机包络没查关节 ROM —— 其 80/100mm 的"物理可行"格作废。… 判决:RS06 提固件对 run v0 不是前置,出队”
train/RUN_V0_SPEC.md § 1. 硬件账判决 / 2. 步态设计点 A nonzero response with the same sign for + and - commands is bias, not ability
same-sign-response-is-yaw-biasBefore crediting any directional skill, test both command signs: response must flip sign with the command; a same-signed pair is a bias to subtract, not an ability to report.
Symptom
Root-selection probe showed nonzero wz "tracking percentages" on turn commands, tempting the read that candidates could partially turn.
Context
During C-ladder root selection, s1e-500's measured yaw rate was +0.084 rad/s for cmd +0.3 and +0.093 rad/s for cmd -0.3 - same sign both ways. The same check on the C2 baseline gave wz+0.20 -> -0.13 and wz-0.20 -> +0.12 (again same sign), while the alternative root s2e_pd-1400 gave +0.16 / -0.16 - opposite signs, i.e. a genuine 16% command response.
Change
Reading corrected and written into the execution sheet: percentages on directional commands are meaningless unless the +cmd and -cmd responses have opposite signs; all three candidates were re-classified as "cannot turn, cannot sidewalk - C2/C3/C4 learn from zero". Acceptance criteria thereafter required "tracking >=50% AND left/right opposite-signed".
Outcome
Prevented crediting turn/sidewalk ability that did not exist; the antisymmetry clause became a standing part of every turn and sidewalk PASS condition (C2, C4, C4-redo levels all carry "且左右反号").
Mechanism
A constant yaw (or lateral) bias projects onto any command's sign convention and shows up as fake fractional tracking; only sign-antisymmetry under command reversal distinguishes a feedback response to the command from an open-loop offset.
Applies when
- evaluating turn/sidewalk/any signed-command tracking percentages
- a candidate shows partial tracking on an axis it was never trained on
- writing PASS criteria for a new directional skill
“C2/C3 那些非零的 wz 百分比不是转向能力 —— 转向+ 与 转向− 的实测同号（s1e：cmd +0.3 → +0.084，cmd −0.3 → +0.093 rad/s），那是恒定偏航偏置。… 三个候选都不会转、都不会侧走。”
train/C_LADDER_RUN.md § 0. 读数纠正（重要，别引错） Changing the gait clock silently flipped a hardwired threshold's meaning - write derived constants as expressions
derived-constants-must-track-their-baseBefore changing any base parameter (clock, control rate, scale), enumerate every constant derived from it and every constant that must NOT change; convert derived literals into expressions of the base so the next change cannot silently flip a term's meaning.
Symptom
Slowing the clock 0.40 -> 0.50 s would have silently inverted the feet_air_time threshold's semantics: the 0.25 s threshold was hardwired, so at ct 0.40 the swing window (~0.20 s) sat below it (constant pressure to lengthen strides), while at ct 0.50 the window (~0.25 s) equals it - the term's meaning flips from "push longer" to "neutral" with no code error anywhere.
Context
The clock change audit walked every dependent quantity: most followed automatically (joint_pos_ref / clearance / contact_number cycle_time params, gait_phase observation, deploy/sim2sim/policy_io, export) - wiring confirmed, zero hand edits; the air_time threshold was the one hardwired constant, fixed by preserving the RATIO: 0.25 -> 0.3125 = 0.625 x ct, with the recommendation to commit it as the expression 0.625*ct "一劳永逸" (solved once and forever). The same audit also listed what must NOT follow the clock (50 Hz control rate, physics dt/decimation, 47-dim contract, action_latency absolute seconds, PD/torque limits) - the change's blast radius stated in both directions.
Change
feet_air_time threshold re-expressed as a fraction of cycle_time; auto-following vs must-not-change lists written into the spec for the clock migration.
Outcome
The clock migration (v10, repeated in v11) carried no silent semantic flips; the expression form removed the trap for every future clock change.
Mechanism
Constants derived from a base parameter encode a ratio at their birth; storing the evaluated number severs the dependency, so changing the base leaves stale semantics with no failing test. Expressions preserve the intent; and an explicit both-directions dependency list (follows / must-not-follow) is what makes a base-parameter change reviewable.
Applies when
- changing gait clock, control frequency, or units
- a reward threshold interacts with a phase/window duration
- config audit finds literals that encode ratios
“feet_air_time 阈值 0.25 是写死的,不跟 ct 走——0.40 时摆动窗 ~0.20s<0.25(恒拉长压力),0.50 时摆动窗 ~0.25s≈阈值(语义翻转)。按比例保原压力:0.25 → 0.3125(=0.625×ct;建议直接写成 0.625 * ct 表达式,一劳永逸)。”
train/WALK_V10_SPEC.md § 3. T —— 慢时钟 (训练侧必做一件) The restart reward table lists a reason for every term AND a lesson for every exclusion - absent terms are removed, not zero-weighted
minimal-reward-table-with-provenanceMaintain the reward table as an evidence ledger: every term cites the episode that justifies it, every excluded term cites the episode that convicted it (including the development stage it is valid at), and retired terms are deleted from the config, never left at weight zero.
Symptom
Seven walk generations had accumulated an entangled reward table where nobody could say which term earned its place; the restart needed a table that could be audited line by line.
Context
The minimal table v2 was built under three written principles: "一项管 一件事、结构性反抬脚的项一个不留、塑形只留一套相位逻辑;不在表里的一律 不加" (one term per job; zero structurally-anti-lift terms; exactly one phase-shaping logic; nothing outside the table gets added). Every row carries its provenance (e.g. base_height target = standing height cites the v4 crouch lesson; split x/y tracking cites the merged-exp gradient hole; world-frame yaw cites the v1/v3 body-frame lesson). Every EXCLUSION carries its same-type precedent: feet_landing_vel out because it is poison while the gait is unformed (drag pays 0, lifting pays - the v4-clearance / v8a-B "reverse threshold" family) though it was fine in v7 when the gait already existed - term validity depends on developmental stage; feet_air_time out with its measured non-lever evidence (v6 had it at 2.0 and still lifted 4 mm); and replaced terms are REMOVED from the config ("置 None,不是权重 0 挂着") so audits see truth, not dormant weight.
Change
Reward table rebuilt as ~20 rows each with weight + provenance column; exclusion list maintained alongside with the falsifying episode for each; dormant terms deleted rather than zeroed.
Outcome
Later revisions (S1.2/S1.3) modified the table by citing and updating specific rows' evidence rather than re-arguing the whole design; the table doubled as the lineage's reward-lesson index.
Mechanism
A reward table is a set of standing hypotheses; attaching each row's evidence makes revisions targeted and reversible, and recording why a term is absent prevents the cycle of re-adding known poisons. Deleting vs zero-weighting matters because config audits and DR interactions see the term either way - a zero-weight term is dormant complexity waiting to be flipped on wrongly.
Applies when
- designing a reward table for a restart or new task
- someone proposes re-adding a previously removed term
- auditing which reward rows still earn their place
“原则:一项管一件事、结构性反抬脚的项一个不留、塑形只留一套相位逻辑;不在表里的一律不加 … feet_landing_vel(评审 #4):拖地时代价恒 0、抬脚才收费——与 v4-clearance/v8a-B 同属「反抬脚门槛」家族,步态未成形时是毒;v7④ 加它时步态已存在。… 已从 cfg 移除(置 None),不是权重 0 挂着。”
train/OMNI_V0_SPEC.md § 3. 最小奖励表 v2 / 明确不带 Knee swing collapsed because it directly trades against the slip penalty - price the conflict explicitly and clamp what reward cannot hold
knee-swing-vs-slip-pricingWhen a behavior collapses as another metric improves, look for the term pair trading them and set their price ratio deliberately (with escalation and reverse tripwires pre-registered); where the policy actively spends action budget to undo your target, stop paying more reward and clamp the target space structurally.
Symptom
Knee peak-to-peak swing collapsed across generations - v5 33 deg, v10 26-30, v10b 7-8, v11 6.5-8.6 - and rolling the clock back did not recover it, acquitting the clock; the collapse tracked the gated slip penalty instead: "屈膝与不打滑在当前奖励里直接对抗" - v10b's excellent 93 deg slip was purchased with knee amplitude.
Context
Reward-side flexion fixes had failed three times: raising reference amplitude backfired twice (v9/v11), and v11's deep-squat default was actively fought by the policy - it spent 0.68 of action budget pulling the squat straight ("被策略花 0.68 动作拉直反杀"). v12's design accepted the conflict as real and attacked on two tracks: (1) ECONOMICS - a direct knee_swing_amplitude reward (+0.3, target 0.55 rad, capped at 0.6/step = 55% of tracking), explicitly opposed to the slip penalty by design ("显式对立——这正是设计:v12 就是这场对抗的定价实验"), with an escalation ladder (K +0.3 -> +0.5, then slip -0.5 -> -0.3, one layer at a time) and a reverse tripwire (slip telemetry back at v10 levels -> slip weight to -0.8, accept ~20 deg knee compromise); (2) STRUCTURE - knee target bounds [0.2, 0.9] rad so full straightening is physically impossible (straightest 11.5 deg) and the 0.68 fighting budget is released. A bonus falsifiable prediction was attached: phase-lock strength tracks amplitude (v9_probe 48 deg locked 2.5 Hz; v11 low-amplitude 1.36 Hz unlocked), so if K works, hardware phase-lock should return - one change, two verdicts.
Change
knee_swing_amplitude reward + knee target clamp + pre-registered escalation/reverse levers; the failed reward-side-only approach retired.
Outcome
The lineage was frozen before v12 trained (strategic reset), but the diagnosis stands as the walk line's clearest example of two reward terms trading a behavior between them, with the pricing experiment and structural clamp fully designed and calibrated.
Mechanism
When two terms price opposite aspects of one motion (swing amplitude creates yaw momentum that becomes slip), the optimizer settles wherever the price ratio puts it - patching one side moves the equilibrium, not the conflict. Explicit pricing makes the trade a designed quantity; structural clamps remove the regions where the policy spends budget fighting the designer.
Conflicts
The pricing experiment (K vs slip) was designed and calibrated but never trained - the 2026-08-05 reset suspended v12; the collapse attribution table and the 0.68-action counterattack are measured, the remedy's效果 is untested.
Applies when
- one gait quality degrades in lockstep with another's improvement
- the policy visibly fights a default pose or reference
- repeated reward-side fixes for the same behavior have failed
“膝摆塌在 v10→v10b,头号嫌疑是门控滑移罚(四代实测膝 p2p:v5 33° / v10 26~30° / v10b 7~8° / v11 6.5~8.6°;退时钟没救回 → 非时钟)——"屈膝"与"不打滑"在当前奖励里直接对抗 … 奖励侧修屈膝已三败 … v11 深蹲 default 被策略花 0.68 动作拉直反杀”
train/WALK_V12_SPEC.md § 0. 定位 / 2. K —— 膝摆经济(与滑移罚的对偶) Torque caps cannot soften footfalls - impact is falling-mass momentum, only the reward can treat it
landing-impact-not-fixed-by-torque-capsClassify each hardware symptom by the physics that sets it: quantities fixed by ballistic momentum at contact must be treated through the policy's trajectory (reward terms on approach velocity/force), never through actuator caps - and size such penalty weights against your own tracking reward, not a lighter robot's.
Symptom
Footfalls slammed at 1.78x body weight in sim baseline (human walking: 1.2-1.5x); the tempting hardware-side fix was cutting actuator torque limits.
Context
Measured directly: scaling torque limits from x1.0 down to x0.4 left peak landing force essentially unchanged (1.75 -> 1.78x body weight) - the impact force comes from the momentum of the falling mass at touchdown, not from motor effort. The fix has to change the trajectory, i.e. the policy, i.e. the reward: feet_contact_forces penalty above a threshold of 113 N (= 1.2x the 9.58 kg robot's weight), clipped, weight -0.005. The weight was sized locally, not copied: the reference robot's -0.001 would amount to 0.9% of tracking reward on this robot ("策略不会理它" - the policy would ignore it); -0.005 gives 4.4%.
Change
Added threshold-type contact-force penalty (-0.005, threshold 1.2x body weight) as one of v6-minimal's three changes; hardware torque cuts explicitly rejected as a footfall treatment.
Outcome
Landing force 1.72x -> 1.55x by v6 (target <1.5x, missed by 3% - progress booked honestly); the torque-cap dead end was documented so it would not be retried.
Mechanism
At touchdown the ground stops a ballistic mass; the impulse is set by approach velocity and effective inertia, which motors can no longer influence in the final instant. Only earlier trajectory choices (approach velocity, timing) reduce it - and those are selected by the reward, not by actuator limits.
Applies when
- footfall impact or landing noise on hardware
- proposals to derate torque as a softness fix
- importing contact-force penalty weights from another robot
“⚠️ 硬件限扭降不了落脚力 —— 砸地力来自下落质量的动量: 实测 tau ×1.0→×0.4, 落脚力 1.75→1.78× 体重纹丝不动。只有这条奖励能治。… ⚠️ 权重不能用 Pi 的 −0.001 —— 实测在我们身上只占跟踪奖励的 0.9%, 策略不会理它 (Pi 6.94 kg 更轻)。−0.005 给到 4.4%。”
train/WALK_V6_MINIMAL.md § ③ 新增 feet_contact_forces Exponential tracking kernels go flat exactly when the error is largest - pair them with an L2 term for the far field
exp-kernel-needs-l2-far-fieldNever let an exp/Gaussian kernel be the only tracking pressure on a quantity that can drift far from target: pair it with an unbounded (L2) term sized as the "don't diverge" floor, and check which frame the kernel reads.
Symptom
With only an exp-type yaw tracking term (exp(-err/std^2), std 0.25), a robot whose heading had drifted badly received almost no corrective gradient: at error 0.6 rad/s the term evaluates to exp(-0.36/0.0625) = 0.003 - near zero AND flat.
Context
The exp kernel is excellent for fine tracking near zero error but its gradient vanishes at large error - precisely when correction matters most. Fix: add track_ang_vel_z_err_l2 (-0.5), a plain quadratic on the same quantity: "exp 管精细跟踪、L2 管'别发散', 互补". Both terms deliberately read WORLD-frame wz (matching the exp term's source), because this torso sways enough that body-frame wz means are systematically off (measured -0.039 while actually turning +0.152). The same far-field-gradient argument reappears in the v8 risk list: frozen joints could not climb back because their huge error put them on the exp plateau ("远端梯度消失是冻结自锁的帮凶").
Change
Added the L2 companion term at -0.5 alongside the existing exp term (a term that had been in an earlier draft and was lost in a rewrite - itself worth noticing).
Outcome
Corrective pressure restored across the whole error range; the exp+L2 pairing became the house pattern for tracking terms.
Mechanism
d/de[exp(-e^2/s^2)] -> 0 as e grows: the kernel saturates and cannot distinguish bad from terrible. A quadratic's gradient grows with error, covering the far field; summing the two yields monotone corrective pressure with fine shaping near the target.
Applies when
- tracking rewards use exp/Gaussian kernels alone
- a drifted or frozen state fails to recover during training
- designing tracking terms for quantities with large transient errors
“exp 在误差大时梯度趋零, 恰好在最需要纠正的时候失灵。… 误差 0.6 → exp(-0.36/0.0625) = 0.003, 接近零且平坦。… exp 管精细跟踪、L2 管"别发散", 互补。”
train/WALK_V7_SPEC.md § ② track_ang_vel_z_err_l2 −0.5 —— 补 exp 的梯度洞 Set torque limits per joint from measured gait peaks - a uniform percentage is the wrong shape, and training must use the deployed numbers
torque-limit-shape-by-measured-peaksMeasure per-joint torque peaks in the actual gait and set each limit as measured-peak x margin capped at rating; then propagate the same numbers into training and add an automated deploy-time consistency check - never derate by a uniform percentage, never let training assume torque deployment will not grant.
Symptom
A uniform 50% torque derating (18/8.5/7) had piled safety margin on the joints that never use it while cutting the busiest joint below half its measured demand.
Context
Per-joint gait peaks were measured (walk_v5 at cmd 0.3/0.6): RS06 (hip_pitch/knee) uses 5.5-5.9 N*m = 15-16% of its 36 N*m rating - cutting it to 12 is a free safety win; RS02's ankle_pitch runs at 16.2 N*m = 95% of its 17 N*m rating - "它是速度的硬件瓶颈", no room to cut; RS00 measured 36-44%, capped at 11. The resulting shape 12/17/11 replaced the uniform percentage. Sweeps across several limit sets (rated / 50% / 14-17-11 / 12-17-11) produced identical speed, lift, and landing force - within this range the limits do not shape the gait; what matters is consistency: "关键是训练和硬件必须是同一个数", because the exporter fills effort_limit from tau_limit, and a policy trained at rated 36/17/14 "会假设有三倍力矩可用" while deployed at 12/17/11 (exactly the v5 cross-generation inconsistency later suspected in its wild kicking).
Change
robot.yaml tau_limit set to the measured-shape 12/17/11, firmware written to match, and train/isaac_values.py regenerated so training sees the same limits; the deploy tool self-checks limits against robot.yaml on every run.
Outcome
Free safety margin captured where demand is low, the real bottleneck joint left at rating, and the train/deploy torque worlds unified with an automated consistency check.
Mechanism
Torque demand is grossly unequal across joints in a gait (15% vs 95% of rating here); a uniform percentage misallocates the safety budget by construction. And since the trainer treats effort_limit as a plant truth, any train/deploy mismatch is an invisible plant gap of exactly the mismatch ratio.
Applies when
- choosing safety torque limits for a legged platform
- training-vs-deployment actuator limit audit
- one joint runs near rating while others idle
“曾用统一 50%(18/8.5/7)是错的形状: 把余量堆在用不到的 RS06 上, 却把 ankle_pitch 砍到需求的 52%。… RS02 在 0.6 m/s 已用到额定 95%, 它是速度的硬件瓶颈 … 实测多组限幅 … 完全一致 —— 限幅在这个范围对步态零影响, 关键是训练和硬件必须是同一个数。… 若训练仍按额定 36/17/14, 学出的策略会假设有三倍力矩可用。”
train/WALK_V6_MINIMAL.md § 3. 训练侧必须同步的一件事 Model CAN polling skew - joint observations are 6-9 ms stale by read order
can-timing-skew-modelingIf joints are read sequentially over a shared bus, reproduce the per-group observation staleness in sim (or randomize it over the measured range) - synchronous observations are a privileged fiction.
Symptom
Policies trained with synchronous joint observations degrade on hardware where motors are polled sequentially over CAN - hip data is already 6-9 ms old by the time ankle data arrives.
Context
Menlo's core sim2real finding on a leg platform of almost identical mass to Lucen's. CAN is a sequential bus: one poll cycle reads motors in a fixed order, so the observation vector mixes timestamps. Lucen runs 6:6 motors on a dual CAN split, so the problem transfers one-to-one.
Change
Explicitly model CAN timing skew in sim: give the joint groups different observation delays matching physical read order. Menlo went further - running real firmware in the loop with a motor simulator between MuJoCo and firmware injecting 0.4-2 ms uniformly distributed delay.
Outcome
Reported by the reference team as their core sim2real enabler on a same-scale platform; recorded in Lucen's experience log as directly applicable ("这个问题一模一样").
Mechanism
A policy exploits any cross-joint temporal coherence present in training observations; when hardware breaks that coherence per bus position, the learned feedback acts on inconsistent state estimates, injecting phase error exactly at the control bandwidth.
Conflicts
Second-hand episode: outcome numbers are the reference team's report, not a Lucen-run experiment; Lucen adopted the requirement but the corpus has no Lucen A/B of skew-on vs skew-off.
Applies when
- robot polls actuators sequentially over CAN/RS485 or similar shared bus
- sim2real degradation appears as jitter or oscillation not seen in sim
- designing the observation/delay model before a training run
“电机走 CAN 是顺序轮询的，髋部电机的数据到踝部电机上报时已经陈旧了 6-9 ms，他们直接在仿真里显式建模了 CAN 时序偏斜，按读取顺序给三组关节不同的观测延迟。… 注入 0.4–2 ms 的均匀分布延迟。你们是 6:6 双 CAN 分总线，这个问题一模一样”
Experience.md § 执行器 + 时序建模 (line 6) Verify changes in the run's resolved config (and checkpoint md5), never in the source you edited
resolved-config-is-source-of-truthAttribution and single-variable claims must be made on the resolved per-run config (and checkpoint hashes), not on source diffs; verify every intended variable landed before burning compute, and verify every rollback byte-level against the historical resolved config.
Symptom
An intended arm-B config change never reached the training run - the run was grid-identical (117/117 cells) to its C2 predecessor - and the burn was only understood afterwards.
Context
The repo's discipline hardened around the logged resolved config (logs/<run>/params/env.yaml) as the only source of truth: (1) the C2 root-cause analysis was performed against the checkpoint's logged env.yaml, not the code ("以真相源 23-19-25/params/env.yaml 核实"); (2) C4 added a pre-flight: grep the landed env.yaml for the new keys, and compare the first checkpoints of the two arms - identical md5 means the variable did not land, stop immediately; (3) the C4 full rollback was accepted only after starting a 1-iter run and byte-comparing its resolved env.yaml against the historical 700-era file (identical except 4 dormant schema fields, each verified to be at its no-op default).
Change
Standing pre-flight and post-change verification: dump/diff the resolved config that the run actually consumed; use checkpoint hash equality as a cheap "variable landed" detector between arms.
Outcome
Caught the not-landed variable class of failure; made the rollback provably equivalent to the historical training state rather than believed-equivalent.
Mechanism
Between edited source and the running experiment sit layered overrides, env-var switches, and registration logic; only the resolved, serialized config reflects their composition. Diffing at that level tests the actual experiment; diffing source tests intent.
Applies when
- launching an A/B pair or any single-variable rung
- rolling back to a historical training state
- a run behaves as if a change was never applied
“开训前先验落盘 cfg（上一轮臂B 的改动没进 run，与 C2 逐格 117/117 相同）：grep -E "base_com|joint_friction|push_robot|track_lin_vel_y_exp" logs/<run>/params/env.yaml 另：两臂第一个 checkpoint 的 md5 若相同 = 变量没进去，立刻停。”
train/C_LADDER_RUN.md § 3d. ⚠️ 开训前先验落盘 cfg / 3l. 回退清单（验证） Audit which joints your imitation term constrains - a task that needs deviation is fighting the reference
imitation-term-scope-auditList which joints your imitation/deviation terms actually constrain and check the new skill's required motion against that list; for balance-coupled joints deliver references as feed-forward residuals, not absolute-position targets - and never assume "reference = 0" is neutral.
Symptom
Sidewalk would not learn despite a dedicated tracking reward; meanwhile the gait-shaping imitation term (joint_pos_ref) computed its error norm over ALL 12 joints while its reference covered only the 6 sagittal joints - roll/yaw reference was constantly 0.
Context
Two prior generations had shown the forward gait itself was taught by joint_pos_ref, not discovered by PPO (v6 halved the shaping and swing height collapsed 35 mm -> 4 mm). So the reference is load-bearing - but sidewalk requires hip_roll to deviate from nominal, and the all-joints norm punished exactly that deviation: "一边悬赏一边罚过程" (posting a bounty while punishing the process). A follow-up experiment (C4-redo3, free_roll=True releasing the 4 roll joints from the norm) raised the regularization headroom 6x -> 27x yet sidewalk stayed flat and released hip_roll wandered, killing other skills - net negative, withdrawn. A --roll-absolute probe showed the converse failure: pinning roll to a clock-driven absolute trajectory drove tilt 6.9 -> 13.7 deg. Conclusion recorded: absolute-position imitation cannot teach actions that must be superimposed on state feedback.
Change
The audit reframed the problem: neither punishing roll deviation nor freeing roll nor absolute roll tracking works; the reference for a balance-coupled joint must be delivered as feed-forward under the policy's residual control (see feedforward-for-phase-locked-skills).
Outcome
free_roll rung: joint_pos_ref term rose 0.887 -> 1.104 (release confirmed effective) but vy stayed flat; regularization hypothesis eliminated by experiment.
Mechanism
An imitation error norm defines a cage: joints inside it are pulled to the reference in absolute position, so any skill requiring systematic deviation is taxed per step; but joints carrying active balance cannot follow absolute references either, since their correct position depends on state. The scope and the delivery mechanism of the reference are therefore design decisions per joint, not defaults.
Applies when
- adding a skill that moves joints your reference sets to zero/nominal
- an imitation or deviation penalty coexists with a new tracking reward
- considering releasing joints from a shaping term mid-lineage
“前进步态也不是 PPO 自己发现的，是 joint_pos_ref 教出来的（v6 砍半塑形 → 抬脚 35 mm 塌到 4 mm…）。而 ref_joint_offset 原本只写 6 个矢状面关节，roll/yaw 参考恒 0 —— 侧走既没被教，roll 一偏离 nominal 反被 joint_pos_ref 扣分。一边悬赏一边罚过程。”
train/C_LADDER_RUN.md § 3e. 为什么首战 FAIL / 3i. 解锁笼子 Never referee a suspect metric with another metric from the same code - they can share the disease
independent-referee-for-metric-disputesTo adjudicate a disputed measurement, compute the quantity by an independent method from raw state; never accept a sibling column from the same pipeline as the tiebreaker.
Symptom
A triple reversal on one question: sidewalk sign diagnosis (correct) was retracted using a second metric from the same script, then the retraction itself had to be retracted when that second metric turned out to be the buggy one - two opposite-direction errors on the same problem in one day, both written into the execution sheet.
Context
The probe's net-displacement metric suggested the sidewalk reference sign was inverted. Worried about yaw-drift pollution of net displacement, the author checked the same table's body-frame vy_mean column (~0.003 everywhere, 20-50x smaller) and retracted the sign diagnosis. But vy_mean came from mj_objectVelocity, which was silently reporting vertical velocity due to a frame bug - the "referee" was the diseased measurement. Re-measured with a truly independent computation (xmat.T @ qvel, world trajectory), the original diagnosis was confirmed: saw -0.5 gave vy +0.058/-0.130 (76%/106%), consistent with the net-displacement values all along (yaw pollution was real but only 10-21 deg, nowhere near reversal-sized).
Change
Lesson written twice, verbatim, as a hard rule: when questioning a measurement, the referee must be an independent algorithm (different code path, different physical derivation), e.g. rotate qvel by the body matrix directly, or inspect the raw world trajectory.
Outcome
With the independent referee in place the frame bug was confirmed, fixed, and the whole C4 line re-scored - revealing sidewalk had been working (see body-frame-velocity-api-audit).
Mechanism
Metrics sharing a code path (or an upstream API) share failure modes; agreement between them is evidence about the code, not the world. Only a measurement with an independent derivation can break the tie, because its errors are uncorrelated with the suspect's.
Applies when
- two metrics of the same quantity disagree
- about to retract a conclusion based on a second readout
- auditing evaluation code after a surprising result
“我用一个坏指标去质疑一个好指标，并把撤回写进了执行单。教训（写死）：质疑一个测量时，不能用同一份代码里的另一个测量当裁判 —— 它们可能同源同病。裁判必须是独立算法（这次的裁判应该一开始就是 xmat.T · qvel[:3]，或直接看世界轨迹）。”
train/C_LADDER_RUN.md § 3m. 二 我今天犯了两个方向相反的错 / 3n. 五 元教训 Privileged signals (true velocity, foot force, foot height) go to the critic only
observation-honesty-critic-onlyTreat the actor observation vector as a hardware contract: every element must exist on the real robot with realistic noise; privileged simulator truths belong in the critic only.
Symptom
Policies trained on ground-truth base linear velocity work in sim and fail on hardware, where only a drifting IMU and encoders exist - the policy has learned to depend on a signal that does not survive deployment.
Context
Many open-source locomotion stacks feed simulator ground-truth linear velocity to the actor. The reference team refused: the real robot has no ground-truth velocity. Asymmetric actor-critic keeps the training benefit of privileged information without deploying the dependency.
Change
Route ground-truth velocity, foot contact forces, and foot heights to the critic only; the actor observes exclusively signals that exist on hardware (IMU-derived quantities, encoders, commands, previous actions).
Outcome
Recorded as adopted doctrine in Lucen's experience log; the trained actor's input contract matches what the real robot can actually produce.
Mechanism
The critic is discarded at deployment, so it may consume any privileged state to reduce value-estimation variance; the actor's observation set is a deployment contract - anything in it that hardware cannot supply (or supplies with different noise/drift) becomes a train/deploy distribution shift the policy was never trained to handle.
Applies when
- designing actor/critic observation spaces
- reviewing a config where the actor sees base_lin_vel or contact forces
- sim policy is strong but real robot drifts, oscillates, or falls without obvious actuator cause
“很多开源代码库把真值线速度喂给策略，Asimov 团队没有，因为真机上没有真值速度，只有会漂的 IMU 和编码器；用完美速度训练出来的策略会依赖它，然后在硬件上失效。真值速度、足底力、足高统统只给 critic”
Experience.md § 观测空间的诚实性 (line 7) 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) 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) Guessed joint friction was 2.5x low and damping 5x high - measure, then DR around nominal
friction-measured-not-guessedMeasure frictionloss and damping separately (they need different rigs), put the measured value at DR center, and express DR as an additive band around that nominal - a DR range around a guessed value can exclude the real robot entirely.
Symptom
Old MJCF friction values were invented, not measured; when finally measured, every guessed value was wrong by a large factor in some direction.
Context
Joint friction split into Coulomb (frictionloss, tau_c) and viscous (damping b). Measured with the robot hung from a crane (吊机测) while armature was measured no-load; the two measurements are deliberately separated. Old MJCF: frictionloss 0.05, damping 0.1, DR joint_friction range [0, 0.1] "凭空拍的" (made up out of thin air).
Change
Replace guessed values with measured ones - frictionloss: RS06 0.15 / RS02 0.12 / RS00 0.13 N*m (old 0.05, i.e. 2.5x too low); damping: 0.02 N*m*s/rad on all three motor types (old 0.1, i.e. 5x too high). DR reshaped from an absolute made-up range [0, 0.1] to an additive band around measured nominal: joint_friction_add [-0.05, +0.10].
Outcome
"摩擦定稿(与 armature 一起, plant 参数第一次全部来自实测)" - friction frozen as part of the first fully-measured plant; DR now brackets a measured truth instead of spanning an invented interval.
Mechanism
Coulomb friction and viscous damping have opposite behavioral signatures (constant-torque threshold vs velocity-proportional drag); guessing both wrong in opposite directions gives a plant that is simultaneously too easy to start moving and too hard to move fast. DR centered on a wrong nominal makes the policy robust to a family of plants that does not contain the real one.
Applies when
- plant friction/damping values have no measurement provenance
- DR ranges are absolute intervals rather than bands around a nominal
- policy is over- or under-damped on hardware relative to sim
“测关节摩擦。吊机测，而armature应该空机测试。… frictionloss τ_c (N·m) │ 0.15 │ 0.12 │ 0.13 │ 0.05(低 2.5×) … damping b (N·m·s/rad) │ 0.02 │ 0.02 │ 0.02 │ 0.1(高 5×) … DR │ joint_friction_add: [−0.05, +0.10] 叠标称 │ 旧 [0, 0.1] 凭空拍的”
Experience.md § 摩擦定稿表 (lines 12-25) Low-friction robustness traced to kd DR bandwidth, not friction training - by digging resolved params across 8 lineages, 3840 cells
kd-bandwidth-mu-law-attributionAttribute capability differences by tabulating every lineage's resolved training params and eliminating zero-variance and non-aligned columns first; never let an eval-side override knob serve as the explanation axis, and never write a mechanism into a law before it survives a targeted test.
Symptom
Lineages differed wildly in low-ground-friction survival, and the intuitive explanation - "some trained ground friction, some didn't" - was about to steer the ladder toward a ground-mu training rung.
Context
The attribution ran as a full parameter-vs-result cross: 8 lineages x 4 eval kd levels x 6 mu levels x 20 seeds = 3840 cells, with each lineage's RESOLVED training params dug out and compared item by item. First kill: all 8 lineages had ground mu pinned at (1.0,1.0) - zero variance - so low-mu differences cannot come from friction training at all. The only training parameter aligned with the mu score was kd DR bandwidth: narrow (<=0.24) lineages scored 19.9/19.5/19.5, wide (>=0.40) scored 17.1/15.2/14.6/14.2/12.8 - the two groups completely non-overlapping. Every rival was excluded item by item (kd center no; kp band no; COM small-beneficial non-driving; friction rung a clean double null 19.5->19.5 and 15.2->14.6; iteration count non-monotonic), and the one clean single-variable causal link confirmed it: the s2e-3 kd surgery (0.7,1.3)->(1.08,1.32) moved the score 17.1->19.5. Counter-proof against "each best at its own operating point": the narrow-band lineage evaluated OUT of band (18.2) still beat the wide-band lineage at its own band center (9.2). Two axes were ordered never to be conflated (the first attribution's own error): training kd bandwidth is a parameter axis / lineage property; the eval-side --kd-scale knob is a plant axis (more damping physically helps on slippery floors for ALL policies) - "plant 轴只能当部署缓解,不能当 归因". A tempting mechanism story ("drag vs step attractor") was tested and falsified, and explicitly kept OUT of the law: "机制未定, 不入定律".
Change
The planned ground-mu training rung was recommended closed ("建议 不开") in favor of a kd band-narrowing rung (0.8,1.2)->(0.9,1.1) centered on the deployed value - with a pre-registered risk that the law demands "bandwidth = measured dispersion" and the real robot's kd dispersion was not yet measured; if it exceeds +/-10%, narrowing sacrifices real coverage and the rung must yield.
Outcome
A whole training rung was deleted from the ladder by attribution alone (the second S2 pass dropped mu and push, 5 rungs -> 3); floor material became a deployment-selection input (mu <~0.6 -> deploy the kd1.2 gain profile) rather than a training target.
Mechanism
Cross-lineage performance differences must be attributed over the actual training-parameter table, not over eval knobs or plausible stories: eval knobs act on the plant for every policy (a physical effect), while lineage properties come only from training-time parameters. Zero-variance columns are free eliminations, and one clean single-variable rung is worth more than any correlation.
Applies when
- explaining why lineages differ on a robustness axis
- an eval-side knob (gain scale, power) changes results and invites misattribution
- deciding whether to open a DR rung for an axis never actually varied in training
“8 血统地面 μ 训练带全部钉 (1.0,1.0) 零方差,低 μ 差异与「训没训地面摩擦」无关,是 kd DR 带宽的副产物 … 宽 ≤0.24 → 19.9/19.5/19.5;宽 ≥0.40 → 17.1/15.2/14.6/14.2/12.8, 两组完全不重叠。… 训练 kd 带宽 = 参数轴/血统属性;评测部署 --kd-scale = plant 轴 … plant 轴只能当部署缓解, 不能当归因。… 机制未定, 不入定律。”
train/OMNI_V0_SPEC.md § 4. 地面 μ 鲁棒性 = kd DR 带宽的副产物 (2026-08-08) The best checkpoint to SHIP is not the best checkpoint to CONTINUE FROM - maturity is capital against adaptation shock
root-maturity-vs-product-qualityDecide shipping points and fork roots separately: gates rank products, but a root candidate must prove itself by surviving a continuation under the next rung's shift (dual-arm if in doubt) - and prefer the more-trained point as root when product metrics conflict with maturity.
Symptom
A band re-audit found s1e-300 beat the incumbent root s1e-500 on nearly every quality gate (stepping 19/20 vs 13/20 with historically-best 26.9 mm swing, speed gate 14/20 vs 2/20, heading 26 vs 54 deg/20 s) - suggesting the root had been mis-picked and the younger point should take over.
Context
The dual-arm control settled it the other way: continuing the S2 PD rung from s1e-500 adapted smoothly (3/3 smoke throughout), while the b300 control arm (same config, from s1e-300) fell into a survival valley under the PD shock (+100 iters: 1/3 -> 0/3), never climbed out within budget, and its 800-iter product scored 13/20 survival - eliminated. Verdict: "幼年点自身指标再好也扛不住新 DR 适应冲击, 成熟度是本钱,s1e-500 根被数据背书" - a young point's own metrics, however good, do not survive new-DR adaptation shock; maturity is capital. The audit still yielded value: the band scan (200-1000, per-100) mapped the lineage's arc (200 dragging -> 300 peak -> 400+ decay -> 900+ drift blowout), and 300 remains the better PRODUCT answer for shipping-as-is questions.
Change
Selection doctrine split into two questions with different answers: best-product point (quality gates at the point itself) vs best-root point (survives adaptation shocks; more training age = more capital), each decided by its own evidence - and root claims settled by a dual-arm continuation test, not by point metrics.
Outcome
s1e-500 kept the root role with data behind it; the S2e ladder built on it passed rung after rung, while the b300 line was closed at the cost of one control arm.
Mechanism
Early checkpoints sit near sharp optima with less accumulated robustness structure; their headline metrics reflect the narrow training distribution, not resilience to distribution shifts. A continuation rung is itself a distribution shift, so the root property being selected for is shock tolerance - observable only by actually continuing, never by static gates.
Applies when
- a younger checkpoint outscores the current root on quality gates
- choosing the base for a robustification or command ladder
- a continuation run stalls in an early survival valley
“b300 对照臂 … PD 冲击下存活谷(+100 起 1/3→0/3),预算尽未爬出,800 档 20-seed 存活 13/20 出局——幼年点自身指标再好也扛不住新 DR 适应冲击,成熟度是本钱,s1e-500 根被数据背书”
train/README.md § omni_s2e_pd (b300 对照臂) / s1e 选点重审 Calibrate a wall penalty by measuring healthy and sick policies - healthy pays ~0, the disease pays a wall
calibrate-threshold-between-healthy-and-sickCalibrate every threshold penalty by evaluating its exact formula on replays of at least one healthy and one sick policy: place the threshold between their distributions, size the weight so the sick policy pays a decisive fraction of tracking while the healthy one pays ~0, and pre-compute neighboring thresholds for cheap adjustment.
Symptom
Real walk_v7 occasionally clipped its own legs (stance narrowed to 133 mm mean vs nominal 214.5); a foot-distance penalty was needed, but an uncalibrated threshold/weight risked either doing nothing or becoming a reverse barrier.
Context
The term (relu(d_min - lateral foot distance), measured in the base yaw frame because world-frame y is meaningless after turning) was calibrated before training by replaying three known policies through the exact reward formula at the acceptance operating point: healthy v5 (183 mm) pays 0.6% of tracking - effectively free; narrowed v7 (133 mm) pays 22% - effective widening pressure; collapsed v8 (93 mm) pays 55% - a wall. d_min 0.16 was placed deliberately between healthy and sick, with alternative thresholds (0.14/0.18) pre-computed in the tool output for later adjustment. The shape self-check was named as a standing question: "先问'零代价的选项是什么'" - the zero-cost region must be exactly the desired behavior.
Change
feet_lateral_distance added at d_min 0.16 / weight -10, with telemetry expectation pre-registered (should decay toward 0 as stance learns >160 mm; if bow-legged over-widening >214 appears, only then discuss an upper bound).
Outcome
v9_probe on hardware: no leg contact ("没碰腿(N2 兑现)"), stance min 145/126 mm green; the term's zero-cost design left healthy gait untaxed.
Mechanism
A relu threshold penalty defines a free region and a priced region; its correctness is entirely in where the boundary sits relative to the healthy and pathological distributions. Replaying known-good and known-bad policies through the exact formula measures both distributions in the term's own currency, making the threshold and weight a placement decision instead of a guess.
Applies when
- adding any relu/threshold-style wall penalty
- a safety margin (foot distance, joint limit, clearance) needs enforcement without taxing normal behavior
- choosing between candidate thresholds for a new term
“形状自检 (v6 横向组/v8-B 的教训 —— 先问"零代价的选项是什么"): 标称站距付 0, 健康步态付 ~0, 收窄才付费 … v5(健康) 183 mm … 0.6% ≈ 免费 | v7(收窄) 133 mm … 22% —— 有效推宽 | v8(塌陷) 93 mm … 55% —— 墙 … d_min=0.16 恰在 v5(183)与 v7(133)之间”
train/WALK_V9_SPEC.md § 2. N2 —— 脚距惩罚(已完成权重预标定) The fallen-state reset was designed, not sampled from SO(3) - fixed category shares with jitter, a low drop that settles physically, equal left/right shares for mirror augmentation, and a numeric check before training
fallen-pose-reset-distributionBuild a fallen-start distribution from named, physically plausible categories with jitter and a settle phase, keep mirrored categories at equal probability, and check the realized shares and penetration numerically before spending a training run on it.
Symptom
A get-up policy can only learn from the fallen states its resets produce; uniformly random orientations produce ground-penetrating and limit-jammed states the robot can never be in.
Context
R0 reset_root_fallen: supine 30%, prone 30%, side_l 15%, side_r 15%, mid (random axis 50-125 deg) 10%, +/-15 deg jitter, full yaw, dropped from 0.28-0.40 m and left to settle under physics, joints uniform inside the soft limits with a 5% margin plus small random velocities. Random SO(3) was rejected (the advisor agreed). side_l and side_r must have equal probability because mirror augmentation turns a left fall into a right fall. The advisor had proposed supine and prone only for R0; the spec included side and mid because the feasibility accounts showed physical solutions for all of them, and wrote "narrow back to supine+prone" down as the first fallback. With no display on the training box the reset was checked numerically instead of by eye.
Change
Category mix as above; realized shares, settle height and penetration measured over 512 envs before the first run. A fallen-state bank (real falls, settled and stored) was pre-registered for R2.
Outcome
Realized shares 29.3/31.6/16.4/17.8% against the config, settle +0.262 m, final penetration 0/512 (a 0.10 m peak at the write instant, ankle links only, pushed out within 80 ms because the 0.28 m drop floor is shorter than a fully extended leg). R0's failure was a reward basin, not a reset artifact. The fallen-state bank stayed unbuilt through V3.1 (checklist item open); R0.3 later re-sliced the prone share into roll_l/roll_r bands, which is what forced the acceptance distribution to be frozen separately.
Mechanism
A category-structured, physically settled start distribution keeps training on states the robot can actually occupy, and equal mirrored shares keep mirror augmentation a pure doubling of data rather than a bias.
Applies when
- designing reset distributions for get-up, recovery or multi-contact skills
- mirror/symmetry augmentation is on and the task has chiral start states
- no viewport is available to inspect resets on the training machine
“角度 jitter ±15°、yaw 全域、0.28~0.40 m 低空放下由物理沉降,关节软限位内 均匀(留 5% 余量)+ 小随机速度。**不用 random SO(3)**(会采出穿地/极限卡死 等现实不可能状态,顾问同判) … side_l/side_r **概率必须相等**(镜像增强的样本同分布前提)”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §3 R0 任务定义 / §9 核查单 Push DR helped one lineage and hurt another at the same dose - robustness budget is conserved and gets borrowed, not created
push-dr-conditional-budget-conservationBefore opening a disturbance-DR rung, measure whether the untrained policy already meets the spec; if training it anyway, expect the benefit to be conditional on the lineage's existing DR load, grade the intensity, and audit retained margins - budget spent elsewhere will be borrowed back.
Symptom
The push rung's outcome flipped with the lineage: direct +/-0.6 m/s push failed outright on first attempt (base walking collapsed - kd1.2 scan 0/3 from iter 3300, sim2sim self-falls with pushes OFF - no PASS point existed); staged +/-0.3 then gave the narrow-kd single-working-point lineage real gains (push survival 1/5 -> 4/5) while the SAME dose made the dual-working-point balanced-band lineage WORSE (20-seed survival 18 -> 12/20 plus across-the-board push regression).
Context
The four-ladder verdict ("四梯定案", s2e/s2f at both intensities) named the pattern: "push DR 收益条件性" - the benefit is conditional on how much robustness budget the lineage has already spent. The law candidate: "DR 总预算守恒, 平衡带鲁棒性从抗扰余量借" - total DR budget is conserved; a lineage already covering a wide plant band pays for push tolerance out of its disturbance margin. Both S2 ladders therefore closed at the friction rung, with the decisive numerator: untrained push tolerance already met the 4-6 N*s requirement, so the rung was not needed at all ("⑥ push 不训(收益条件性,免训 ±0.6 已达 标)"). The same accounting later justified the C-before-S2 ordering ("push/μ 两轮已实证 DR 预算有限且会被重分配") and trimmed the second S2 pass to three rungs.
Change
Push removed from the standing ladder; graded intensity retained as the method IF a lineage ever needs push training; "does the untrained policy already meet the disturbance spec" instituted as the first check before opening any disturbance rung.
Outcome
Two rungs (push, ground mu) deleted from the second S2 pass on measured grounds; the ladder's real yield was re-stated honestly as precision, not robustness (speed gate 0 -> 20/20, zero-command drift 0.98 -> 0.06 m, but push 159 -> 125/160).
Mechanism
A fixed-capacity policy allocates representation and margin across the training distribution; adding a disturbance axis to a lineage that already spans a wide plant family forces reallocation - the new tolerance is bought with existing margins. Lineages with narrow plant coverage have free budget, so the identical DR dose lands as gain. Benefit is a property of (dose x lineage state), never of the dose alone.
Applies when
- proposing push/perturbation training on a hardened lineage
- the same DR rung helped one lineage and hurt another
- accounting where a ladder's robustness gains actually came from
“push DR 收益条件性 —— s2e⑥a (单工作点血统 kd 窄带) ±0.3 得抗推 1/5→4/5; s2f⑥ (双工作点平衡带血统) 同档反而 20-seed 存活 18→12/20 且抗推全面倒退。规律候选: DR 总预算守恒, 平衡带鲁棒性从抗扰余量借。两阶梯均以 ⑤ 摩擦级收官 … 抗推 4~6 N·s 免训已达标。”
train/OMNI_V0_SPEC.md § 4. ⑥ push 四梯定案 (2026-08-07)