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
90 cards matching “root-maturity-vs-product-quality”.
A constant-value plant rung passed every binary gate with record scores - and shipped 60% thinner posture margins that hardware exposed
constant-value-dr-overfits-marginRandomize deployment-critical axes over a narrow band spanning the measured real support - never a single value, never a fictitious tail - and report graded margin quantities (tilt margin) next to binary gates, because saturated gates rank thin-margin and thick-margin policies identically.
Symptom
s2_lag1 (trained at constant 1-frame latency) posted the strongest sim gate sheet in history (20/20 everywhere) yet was unstable on hardware, while s1e (trained across the full 0-3 frame band) was the every-run-stable SOTA at the same power.
Context
The sim autopsy (new --delay-jitter harness modeling the BusWorker's time-varying phase drift): 18 runs across constant and time-varying delays ALL survived - time variation alone does not kill - but the tilt-margin ordering reproduced hardware exactly: s1e 7.7-9.1 deg (thickest) < s2_lag1 10.7-15.0 < s1c 16.2-18.2. Attribution: constant-value training permits precise specialization to that one value; s1e's band diversity forced cross-value robustness - "恒定 1 帧训练 vs s1e 的 0~3 帧全带——分布多样性逼出跨值鲁棒,恒定值允许精确 特化" - so the constant-rung policy's margins were ~60% thinner, fine in sim's clean world, pushed over the line by real-world disturbances. Tool lesson booked: "存活门二值饱和后掩盖裕度差" - binary survival gates saturate and hide margin differences; graded margin columns (tilt-max) belong in the report. The synthesis with the opposite failure (wide tails cause drag-glide): the proposed resolution was a NARROW uniform band (0.02, 0.04) covering exactly the real 1-2 ticks - diversity inside the measured support, no tail, no single point. s1e's root selection later leaned on the same property: its full-band latency training "预装" the delay rungs and delivered "全工况稳定裕度" that survived power derating.
Change
DR-on-an-axis design refined to a three-way distinction: no wide fictitious tails (drag), no single constant values (thin margins), but a narrow band spanning the measured real support; acceptance reports gained graded margin columns alongside binary gates.
Outcome
The tilt-margin column entered the standard report; the s1e root (band-trained) carried the C ladder while the constant-value branch was archived with its three contributions credited.
Mechanism
Robustness margins are shaped by the diversity of the training distribution, not just its support: a point-mass distribution lets the optimizer trade margin for on-point performance, while a band forces solutions that keep margin across the band - and binary survival metrics cannot see the difference until the margin is spent on hardware.
Conflicts
The narrow-band (0.02,0.04) resolution was a pending recommendation ("裁决建议(待用户)") at the time of writing; the lineage instead moved root to s1e whose full-band training predated the staged ladder - the deterministic-staging card and this card record the two failure modes the final design must avoid simultaneously.
Applies when
- a rung trained at a fixed plant value aces sim but wobbles on hardware
- binary acceptance gates are all saturated across candidates
- choosing between constant, banded, and wide DR on one axis
“18 跑全活,时变性单独不足以击杀;但 tilt_max 裕度排序完整复现真机:s1e 7.7~9.1°(最厚)< s2_lag1 10.7~15.0 … 恒定 1 帧训练 vs s1e 的 0~3 帧全带——分布多样性逼出跨值鲁棒,恒定值允许精确特化 … 存活门二值饱和后掩盖裕度差(s2_lag1 sim 门 20/20 史上最强却真机不稳)”
train/README.md § s2_lag1 真机不稳 × s1e 稳的 sim 对拍(2026-08-07,时变延迟实验) The shipped checkpoint was chosen by scanning checkpoints on the full gate - neighbours 100 iterations apart failed 1 and 38 cells, late checkpoints degraded - never by taking the last one, and training stopped on signals, not on a schedule
checkpoint-choice-is-a-full-gate-scanChoose a release checkpoint by running the full acceptance battery over a band of checkpoints (including the transfer axis), stop training on measured signals rather than a fixed iteration count, and expect adjacent checkpoints to differ sharply.
Symptom
Gate results moved sharply and non-monotonically between checkpoints of the same run, and the last checkpoint was often not the best.
Context
One-leg V0r1: the 2,000 neighbourhood was best; from 2,500 on the nominal gates degraded (late overtraining); 2,000 itself had one real micro-hop (17.7 mm over 5 frames); 2,300 was all green and shipped. V0r2: failed cells per checkpoint 2,000:19, 2,100:38, 2,200:1, 2,300:3, 2,400:27, 2,500:12, 3,000:18 - 2,200 shipped. The recovery line learned the same from the other side: stopping v2_6 early at a scheduled point left a policy whose re-fall rate had spiked to 9-22% before consolidation healed it ("stop on signals, not on the schedule"), and a continuation's transfer decayed checkpoint by checkpoint while Isaac stayed perfect.
Change
The acceptance rule "scan checkpoints, do not look only at the last one" is written into the one-leg gates (called the S1 discipline); release candidates are chosen from the scan.
Outcome
Both one-leg releases were mid-run checkpoints (2,300 and 2,200) chosen by the full 40-cell battery.
Mechanism
PPO keeps changing the policy after the gates saturate; with no gradient toward the gate's conditions, later checkpoints wander, so gate quality is a noisy function of iteration.
Applies when
- picking which checkpoint of a run to export and stamp
- a run is stopped at a fixed iteration budget
- final-checkpoint results are worse than mid-run smoke tests
“Isaac 侧 S1 纪律: 验收扫 checkpoint，不是只看最后一个。 … 扫描判决: 2000 邻域最优——2500+ 标称面退化（⑤③② 散挂, 晚期过训）, 2000 有一例真微跳（L s100 μ1.2, 17.7mm/5帧）, 2300 全绿。”
git:Lucen V2@origin/oneleg-line:train/ONELEG_V0_SPEC.md § §6 验收门 / §8 核查单 5 与 7 IMU observation age cut 52-68 ms to ~4 ms by moving AHRS onto the MCU - as a single variable
imu-age-move-fusion-downstreamAudit observation age end-to-end and move time-critical fusion as close to the sensor as possible - and when you fix a latency, change only that one variable so the gain is attributable.
Symptom
IMU-derived observations reaching the policy were 52-68 ms old because attitude fusion ran in Python on the loaded host computer - stale attitude is a direct feedback-loop delay the policy was not trained with.
Context
The fix was scoped deliberately narrowly: move the AHRS computation from Python to the STM32 H7 (MC02). CAN topology explicitly unchanged, so the change is a clean single variable.
Change
AHRS fusion relocated Python -> H7. Before/after - IMU age: 52-68 ms -> ~4 ms; CAN timing: unchanged; Python load: high -> ~0.
Outcome
IMU age reduced by an order of magnitude with no confound; host CPU headroom recovered ("把计算单元搬在stm32上, 这样imu有剩余").
Mechanism
Sensor age is pipeline latency, not sensor quality: fusing on the MCU next to the sensor removes host scheduling jitter and interpreter overhead from the critical path. Keeping the bus topology fixed makes the improvement attributable to the relocation alone.
Applies when
- measured sensor-to-policy age far exceeds sensor sample period
- attitude fusion or filtering runs on a loaded host CPU in an interpreted runtime
- planning infrastructure changes during a sim2real campaign
“AHRS 搬到 H7——这个不改 CAN 拓扑，只是把一段计算从 Python 挪到 MC02，单变量：IMU age 52–68 ms → ~4 ms / CAN 时序 不变 / Python 负载 高 → ≈0”
Experience.md § AHRS 搬到 H7 (lines 28-35) Ungated phase shaping made standing 42x more expensive than stepping - and the stepping was cooking the hip motors
moving-gate-42x-stand-taxGate every phase/clock-driven shaping term on the command that justifies motion, price the cmd=0 case explicitly during design - and when a reward flaw is sim-only, book it with trigger conditions instead of operating immediately on a working lineage.
Symptom
At cmd = 0 the sim policy never stood still - it stepped in place and crept 0.98 m per 20 s; on the real robot the same lineage's stepping made hip_roll motors run 20 degC hotter than every other joint (43-48 degC vs 25-28).
Context
The arithmetic closed it: the three phase-shaping terms (joint_pos_ref 1.6, feet_contact_number 1.2, feet_clearance_swing 1.6) are driven by the gait clock with NO command gating, so standing at cmd=0 forfeits 3.32/step of shaping while honest standing earns only 0.078 of tracking - stepping wins 42x. The heat chain: perpetual stepping = perpetual single support = one hip_roll stalled at ~4.3 N*m (25% of torque limit) carrying the torso's frontal-plane moment - two hip_rolls = 90% of whole-machine steady-state I2R; measured stand-vs-step comparison: total heat -71% when actually standing. The suspended test acquitted the actuator (0.21 rad sag -> 0.0008 rad in air) and mechanics vetoed the easy fix ("降 kp 救不了热" - equilibrium torque equals the external load regardless of kp). The fix (moving_gate: hard-gate the three shaping terms on |cmd| > eps) was designed - then DEFERRED by the user because the real robot at the time stood fine: "真机不表现该问题, 为真机不存在的病改奖励表不划算", with written trigger conditions (C-ladder stand row persistently failing, or real robot starting to step/drift at cmd=0) and the known hazard tag (this is exactly the reward-change class that triggers the B-arm signature). When the real robot later DID step and cook, the booked trigger fired and moving_gate moved from debt to to-do with its benefit re-priced: "停止空烧 hip_roll,稳态发热降 ~71%".
Change
moving_gate designed with the gate_by_cmd convention; deferral, triggers, and expected heat recovery all pre-registered instead of patching the reward for a then-sim-only symptom.
Outcome
The cmd=0 stepping went from mystery to closed arithmetic; the thermal measurement (hip_roll +20 degC) quantitatively confirmed the 90%-of-heat prediction; the reward change waited for real-world justification instead of spending a risky revision early.
Mechanism
Clock-driven shaping terms define a perpetual-motion bounty unless gated by command; the resulting idle gait is not a training bug but the table's optimum. Its cost surfaces on hardware as stall-torque heating set by statics (mass x lateral offset), which no gain change can remove - only removing the motion (gating) or widening the stance can.
Applies when
- the policy steps in place or creeps at zero command
- specific joints run hot in idle behaviors
- deciding when a known reward flaw justifies a risky mid-lineage fix
“塑形合计 −3.32/步 … 净: 站定亏 42 倍 … 两颗 hip_roll 4.29 / 4.09 N·m(各占限扭 25%),占全机稳态 I²R 的 90% … 吊挂实测 hip_roll 跟踪误差 0.21 rad → 0.0008 rad … 降 kp 救不了热 … 真机不表现该问题, 为真机不存在的病改奖励表不划算”
train/README.md § C1 FAIL 节 (cmd=0 的 sim/真机分歧记账) / C2 真机 A/B 三b 发热定性 Raising a command bucket's share does not strengthen its per-state gradient - it only starves the other modes
bucket-share-is-not-a-gradient-leverWhen a skill is not learning, first prove its per-state signal is nonzero (ignore-floor and probe checks); only rebalance sampling shares to fix genuine sample starvation, and account the regression risk to the diluted modes before doing it.
Symptom
Sidewalk was not learning, and the reflex proposal was to give the side bucket a larger share of sampled commands.
Context
The C4-redo3 rung explicitly kept the 20/40/20/20 bucket (stand/forward/turn/side) with the reasoning written out: PPO computes advantages per state, so bucket proportion does not change the per-state gradient of side states; at 4096 envs x 20% x 24 steps the rollout already contained ~19.7k sidewalk states - sample count was not the bottleneck. And the cost side was already measured: cutting forward from 60% to 40% had made vx+0.30 die at +400 in an earlier run - more cuts would only collapse it sooner.
Change
Bucket proportions held constant across the entire C4 redo series; the actual bottlenecks (metric frame bug, reward variance penalty, exploration form) were pursued instead.
Outcome
Sidewalk was eventually fixed with zero bucket changes (feed-forward delivery, +100 iters); forward/turn skills never suffered starvation-induced regressions during the redo series.
Mechanism
Policy-gradient credit is assigned per visited state; oversampling a mode multiplies its states in the batch but not the informativeness of each, so if the per-state gradient is ~0 (behavior unreachable or reward indifferent), N times zero is still zero - while the displaced modes genuinely lose data and regress.
Applies when
- proposing to oversample a failing task/command mode
- a majority mode regresses after share rebalancing
- budgeting env count vs mode share for a multi-skill policy
“比例不动：PPO 逐状态算优势，桶占比不改变单状态梯度；4096 env × 20% × 24 = 每 rollout 已有 1.97 万个侧走状态，样本数不是瓶颈；而 forward 60%→40% 已实测让 f30 在 +400 处死掉，再加码只会更早塌。”
train/C_LADDER_RUN.md § 3i. 桶 20/40/20/20 不动（比例不动） Training-log reward values and fixed-command eval values live on different distributions - comparing them once claimed a 44% improvement that was really 6-10%
same-distribution-reward-comparisonQuote reward-term values only with their distribution attached (command range, DR on/off, environment), and compare across runs only when those match; re-measure in a common environment before claiming any improvement percentage.
Symptom
A v6-era analysis concluded slip had dropped 44% by comparing the training log's Episode_Reward against values calibrated in a fixed-command play environment; a same-condition re-measurement showed the true improvement was 6-10%.
Context
The training log's reward is an expectation over the training command distribution (vx 0.15-0.5, yaw +/-0.6, with pushes and domain randomization); play-environment calibrations are taken at a single fixed command with DR off. Subtracting one from the other compares apples to oranges - the warning was written into the v7 pre-flight: "奖励数值只能在同一指令分布下比较 … 拿它和固定 cmd 的 play 环境标定值相减会得出错误结论(v6 那轮已经栽过一次)".
Change
Rule adopted: any before/after reward-term comparison must hold the command distribution, DR state, and evaluation environment fixed; training-log values compare only against training-log values of runs with identical command/DR configs.
Outcome
The phantom 44% improvement was retracted; later term-level accounting (e.g. the C4 ignore-floor work) consistently specified its distribution before quoting numbers.
Mechanism
A reward term's expectation depends on the visited-state distribution as much as on the policy; changing the command distribution or DR moves every term's baseline. Cross-distribution differences therefore measure the distributions, not the policy change.
Applies when
- comparing reward telemetry across training runs or vs play evals
- claiming improvement percentages from training logs
- term-level reward accounting for diagnosis
“奖励数值只能在同一指令分布下比较。训练日志的 Episode_Reward 是在训练指令分布上算的(vx 0.15~0.5 / 偏航 ±0.6 / 带推力与域随机化), 拿它和固定 cmd 的 play 环境标定值相减会得出错误结论(v6 那轮已经栽过一次: 据此以为滑移降了 44%, 同条件对拍只有 6~10%)。”
train/WALK_V7_SPEC.md § 3. 开训自查 ⚠️ Cross-simulator gate (Isaac Lab to MuJoCo) comes before any hardware attempt
sim2sim-gate-before-sim2realGate every policy through a second simulator with an independently built plant before hardware; treat sim2sim failure as a contract or overfitting bug, and sim2real failure after a sim2sim pass as a plant/actuator gap.
Symptom
A policy that only ever ran in its training simulator carries untested dependencies on that simulator's solver, contact model, and defaults; the first place those dependencies surface should not be the real robot.
Context
Standing order of operations for the whole Lucen program, recorded as the opening line of the experience log: train in Isaac Lab, gate in MuJoCo, only then go to hardware. The MuJoCo side is the same plant used for evaluation batteries, so a sim2sim pass also validates the exported policy + contract (obs ordering, scales, defaults) outside the training stack.
Change
Pipeline rule adopted - every checkpoint must pass the MuJoCo evaluation battery (sim2sim) before it is considered for real deployment (sim2real).
Outcome
Used generation after generation as the cheap filter; hardware sessions only ever received policies that had already survived a second simulator.
Mechanism
Two simulators disagree exactly where a policy is overfit to simulator-specific artifacts (contact softness, integrator, default parameters, obs conventions); a cross-sim transfer catches contract bugs and solver overfitting at zero hardware risk, so real-robot failures that remain are attributable to genuine plant/actuator gaps.
Applies when
- planning the path from training to first hardware trial
- exported policy behaves differently outside the training framework
- triaging whether a real-robot failure is contract vs plant
“先sim2sim - 从isaaclab 到mujoco / 再sim2real”
Experience.md § opening lines (1-2) The get-up kept getting faster because standing earlier paid more every step - lowering torque authority barely slowed it, and only zeroing the standing income for the first 3 s moved the pace into the design band
per-step-income-drives-speed-time-gateWhen a skill is too fast, find the term that pays for finishing early and gate that income by time; keep the "get into position" term ungated so the policy does not learn to wait, use a ramp instead of a cliff, and confirm with a paired same-level experiment that the drift is motivational before changing it.
Symptom
The user judged the get-up too fast (Isaac medians about 0.7-1.6 s) and suspected path dependence: the policy seemed to get faster the longer it trained.
Context
Lowering the beta authority 0.40 -> 0.30 cut impact but moved supine only 1.70 -> 2.00 s: coordination-limited, not torque-limited. A paired experiment inside one beta level (checkpoint 15,600 vs 18,499, +2,900 iterations, same ruler) measured the drift: get-up medians -7 to -10%. The spec concluded the motive, not the path, was the cause - per-step standing income pays for every early step, and any lineage (even one from scratch) races toward the fastest solution inside its constraints.
Change
V2.5: the standing income (base_height, stand_pose, still, feet_on_ground) multiplied by w(t) = clamp(t/3 s, 0, 1); upright deliberately NOT gated, so righting and sitting up early still pay and the policy is not taught to lie flat and wait; a ramp, not a step. V2.5b: zero before t0 = 3 s, then a 1 s ramp.
Outcome
V2.5: Isaac 100%, get-up +17-43% slower, MuJoCo 100/100/98/100% (the best cross-simulator reading yet), still short of the 3.5-4.5 s design band - a linear ramp only discounts early income. V2.5b: MuJoCo supine 2.04 -> 4.10 s and prone 3.18 -> 4.04 s, inside the band; the Isaac pace barely moved (a lineage habit on a gradient-free plateau). Later the zero gate proved harmful when trained from scratch (curriculum-history-is-part-of-the-product) and in the V3.1 lineage (time-gate-vs-wide-stance-retire-the-fix).
Mechanism
Constraints on authority or velocity change how the fastest solution looks; the time structure of the task income decides how fast the fastest solution is.
Applies when
- a policy is faster or more aggressive than wanted and constraints do not slow it
- progress-style rewards pay every step spent at the goal
- performance drifts faster with more training at fixed settings
“**V2.5 机制(唯一)**:站立收入(base_height/stand_pose/still/feet_on_ground) 乘时间斜坡 w(t)=clamp(t/T_gate,0,1),T_gate=3.0 s;**upright 刻意不门控** (翻正/坐直早期照常拿钱,防"躺平等门开" … **用户假设量化 证实:逐步计酬动机在 β 包络内持续压缩时间,约束挡不住动机 —— V2.5 动机层 修法为正解。**”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §39 V2.5 预注册 / §39 补 配对实验 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) Under continuous 3-axis uniform sampling, pure straight-line walking is a zero-measure event the policy never trained
zero-measure-commands-need-mode-samplingEnumerate the exact command points users will actually issue (straight, stop, in-place turn) and give each explicit probability mass via mode sampling with off-axes pinned to zero - never assume a continuous sampler covers its measure-zero subsets.
Symptom
"The robot drifts even in sim when told to walk straight" persisted across reward tunings - because with commands drawn as vx in [0.15,0.5] x vy ~ U(+/-0.2) x wz ~ U(+/-0.6), the event vy=0 AND wz=0 has probability zero: pure straight-line walking was never sampled even once.
Context
Restart evidence item #3: "纯直行是零测度点 … 'sim 里直行就漂'是分布的 必然,不是 reward 没调好" - the drift metric was legitimately drowned by commanded turning (v11's own comment self-documented this). The structural fix is discrete mode sampling: a custom ModeVelocityCommand that first draws a mode by share (stand/forward/back/turn/side/mixed), then draws values only on that mode's axes with all others pinned to exact zero - which is also what preserves single-variable discipline in the C ladder (native 3-axis uniform "采不出'离散模式桶' … 把 C1~C4 的单变量纪律直接毁掉"). The mixed mode later got an ellipsoid constraint rather than a cube for the same reason in reverse - corner combinations of a cube are unrepresentative extremes.
Change
Command generation moved from independent per-axis uniforms to mode-bucket sampling with pinned-zero off-axes (plus 20% rel_standing); acceptance likewise evaluates per mode.
Outcome
Straight-line behavior became a trained, testable mode instead of a measure-zero hope; the C ladder could add one mode per rung with provable isolation.
Mechanism
A policy optimizes expected reward under the command distribution; events of probability zero contribute nothing to the objective, so exact-zero-command behaviors (straight walk, stand, in-place turn) are only learned if the sampler gives them mass. Product-of-uniforms distributions concentrate mass on mixtures and give none to the pure behaviors users actually command.
Applies when
- a "simple" command (straight, stop) underperforms mixtures in sim
- designing command distributions for velocity-tracking tasks
- a ladder needs per-mode isolation for attribution
“纯直行是零测度点:最终 command 为 vx∈[0.15,0.5] × vy∈U(±0.2) × wz∈U(±0.6) 连续均匀,vy=0∧wz=0 从未被专门采样 —— "sim 里直行就漂"是分布的必然,不是 reward 没调好 … Isaac 原生 UniformVelocityCommand 是三轴各自 uniform,采不出"离散模式桶"”
train/OMNI_V0_SPEC.md § 0. 为什么从零 (3) / 三件前置 (1) A reward on a quantity the actor cannot observe teaches "produce less of it", never "correct it" - closed-loop correction needs an outer loop
reward-observability-limitBefore adding a reward, check the actor can observe (or infer) the quantity: unobservable-error rewards buy only average suppression - route correction tasks to an outer loop whose commands stay in distribution, and do not break a frozen contract to add an observation a deploy-side loop can supply.
Symptom
Heading kept drifting despite world-frame yaw rewards, and a reviewer proposed heading-error rewards - raising the question of what yaw shaping can even teach this actor.
Context
The adopted architectural verdict: the actor's 45-dim base observation cannot see accumulated heading at all - projected_gravity is invariant to rotation about the gravity axis, and omega_z is a rate, not an angle. World-frame yaw-rate rewards are therefore privileged shaping that can only teach "少产生旋转" (generate less rotation), never "偏了以后拉回原线" (pull back to the line after drifting) - the policy cannot represent the error it would need to correct. The S1 gate (<=5 deg / 10 s) demands exactly the former, so the stack is right for its gate; active heading correction is assigned to the deployment outer loop (--heading P-loop converting heading error into in-distribution wz commands) plus small-wz training - and the 215-dim contract is explicitly NOT extended with a heading observation ("契约不加 heading 观测,冻结不动"). The reviewer's companion bias hypothesis was adjudicated with data: drift is bimodal - a basin mechanism decides whether you leave (seeds vary +/-16-46 deg vs -385 to -391 deg), and once out, rotation direction is constant (weight chirality; candidate root: the phase clock always swings left first).
Change
Yaw shaping kept as rate-tracking (three-layer stack); heading correction owned by the deploy outer loop; contract frozen; the "which behaviors need an outer loop" question settled by observability analysis rather than reward tuning.
Outcome
Stopped a contract change and a futile reward direction; drift work split correctly into rate-suppression (trainable) and error correction (outer loop), consistent with the earlier measured 10x drift reduction from the deploy-side loop.
Mechanism
A policy can only condition on its observation sigma-algebra; rewards on functions outside it shift the marginal action distribution (open-loop average effects) but cannot create feedback on the unobserved variable. Whether to add an observation, an outer loop, or accept average-shaping is decided by the task's gate: suppression gates need shaping, correction gates need the variable in some loop's view.
Applies when
- adding rewards on accumulated/世界-frame quantities (heading, position)
- deciding between a new observation, an outer loop, and shaping
- a drift symptom persists across reward-weight changes
“actor 的 45 维基座观测不到累计航向(projected_gravity 对绕重力轴旋转不变,ωz 是速率不是角度)——世界系 yaw 奖励是特权塑形,只能教「少产生旋转」,不能教「偏了以后拉回原线」。… 主动纠偏闭环 = S3 把小 wz 进分布 + deploy --heading 外环 … 215 契约不加 heading 观测,冻结不动。”
train/OMNI_V0_SPEC.md § 3. 评审④判决(2026-08-06,S1.3 开训前) Compare the achieved reward to the computed ignore-floor to tell "never learned" from "learned but unprofitable"
ignore-floor-diagnosisFor any skill that trains flat, compute the reward the null policy would earn on that term; achieved==floor means the behavior never paid out (find why: exploration, reward observability, or feasibility) - do not tune weights first.
Symptom
C4 sidewalk failed on both arms; the question was whether the policy had found sidewalk and rejected it as unprofitable, or never found it at all - two diagnoses with opposite fixes.
Context
The theoretical value of the sidewalk tracking term for a policy that completely ignores the command was computable from the command distribution: 0.189. Trained final values landed at 0.187 (arm A) and 0.204 (arm B) - sitting exactly on the ignore-floor - while the honest balance at the optimum actually favored sidewalking (net +0.88/step inside the side bucket). Later the same arithmetic closed the whole saga: for the true reward landscape, doing real sidewalk scored 0.153 vs 0.944 for ignoring - the policy's refusal "是理性最优，不是探索失败" (rational optimum, not exploration failure) under one hypothesis, and under the final measurement-corrected account the policy had "每一次都在 做理性选择" (made the rational choice every time).
Change
Diagnostic rule adopted: compute the ignore-floor for the new term; if the achieved value sits on it, the behavior was never expressed in useful volume (or the reward cannot distinguish it - check both); if the achieved value is above floor but the behavior is absent at deployment, the policy sampled it and priced it out - then the reward balance, not exploration, is the lever.
Outcome
Correctly identified that PPO had not merely under-valued sidewalk; each subsequent hypothesis (waveform sign, regularization cage, exploration form, reward kernel) was tested against this floor arithmetic, which kept the search honest through three reversals.
Mechanism
Every reward term has a computable value under the null behavior; the achieved-vs-floor gap is a one-number audit of whether the optimizer ever monetized the target behavior. It converts "training failed" into one of two mechanistically distinct states with different fixes.
Applies when
- a new skill's tracking reward plateaus early
- deciding between exploration fixes and reward-weight fixes
- post-mortem of a failed curriculum rung
“track_lin_vel_y_exp 训练终值恰好坐在「完全无视指令」的底分上（A 0.187 / B 0.204，理论值 0.189），而终点 balance 明明有利（side 桶内净 +0.88/步）。不是学会了不划算，是根本没学到。”
train/C_LADDER_RUN.md § 3e. 为什么首战 FAIL A DR tail the robot never has is pure cost - stage deterministic plant levels instead of one wide uniform
dr-tail-plant-continuationSet every DR range from the measured deployment distribution and cut tails that hardware cannot produce; when an axis changes the controller's character (delay, major gain regimes), prefer staged deterministic levels with gates over one wide uniform.
Symptom
Two consecutive lineages (s1e, s1f) trained under uniform latency DR (0, 0.06 s = 0-3 frames) both converged to drag-glide gaits - buying survival under heavy delay by giving up swing (3.6 mm) - even though the real pipeline never exceeds ~2 frames.
Context
The account: roughly 1/3 of training quality was spent on the >2 frame tail that hardware never presents ("uniform 尾部 ~1/3 训练质量 花在真机不出现的 >2 帧上"). The deeper reading came from the user: uniform 0-3 frames is not merely tail-heavy - it "把性质不同的控制系统 混进同一 PPO batch" (mixes qualitatively different control systems into one PPO batch); a 0-frame and a 3-frame plant demand different controllers, and one policy trained on the mixture serves neither. The S2 v2 ladder therefore redefined latency "从「随机化参数」重新定 义为 actuator/control plant 的一部分": deterministic FIFO levels, staged 1 frame then 2 frames (lo=hi so fractional interpolation degenerates to exact N frames, synonymous with the harness --delay N), each level gated by the fixed acceptance battery - a plant continuation, not a randomization.
Change
Latency DR replaced by staged deterministic levels covering the measured 1-2 tick reality with no tail; each stage a separate continuation rung with the standard gate and rollback.
Outcome
The s2_lag1 rung showed the clean-signal benefit immediately (survival 20/20, heading 6x recovery) with the swing cost booked honestly (21 -> 12 mm, half-pass, ladder paused for adjudication); the drag-glide attractor from uniform tails did not recur.
Mechanism
DR asks one policy to cover a plant family; when part of the family is fictitious, the policy pays real capability for fictitious robustness, and when family members demand structurally different controllers, gradient averaging produces a compromise controller optimal for none. A measured, discrete plant set matches the actual deployment support and keeps each rung's training signal coherent.
Applies when
- policies converge to degenerate gaits that buy worst-case survival
- a DR range extends well past the measured hardware range
- choosing between wide randomization and a staged ladder on an axis
“两轮实证(s1e/s1f)宽尾延迟 DR 逼出拖地滑行 … uniform 0~3 帧不止尾重,而是把性质不同的 控制系统混进同一 PPO batch;1→2 帧确定性分级 = plant continuation,训练信号干净得多—— latency 从「随机化参数」重新定义为 actuator/control plant 的一部分。”
train/OMNI_V0_SPEC.md § 4. v2 阶梯 (2026-08-07 用户定) A frame-history observation under zero DR memorizes the trainer's plant fingerprint - the estimator must see variation to learn estimation
history-obs-needs-plant-variationIf the observation carries history (stacked frames, RNN), keep at least minimal plant variation (gain/latency jitter) on from the first iteration - "nominal first, robust later" is structurally invalid for estimator-bearing contracts.
Symptom
omni_s1 (fresh 215-dim contract with a 5-frame history window, trained with DR fully off): training all green, yet the MuJoCo gate scored 0/20 on all eight doors - falls within 2 s, seven checkpoints, not one transferred.
Context
The history window exists precisely to let the actor implicitly estimate line velocity and actuator dynamics (the actor is denied base_lin_vel by observation honesty). Under a constant plant that implicit estimator has nothing to estimate - it learns the trainer's exact response fingerprint instead, and any other simulator's micro-differences are out-of-distribution: "5 帧窗按设计就是隐式估计器, plant 恒定时它学到 Isaac 精确响应的指纹". The planned "nominal-first-robust-later" staging was declared STRUCTURALLY incompatible with history observations: "估计器要见过变化才学估计, 否则学背诵" (an estimator must see variation to learn estimation, otherwise it learns recitation). Honest confound note kept: this is mixed with "zero DR does not transfer, period" - but both attributions prescribe the same fix, so no control was run.
Change
S1.1: minimum actuator jitter turned on from day one - kp/kd +/-10%, latency 0-1 frame (friction/COM/mass still nominal, no push - those stay for the S2 ladder).
Outcome
Transfer restored: survival 0/20 -> 20/20, speed 19/20, foot distance 20/20 (remaining failures moved to gait quality, a different disease); the staging doctrine was amended - history-carrying contracts never train under a frozen plant.
Mechanism
A recurrent/history channel fits whatever temporal structure minimizes loss; with a deterministic plant the cheapest structure is the plant's own impulse-response signature, yielding features that are simulator-specific rather than physics-general. Plant variation forces the channel to carry state-estimation features that transfer.
Conflicts
Attribution is explicitly confounded with the simpler "zero DR never transfers" reading ("与「零 DR 本身就不迁移」混杂 … 两种归因处方相同, 不做对照") - the source chose not to spend a control run separating them.
Applies when
- adding frame stacking or recurrence to an actor observation
- a nominal-plant policy fails a cross-simulator gate within seconds
- planning DR staging for a new contract
“frame_hist × 零 DR = plant 指纹过拟合——5 帧窗按设计就是隐式估计器, plant 恒定时它学到 Isaac 精确响应的指纹, MuJoCo 的微小差异即 OOD, 2 s 内摔, 七个 checkpoint 无一迁移。「先标称后鲁棒」的分段与历史观测结构性冲突:估计器要见过变化才学估计,否则学背诵。”
train/OMNI_V0_SPEC.md § 3. S1.1 修订记录 ① When a contract default changes, old policies must run under a pinned legacy profile - a silent clock swap is out-of-distribution on hardware
legacy-profile-pinningTreat every trained policy as bound to the contract values of its training era: version the deployment profiles, pin old policies to their era's profile in every command, and never let a changed default silently apply to an old artifact.
Symptom
The walk profile's gait clock moved from 0.40 s to 0.50 s for new training, but versions v5-v9 were all trained at 0.40 s - running them under the updated default would silently feed a 25% slower phase clock to policies that never saw one.
Context
The re-test runbook hard-codes --policy-profile legacy_walk_040 into every command for the old versions, with the warning not to omit the flag: the mismatch is invisible (no error, no crash) but puts the policy out of distribution on hardware, where the same file had already documented that off-clock operation collapses gait quality.
Change
Deployment profiles versioned per training era; historical policies permanently associated with their era's profile; runbooks write the profile flag explicitly rather than relying on defaults.
Outcome
Old policies stayed runnable and comparable after the contract moved on; the silent-mismatch failure mode was closed by convention.
Mechanism
Changing a shared default rebinds every old artifact to a contract it was not trained under; unlike a schema break, a value change produces no error - only degraded, unexplainable behavior. Version-pinned profiles make the binding explicit and permanent.
Applies when
- changing any default in the deployment contract (clock, scales, gains) while old policies remain in use
- writing runbooks that mix policy generations
- a re-tested old policy behaves worse than its era's records
“2026-08-02 起 walk profile 的时钟改为 0.50(WALK_V10_SPEC §3)。v5~v9 全是 0.40 训的,本文件所有命令已改带 --policy-profile legacy_walk_040 ——不要省掉这个 flag,否则是拿慢 25% 的相位时钟静默喂旧策略(分布外,真机危险)。”
train/REAL_SWEEP_V5_V8.md § 1. 预检 ⚠️ 时钟改为 0.50 The first real-robot get-up was "very violent, kicking on the floor, dangerous" - a sim-perfect policy with no reason to be slow, unbounded absolute targets, no domain randomization and a rate limiter that filtered nothing; the task was restated as "safe, slow, transferable"
first-real-get-up-violent-stage-one-policyDo not put a get-up policy on hardware until its action is bounded (hard bound or state-anchored targets), smoothed, randomized and tested at the real pipeline's latency, and say explicitly that the task is "safe, slow and transferable" - a simulation-perfect policy optimizes only "gets up".
Symptom
On 2026-08-09 the user ran a V0-lineage recovery policy on the real robot and stopped it: very violent, kicking on the floor, dangerous. The planned next rung (a heavier torque_headroom) was never started.
Context
The spec had pre-registered that R0/R1 products stay in simulation and that the real-robot precondition was the R3 smoothing rungs plus a bridge-slew check plus a hanging protocol; the robustness (DR) rungs had not run. In simulation the policy passed 100% with a get-up of about a second. Which ONNX, which gain profile and whether a torque/joint log existed were left "to be recorded later" and never were.
Change
The V0 ladder was stopped at its best product (R3.1, sim only) and a re-rooting proposal was put to the user. The spec's four-layer account: style (the reward pays for standing early and nothing pays for slowness - HumanUP's "Stage I" get-up, "fast but unsafe ... infeasible for real-world deployment"); impact (full-range absolute targets with no hard bound, raw |a| up to 4.77, action saturation 100%, a single-step change of 0.306 saturating hip_pitch); transfer (zero DR, friction pinned at 1.0, the learned leg bracing); link (the bridge's RL slew equals vel_limit, 0.2-0.66 rad per step, while the real pipeline has 1-2 steps of time-varying latency and acceptance ran at delay 0).
Outcome
The line was re-rooted twice (training-side rate limit, then the beta-anchored action space) and gained a hang protocol before the next real attempt; on 08-11 a beta-anchored policy produced the line's first real get-up.
Mechanism
A task reward that pays for standing early selects the fastest feasible get-up; with absolute full-range targets every large target jump is a torque impulse bounded only by the clip; zero DR and braced-leg solutions do not transfer; and a limiter set at the velocity limit does nothing at 50 Hz.
Conflicts
The four layers are the spec's reconstruction from simulation probes and the literature; the real run's policy file, gain profile and log were never recorded, so no layer was confirmed against hardware data.
Applies when
- a first hardware trial of a high-effort skill is being scheduled
- sim success is high but the policy saturates actions or torques
- pre-registered hardware preconditions are not all met
“用户真机反馈:**非常猛、地上乱踢、危险**,叫停(R3.3 torque_headroom 加档已选型 weight −0.5→−1.5,未启动)。真机细节(哪个 onnx、什么档、有无 τ/q log)**待补记** … 任务从"能起来"变成 **"安全、慢、可迁移"** … **链路层**:桥层 slew RL 档 = vel_limit(10/20/33 rad/s ≈ 每拍 0.2~0.66 rad), 对 recovery 形同虚设;真机 1~2 拍时变延迟,验收默认 delay 0。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §26 真机叫停与换根判决(2026-08-09) Four consecutive fixes were each continued from the previous fix's degraded state until the user stopped the ladder - "change parameters, don't stack errors" - rolled back to the last good checkpoint and audited the target geometry first
stop-stacking-roll-back-and-auditWhen successive rungs each start from the previous rung's output and the target symptom does not move, stop, roll back to the last good checkpoint and re-derive the next change from an audit; keep the measurements, discard the stacked remedies.
Symptom
After the real-robot splits, the V2.7 ladder tried to widen the stance: a term swap (A), a new stance knife (b), more iterations (甲), a doubled weight (乙). Stance barely moved while hip yaw ratcheted 46.7 -> 49.9 -> 52.5 deg toward its 60 deg limit.
Context
Each rung started from the previous rung's output. The user ruled on 2026-08-11 that things had gone wrong from V2.7-A: go back to v2_6 and rethink which parameters to change instead of stacking errors.
Change
乙 was killed at start and not counted; the product baseline rolled back to v2_6c model_29399; every measurement and law learned on the ladder was kept ("the data is real; what stacked was the treatment"). Before any new training, a zero-training kinematic audit of the stance targets was run.
Outcome
The audit found the stand_pose target itself rewarding the narrow stance (pose-target-geometric-audit) and showed geometrically why the yawed stance could not be widened with flat feet - so 乙 was proven unnecessary without running it. The next in-lineage attempts still failed, which is what established that the stance is set by the get-up path.
Mechanism
A rung continued from a degraded state inherits its compensations, so each new fix answers the previous fix's side effects; the yaw ratchet was the visible trace of that stacking.
Applies when
- three or more corrective rungs in a row without progress on the target metric
- a side-effect metric ratchets in one direction across rungs
- a new rung is being planned from the latest (not the best) checkpoint
“用户裁:"从 V2.7-A 开始就出问题了,应该回到 2.6 再思考如何改变参数而不是 错误叠加。"认账:A 的补丁 → b 的新刀 → 甲的加时 → 乙的加权,每级都从上级 的**退化状态**续(yaw 46.7→52.5° 的棘轮就是叠加痕迹)。 … 数据是真的,叠加的是处置。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §44 方法论裁定(2026-08-11,用户):V2.7 全阶梯叫停,回滚 v2_6 Removing a hand trim re-exposed the plant offset it had been silently compensating - and a slope scan told bias from sensitivity
hand-trims-hide-plant-offsetsTreat hand-tuned trims as undocumented plant measurements: before deleting one, find what it compensates and re-house that knowledge in the model or the reward budget; diagnose posture errors with a sensitivity sweep to distinguish constant bias from gain problems.
Symptom
After switching from the old hand-trimmed default to the clean geometric zero, the retrained stand policy's only regression was torso lean: 1.8 deg -> 4.1 deg backward.
Context
The old default's ankle-pitch trim (-0.0489/+0.0628) had been pre-compensating a fore-aft COM mismatch; removing the trim removed the hidden compensation, and the posture reward alone was too weak to win it back. A COM sensitivity scan settled what kind of problem this was: sweeping base COM offset -50 to +50 mm gave nearly identical slopes for old and new policies (~0.026 deg/mm) - "不是质心敏感度问题, 是恒定偏置" (not a sensitivity problem, a constant bias). Fix landed in stand_v1b: posture corrected to +0.24 deg while keeping symmetry (<=0.1 deg) and low effort (0.259), disturbance rejection better than both predecessors. Model credibility was checked the honest way: v0's sim prediction at the real COM position (-22 mm) was -2.31 deg lean vs real measured 2.2-3.1 deg - "预测精准命中" - which is what licensed trusting v1b's -0.52 deg prediction. (Side flag from the same file: a sign convention had been documented wrongly in early comments - gravity_base[0] > 0 is forward lean.)
Change
Trims retired in favor of explicit modeling: symmetric geometric default plus a posture-reward budget sized to carry the real COM offset; the offset itself known (real COM ~22 mm behind model).
Outcome
stand_v1b passed acceptance as the standing lineage's final version; the walk-line requirement "加大躯干姿态惩罚权重" was upgraded from suggestion to mandatory, since walking amplifies what standing tolerates (real walk_v1 hit 26 deg lean vs sim 7.4).
Mechanism
Hand trims are plant knowledge stored in the wrong place - invisible, asymmetric, and stale after recalibration; removing them re-exposes the raw plant error. A sensitivity sweep separates the two possible diagnoses (slope change = control problem; parallel offset = constant plant bias), each with a different fix.
Applies when
- cleaning up hand-tuned offsets/trims in defaults or calibration
- a posture bias appears after a default or calibration change
- deciding whether a lean is a COM-sensitivity or constant-offset issue
“两者斜率几乎相同(≈0.026°/mm),v1 只是整体多后仰约 2.4° —— 不是质心敏感度问题,是恒定偏置。成因:旧 default 的踝俯仰 trim(−0.0489/+0.0628)本就预补偿了前后质心偏差,换成零位 default 后这份补偿没了 … v0 在真机质心处(−22 mm)的 sim 预测为 −2.31° 后仰,真机实测 2.2~3.1° 后仰 —— 预测精准命中。”
train/RETRAIN_v2.md § 4b. stand_v1 独立验证结果 / 4c. stand_v1b 验收结果 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) Deployment power derating damages non-forward axes far more than forward - sweep it in sim before deploying
power-scale-hurts-nonforward-axesTreat deployment power/torque scaling as a plant parameter: evaluate the policy in sim at the exact deployment scale, expect non-dominant axes to degrade first under derating, and either deploy at the training power or train with power randomization.
Symptom
Policies deployed at power-scale 0.8 (a safety derating of commanded torque) looked fine walking forward but were weak at backward and turning, inviting the wrong diagnosis "the skill was not trained well".
Context
Measured repeatedly: on s1e, going 1.0 -> 0.8 cost forward 18% but backward 58%; on C4-ff800, turn tracking was +25%/+40% at pw0.8 vs +75%/+58% at pw1.0, backward 51-52% vs 97-103%, while forward stayed 96-98% at both. Sim evaluation numbers in the plan were all pw1.0, but the robot was being run at 0.8.
Change
Pre-deploy protocol added: sweep the exported policy across power in sim (for PW in 0.8 0.9 1.0: eval_c_matrix --power $PW --seeds 20) and deploy at the first level where both turn directions reach >=50%. For C4 the recommendation was raise the robot to pw1.0 - the sweep showed it nearly free (saturation 47%->33%, left foot-clipping danger zone 25%->6%, cost only tilt 6.7->8.3 deg).
Outcome
Turning "weakness" resolved without any retraining; the sim sweep correctly predicted the real-robot signature at both power levels.
Mechanism
Forward walking is the reward-dominant, torque-cheapest skill with the most margin; backward/turn/sidewalk live closer to the torque envelope, so a uniform torque derating consumes their margin first. Training ran at power 1.0 (the trainer does no power scaling), so deploying at 0.8 is a systematic underactuation the policy never experienced.
Applies when
- deploying with any torque/power derating or safety scale
- secondary skills (backward, turn, lateral) underperform on hardware while forward walking looks fine
- choosing the deployment power level for a new policy
“power 衰减对非前进轴的伤害远大于前进轴（s1e：前进 1.0→0.8 掉 18%，后退掉 58%）。转向是非前进轴，0.8 下很可能明显跟不动。”
train/C_LADDER_RUN.md § 3c. A-2 上机前先定部署力度档 / 3p. 二 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) A pull-assist curriculum keyed to a global success share was satisfied by the categories that already worked and withdrew before prone learned anything - conditioning the criterion on prone took it from 2.5% to 98.7%
curriculum-criterion-conditioned-on-lagging-categoryMeasure a curriculum's advancement criterion on the population the scaffold is meant to help; a global success share is met by whatever already works, and the help is withdrawn before the lagging case learns.
Symptom
Under the beta-anchored action, supine and side stood reliably while prone still sat (1.3%). A pull-assist curriculum added to help it was withdrawn completely within ~790 iterations and prone moved only to 2.5% (noise).
Context
The pull assist follows HoST: an upward force on the base, active only when the torso is within 30 deg of vertical, scaled by body weight (HoST's 200 N on G1 = 0.583 BW -> 56 N here, steps of 5.6 N, ten levels to zero); the product must pass with no assist. It had already taught sit-to-stand in V1. In V2.1 its advancement criterion was the standing-time share over all envs (threshold raised to 0.55 because the share was already ~0.53).
Change
V2.2: PullAssistForce with gate_category = "prone" - only envs whose first step classifies them as prone count toward the criterion - and the threshold back at 0.35. A feasibility signal was pre-registered: if prone's share stayed near zero under the full 56 N, return to the roll-over path instead of adding force.
Outcome
The prone-conditioned curriculum withdrew level by level only as prone itself passed: prone 98.7%, and the four-category gate passed for the first time on the line (98.6% overall, re-falls 0%, torque gate PASS).
Mechanism
A pooled success share is filled by the categories that already succeed (supine/side ~53%), so the scaffold is removed on their account before the lagging category has used it.
Applies when
- an assist, guide force or easier setting is withdrawn by a success threshold
- one task category lags while the pooled metric looks healthy
- a curriculum ran to completion without changing the lagging category
“**教训:全局站立占比阈会被存量类别(supine/side ~53%)凑够,拉力在 prone 学会前就撤光了 —— metric 设计失误,不是拉力机制失效**(它在 V1 教会过 坐→站)。 … **V2.2(已启动)**:`PullAssistForce` 加 `gate_category="prone"` —— 达标判据 只统计 prone 类 env”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §34 V2.1 判决(2026-08-10) 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 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 PPO's Gaussian noise cannot compose phase-locked oscillations - deliver them as feed-forward and let the policy learn the residual
feedforward-for-phase-locked-skillsIf a skill needs a temporally coherent (phase-locked) action component, do not expect step-wise exploration to find it: inject a verified feed-forward and train the policy as a residual stabilizer, keeping the feed-forward inside the deployment contract.
Symptom
Four different reward arrangements (no reference / wrong-sign reference / correct-sign reference / cage released) all failed to elicit sidewalk, while open-loop probes proved the behavior existed and was safe on the same platform with the same policy as base.
Context
Producing lateral velocity requires a phase-locked hip_roll oscillation synchronized to the gait clock. PPO's exploration is per-step, zero-mean, uncorrelated Gaussian noise - it can never compose a sustained phase-locked component, so the behavior is unreachable by exploration regardless of how it is rewarded. The fix changed the delivery channel: target = default + scale*action + lat_ff(cmd_vy, phi). The policy's action becomes a residual on top of the feed-forward, retaining full balance authority (it can even cancel the feed-forward); the feed-forward supplies exactly the component exploration cannot. This mirrors why the sagittal joint_pos_ref worked (it also delivered phase structure), just via a different channel.
Change
Contract-level change, done cleanly: new profile omni_ff (= omni + lat_ff_gain -0.5), existing omni profile bit-identical; feed-forward applied after the action delay stage; missing cmd/phase raises instead of silently dropping; deployment must use the same phi as build_obs (recomputing gives a one-tick phase misalignment).
Outcome
From C2-700, +100 iterations sufficed: product omni_c4_ff800 scored vy +120%/+125% (from +4%/-1%), 260/260 cells at 20/20 survival, zero old-skill regression, left/right gap 5 pp - the entire C4 saga resolved by changing the delivery mechanism, not the reward.
Mechanism
Exploration noise spans only the subspace its correlation structure can express; skills requiring coherent oscillation lie outside the span of i.i.d. per-step noise. Feed-forward moves the required structure into the action pipeline where it needs zero probability mass to appear, reducing the learning problem to stabilizing around a demonstrated behavior - which PPO does well.
Applies when
- a periodic/oscillatory skill trains flat under every reward variant
- open-loop injection of the behavior already works
- considering GRU/curriculum/exploration tricks for a rhythmic skill
“病因不在奖励，在探索形式：产生侧向速度需要相位锁定的 hip_roll 振荡，PPO 的逐步高斯噪声零均值无相关，合不出相位锁定分量。… target = default + scale·a + lat_ff(cmd_vy, φ)。策略动作因此是前馈之上的残差，保留全部平衡权限”
train/C_LADDER_RUN.md § 3j. C4-redo4：唯一变量 = 侧步参考改为前馈注入（契约级） Fine-tuning through a reward-table change was falsified (scatter, half-recover, collapse) - continuation training is legal only with the reward frozen
fine-tune-reward-change-falsifiedNever fine-tune through a reward-table change - retrain from zero; reserve checkpoint continuation for frozen-reward plant/DR widening, reset noise_std when branching, and watch for the scatter/half-recover/collapse signature as the abort trigger.
Symptom
The s1c A/B experiment: arm B fine-tuned from an existing checkpoint under the revised reward (same contract, same network, changed reward + small DR) and failed with a characteristic signature - scatter, half-recover, fall back ("打散→半恢复→摔回"); arm A trained from zero under the same config won decisively (full shaping lifted swing to 21.6 mm within 500 iters; shipped at 5500).
Context
Verdict recorded: "从零 + 强塑形是本机唯一验证过的发育路径" (from-zero plus strong shaping is this machine's only validated development path). The signature became a standing stop criterion in every later rung that touched a reward ("s1c B 臂签名,出现即停"). Crucially the boundary of the law was drawn explicitly when S2 continuation training was proposed: "当年证伪的是「奖励表中途改版的 fine-tune」… S2 续训奖励表全程冻结、只逐级加宽 plant/DR,属另一类" - continuing a checkpoint with the reward FROZEN while widening plant/DR one rung at a time is a different class and was allowed (and then worked, powering the whole S2/C lineage) - with the honest fallback that if frozen-reward continuation ever collapses, that rung retrains from zero and the doctrine gets re-examined with data. Fine-tune arms also need mechanical care: reset the checkpoint's collapsed noise_std (terminal 0.033 "会杀死探索") and account for iteration counters re-zeroing (curriculum gates fire immediately).
Change
Reward changes and lineage continuation permanently separated: reward revisions -> from-zero retrain; plant/DR widening -> frozen-reward continuation with per-rung gates; the B-arm signature promoted to a universal tripwire.
Outcome
No later reward revision was attempted by fine-tune; frozen-reward continuation carried S2 (PD/COM/friction rungs) and the C command ladder successfully from the s1e root.
Mechanism
A trained policy sits in an optimum of its reward's geometry; changing the reward moves the optimum but leaves the policy's exploration noise near-zero and its value function calibrated to the old returns - it disassembles the old solution faster than it can assemble the new one. Widening DR under a frozen reward instead keeps the optimum's identity and asks only for local robustification.
Applies when
- proposing to fine-tune an existing policy under a revised reward
- planning a robustification ladder from a validated checkpoint
- a continued run scatters then partially recovers then collapses
“B 臂 fine-tune 证伪(打散→半恢复→摔回——从零 + 强塑形是本机唯一验证过的发育路径)。… 当年证伪的是「奖励表中途改版的 fine-tune」(B 臂,塑形突变致终盘摔回);S2 续训奖励表全程冻结、只逐级加宽 plant/DR,属另一类;若 s2_lag1 续训本身塌方,回退方案 = 该级从零重训,续训教义再议(拿数据说话)。”
train/OMNI_V0_SPEC.md § 3. S1.3 / 4. 与 s1c fine-tune 证伪的关系 Brief the operator on the lineage's measured zero-command and untrained-axis behavior before handing over the joystick
know-zero-command-behaviorBefore any teleop/demo, measure and write down the policy's zero-command behavior and per-axis competence, label untrained axes explicitly as not-bugs, and set the floor/procedure to accommodate the known drift.
Symptom
A teleop session was about to start on a policy that does not stand still at zero command and has never been trained on lateral commands - behaviors an unbriefed operator would report as bugs or emergencies.
Context
Three measured facts were written into the teleop instructions ("都有 实测依据, 不是猜"): (1) A/D (lateral) keys will get essentially no response - probe-measured sidewalk tracking ~3%, an untrained axis: "这正是 C4 要解决的事, 不是 bug"; (2) no keypress = cmd 0, and this lineage does not stand still at zero command - a three-generation lineage property: paces in place, drifts right ~5 cm/s, net rotation -30 deg/20 s; sim survival is 20/20 (it will not fall) but it walks away slowly, so leave floor margin especially on the right; (3) S (backward) WILL respond - probe-measured 20/20 survival, 67% tracking untrained, which is also why this root was chosen for the C ladder. Plus a keybinding dry-run while suspended before touching down.
Change
Operator briefing became part of the deployment artifact: expected response per key, expected idle behavior with magnitudes and directions, and the distinction between untrained (expected, not a bug) and abnormal.
Outcome
The session proceeded with correct interpretations available in advance; the known zero-command wander was handled by floor margin and start-with-command procedure rather than misdiagnosed on the spot.
Mechanism
A learned policy's off-nominal behaviors (idle drift, untrained axes) are lineage properties, stable and measurable in sim beforehand; operator surprise converts known properties into false incident reports and unsafe reactions. A briefing transfers the measured behavior model to the person holding the controller.
Applies when
- handing a learned policy to an operator or demo audience
- the policy idles in a non-stationary way at zero command
- some command axes are untrained in the current lineage
“A/D 基本不会有反应 —— s1e 从未训过非零 vy, 选根探针实测侧走跟踪率 ~3% … 这正是 C4 要解决的事, 不是 bug。… 不按键 = cmd 0, 而 s1e 在零指令下不站定 —— 血统属性, 三代实录: 原地踏步 + 右漂 ~5 cm/s + 净旋 −30°/20s。”
train/REAL_RUN_S2.md § 附: WSAD 遥控 上机前必须知道的三条 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） Real-robot trials of a new skill were staged by risk - a hanging dry run with the robot posed by hand, then one short try per category on a mat with the hardest last, then the composed behaviour (switch + walking) last - with the user present and a log every time
staged-hang-mat-floor-for-get-upStage a new skill's hardware trials by risk - hanging dry run posed by hand, one short try on a mat per start category with the hardest last, the composed behaviour last - with an operator ready to cut enable and a log for every try; relax a safety ban only for short, attended runs and say so in writing.
Symptom
A get-up policy acts violently near the ground by design, and the first unstaged real run of the line was stopped as dangerous.
Context
The hanging checklist written with the first stamped recovery product (v2_5, 2026-08-11), to be ticked item by item with the user present: both machines on the same commit and firmware torque limits checked; the robot hung from a single point about 0.1 m off the ground; a dry run with the robot posed by hand into supine and prone to watch that the target stream is gentle (the beta contract keeps targets within +/-0.25 rad of the measured pose, so enabling causes no homing fling); the first floor try is supine only, on a mat, once, with torque and joint logs; categories are added one at a time, prone last; any kicking or oscillation cuts enable immediately. For the switch (08-14) the runbook orders: hang with the standing policy as the locomotion side, then on a mat push the robot over and let it recover, and only last swap in the walking policy. An exemption was also written: edge-standing policies stay banned from long or unattended runs, but a short single A/B with the user present, hung or on a mat, is allowed. The one-leg line reused the same order (hang, then floor with a spotter, 60 s segments with a temperature check).
Change
Real trials as a checklist of stages, each gated on the previous one, with the composed behaviour last.
Outcome
The line's first real get-up (v2_6, 08-11) came through this protocol and was reported "fairly stable"; no further hardware outcomes of the switch are recorded in the spec.
Mechanism
Each stage exposes one new risk (commanded targets without contact, a single category with contact, harder categories, then the interaction of two policies), so a failure is attributable and cheap.
Applies when
- first hardware trial of a recovery, jumping or other high-impact skill
- switching between two policies on hardware for the first time
- a policy with a known posture defect needs a comparison run
“吊挂空跑: 手动摆到 supine/prone 姿态, 看目标流是否温和 (β 帽 7.5 N·m, 目标永远贴着当前 q ±0.25 rad —— 使能瞬间无归位甩动, 这是 β 契约附带保证) … 落地首试: supine 一类, 垫子, 单次; τ/q --log 全程记录 … 逐类别扩展 (prone 最后), 每类先单次”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §40 吊挂执行单(真机首试;需用户在场,逐项打勾) Four in-lineage attempts to widen the standing stance failed - remove a tax, add a joint-space knife, change the target, add a task-space metric penalty - because the stance was the end state of the get-up path; trained from scratch with the right terms it grew right from day one
stance-decided-by-get-up-pathA posture a skill ends in is shaped by the path the policy takes to reach it; if several single-variable edits to the terminal-phase reward cannot move it, stop editing that phase and retrain with the terminal constraint present from the start.
Symptom
v2_6c stood with its feet 0.159 m apart (task-space) and its hips yawed 45-47 deg the same way, which split on the real robot. Standing-phase reward edits did not move it.
Context
V2.7-A removed the flat-feet tax on compensated stances (stance unchanged); V2.7b added a hip-roll lower-bound hinge (+5 deg in 3,000 iterations, yaw ratchet); V2.8 changed the stand_pose target to a wide flat stance (stance unchanged, yaw not unwound, feet nearly overlapping, mu 0.4 transfer 2%); V2.9 penalized lateral spacing in metres (the policy parked just outside the penalty's gate in a lunge, 0% success). The v2_6c get-up goes through a split and closes the feet together as it rises.
Change
In-lineage stance surgery was formally closed. V3.1 trained from scratch with task-space stance terms present from the first iteration (and, after P1, a positive width band instead of a penalty).
Outcome
V3.1 P1b: lateral stance 0.364 m, foot tilt 0.0 deg, all four categories 100%, MuJoCo mu 1.0 and 0.4 both 100% - with a symmetric toe-out the kinematic audit had not enumerated. P1c (with a yaw guard): 0.355 m, all six acceptance criteria passing, mu 1.0-0.4 all 100%; it became the product.
Mechanism
A converged policy does not rebuild the path that produced its terminal posture; a standing-phase gradient only finds the nearest hack around the posture the get-up delivers.
Applies when
- the final posture of a transition skill is wrong and resists terminal-phase shaping
- repeated continuation rungs produce hacks instead of the intended posture
- deciding between another in-lineage fix and a from-scratch retrain
“窄站距 + yaw 扭是 v2_6c 起身策略(劈叉起身 → 双脚并拢收势)的**结构性 终态**,不是站立段的孤立参数 —— 站立形态由起身路径决定,在血统内只动 站立段奖励改不动它。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §44 结果:V2.8 判 FAIL —— 血统内站姿手术第三次证伪 The recovery line's real-robot verdicts live in three places that disagree - the spec's "fairly stable, first real stand-up" for v2_6, an undated runbook note that only v3_1p1c works, and a first run whose details were never recorded
write-hardware-verdicts-backA hardware verdict is a dated entry in the authoritative ledger - policy file and stamp, gain profile, floor, battery, tries, log file, what was seen - written back the same day; a note in a command file is a pointer, not a verdict, and a newer verdict that contradicts an older one must say so.
Symptom
Asked "which recovery policy works on the real robot", the sources give different answers, and none of them carries the conditions of the test.
Context
08-09: the first real run was stopped as violent and dangerous; which ONNX, which gain profile and whether a log existed were marked "to be recorded" and never were. 08-11: v2_6 was "fairly stable", the line's first real get-up, with splits after standing; v2_5b's result and both CSVs were "to be reported". 08-14: v3_1p1c was stamped and pushed, with "the real first test still needs the user present"; the spec records no hardware result for it. The operator runbook (undated) puts above the v2_6 and v2_5b floor commands the note that none of the recovery policies below work, only recovery_v3_1p1c - a verdict never written back into the spec, with no date, floor, battery, number of tries or log attached.
Change
None recorded in the sources; this card records the gap.
Outcome
The line's authoritative record ends with v3_1p1c as the product awaiting its first real test, while the operator's note implies it is the only one that works and that v2_6 (recorded as a success) does not.
Mechanism
Verdicts given at the robot travel by word of mouth and command-file comments; without a record carrying the conditions, a later reader cannot tell a changed verdict from a changed floor, battery or stack.
Conflicts
§43 (2026-08-11) records v2_6 as the first successful real get-up ("fairly stable"); the undated runbook says every recovery policy except recovery_v3_1p1c does not work; §49 (2026-08-14) says v3_1p1c's first real test was still pending. The runbook's claim has no date and was never written back to the spec, so it cannot be ordered against §43.
Applies when
- choosing which policy to deploy from an operator's notes
- a hardware session ends without a written result
- two documents disagree about what worked on the robot
“下面的recovery都不行 只有recovery_v3_1p1c.onnx”
RL系统/FOLLOW THIS copy 2.md § #### Recovery Policy (operator runbook, undated) 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) 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 Isaac splits Coulomb friction into static and dynamic columns - wiring only static means zero loss during motion, silently discarding the identified value
sim-api-friction-columnsWhen installing identified actuator parameters, map each measured quantity to the simulator's exact API column for the operative regime (dynamic for moving loss, viscous for damping), verify per joint after landing, and audit how randomization intervals fall on each column's nominal.
Symptom
The hardware-identified Coulomb friction (tau_c) was about to be installed into the trainer through the friction= field alone - which in Isaac 5 populates only STATIC friction, so during motion the joints would lose no torque at all: "只给 static 则运动中不损耗, 辨识的 τ_c 走路时等于没接" (the identified tau_c would effectively not be connected while walking).
Context
The v12 integration wired all three columns deliberately: armature= and friction= from the 2026-08-04 hardware identification, PLUS dynamic_friction= (Isaac 5 splits Coulomb into static/dynamic; the moving-loss column is dynamic) and viscous_friction= (= the measured joint damping 0.02, aligned to MJCF's damping). Each value was re-checked per joint after landing. A DR interaction was audited and booked rather than hidden: randomize_joint_parameters jitters ALL friction columns with ONE interval - the [-0.05, +0.10] band was calibrated against the Coulomb nominal, and landing on the viscous nominal 0.02 it becomes [0, 0.12], "偏宽但保守" (wide but conservative), accepted with the note that pre-viscous behavior was already [0, 0.10] on a base of 0.
Change
Measured actuator parameters installed across all applicable API columns (armature, static, dynamic, viscous), with the DR side effect on shared randomization intervals audited and recorded.
Outcome
The first generation where the identified plant actually acts during motion in the trainer; the silent-column failure mode documented before it cost a training run.
Mechanism
Physics engines decompose "friction" differently (single coefficient vs static/dynamic/viscous columns); a measured parameter is only installed when it reaches the column the solver reads in the regime that matters (motion, not stiction). Randomizers that share one interval across columns rescale the band by each column's nominal - a hidden unit change.
Applies when
- installing identified friction/armature into any trainer
- porting plant parameters between simulators or engine versions
- joint losses in sim do not match bench measurements during motion
“并额外传 dynamic_friction=(Isaac 5 把库仑拆 static/dynamic 两列,只给 static 则运动中不损耗,辨识的 τ_c 走路时等于没接)与 viscous_friction=(= joint_damping 0.02,对齐 MJCF damping)。… randomize_joint_parameters 用同一个 friction 区间抖三列, [-0.05,+0.10] 是按库仑标称标的,落到粘滞标称 0.02 上成了 [0,0.12]”
train/WALK_V12_SPEC.md § 7. 核查单 (Isaac 接 V.ACTUATORS 新字段) A suspended (no-load) test acquits or convicts the actuator before you blame authority
suspended-test-isolates-actuator-authorityBefore attributing a failure to actuator authority, measure no-load tracking error and steady-state torque fraction; blame authority only if the task fails while the error grows with demanded force - and then fix gains or targets, not training.
Symptom
hip_roll sagged 0.21 rad on the ground and saturation questions loomed over the sidewalk plan - was the roll axis physically too weak (authority), or was something else limiting it?
Context
Before C4, the roll-authority question was settled by measurement triage: suspended test (--suspend, feet off ground) showed hip_roll tracking error 0.0008 rad - actuator acquitted; the entire 0.21 rad ground sag is load-induced. Steady-state torque was 25% of limit - 75% margin remains. Since sidewalk needs lateral force, not exact angles, authority was ruled "not a hard limit", with a pre-registered criterion for when it WOULD become one: sidewalk fails to track AND roll error keeps growing - then the fix is raising hip_roll kp or lowering the vy target, not more training.
Change
Hypothesis "roll authority insufficient" demoted from blocker to a monitored branch with an explicit trigger condition; C4 proceeded.
Outcome
Later open-loop probes confirmed the actuator could produce the behavior (sidewalk feed-forward ran at full amplitude, 5/5 survival), and the eventual C4 failure causes were measurement and reward, never authority.
Mechanism
Suspended vs loaded comparison separates the actuator's closed-loop competence from the load path: tiny no-load tracking error means the motor/controller is fine and any loaded deviation is statics (gravity / stiffness budget, kp trading error for force). Torque-fraction measurement then bounds how much force headroom actually remains.
Applies when
- suspecting an axis is "too weak" for a new skill
- large position sag on a loaded joint
- deciding between hardware fix, gain change, and more training
“吊挂（--suspend）实测 hip_roll 跟踪误差 0.0008 rad → 执行器无罪，地面下垂 0.21 rad 全是负载所致；稳态占限扭 25% → 仍有 75% 扭矩余量。… 判据：若 C4 出现「侧走跟不动且 roll 误差继续变大」，那才是权限账 … 解法是提 hip_roll 的 kp 或降 vy 目标，不是硬训。”
train/C_LADDER_RUN.md § 3d. roll 权限：已部分澄清，不是硬上限 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) An edge-triggered landing penalty missed the tail and fired after the harm - penalize overspeed continuously inside the contact window
penalize-tail-before-touchdownPenalties aimed at impact/violation events must (a) price the excess over a threshold, not the mean, and (b) be active on the approach (state-gated window), not triggered by the event - check your control rate can even see the event you are penalizing.
Symptom
The v7 landing penalty (vz^2 on the contact-force rising edge, weight -10) did not bite: landing-velocity 95th percentile stayed at 2.61 m/s against a 0.3 target.
Context
Two structural faults were identified: (1) it penalized the MEAN over sparse events - many soft landings dilute the occasional violent slam, while the damage (GRF peaks, motor peak load) lives in the tail; (2) it fired AFTER touchdown - at 50 Hz evaluation the rising edge is aliased by physics decimation, so the read vz is often the already-decelerated post-impact value: underestimated, and with no shaping gradient before contact. Replacement: continuous penalty while the sole is inside a height gate (h < 0.03 m): relu(-vz - 0.30) - only the excess over an allowed approach speed is penalized (tail only), and gradient exists for several frames BEFORE touchdown. The sole-height computation again subtracts the 0.0585 m link offset ("WALK_DIAGNOSIS 坑#1, 别再踩"); the edge-triggered version was kept as a diagnostic only.
Change
feet_landing_vel reformulated: edge-event vz^2 -> in-window relu(-vz - v_ok) with v_ok 0.30 (conservative vs the sqrt(L)-scaled human value ~0.19, to be tightened after passing), h_gate 0.03, weight unchanged -10.
Outcome
The failure analysis of the first form was written before the second was trained; the v_ok escalation path (0.30 -> 0.45 if the robot becomes afraid to land) was pre-registered in the risk table.
Mechanism
Sparse-event mean penalties optimize the average case while the constraint is a quantile; and any penalty evaluated only at/after a discrete event gives the optimizer no gradient along the approach trajectory that determines the event. A state-gated continuous excess penalty fixes both: it prices only violations and shapes the approach.
Applies when
- impact/landing penalties fail to move tail percentiles
- a penalty is triggered by contact edges at a coarse control rate
- designing constraint-style penalties for rare violent events
“罚的是均值路径:上升沿是稀疏事件 … 大量软着陆稀释偶发猛砸;而伤害在尾部 … 罚在触地后:50 Hz 评一次,上升沿被物理 decimation 混叠,读到的 vz 常是撞完已减速的值——既低估,又没有触地前的塑形梯度。”
train/WALK_V8_SPEC.md § 2. 改动 B — 落地惩罚改罚尾部、罚在触地前 Bracket a real-robot A/B with a repeated reference run - battery drain is the confound
battery-bracketed-real-abOrder hardware A/B sessions as A-B-A: repeat the first condition at the end, and void the comparison if the bracket runs disagree - never let battery or venue drift ride on the second condition.
Symptom
In a two-policy teleop A/B on hardware, the second policy is measured on a lower battery voltage than the first - a systematic bias that would be read as a policy difference.
Context
C2 real A/B (checkpoint 700 vs A800, same floor, same day) was scripted as 700 -> A800 -> 700-rerun, with the explicit note that a teleop session drains the pack and the trailing policy "naturally suffers".
Change
Protocol: run the reference policy first AND last; if the two reference runs differ noticeably, declare the whole session battery/floor-polluted and void the A/B ("结论作废重来"). Also log electricity per run.
Outcome
Called out as the round's only systematic confound, closed by one extra command ("这是本轮唯一的系统性混淆源，一条命令就能堵掉").
Mechanism
Battery voltage scales available torque, and torque loss hits behavior asymmetrically (see power-scale-hurts-nonforward-axes), so drain masquerades as policy regression; a head/tail reference pair converts the unobserved drift into a measured control.
Applies when
- comparing two policies or settings on hardware in one session
- any sequential hardware evaluation where the plant drifts (battery, temperature, floor wear)
“为什么要 700 复跑：一次遥控 session 下来电池会掉压，第二枚天然吃亏。头尾各跑一次 700，若两次 700 明显不同，说明这轮 A/B 被电量污染，结论作废重来。这是本轮唯一的系统性混淆源，一条命令就能堵掉。”
train/C_LADDER_RUN.md § 3c. A-3 真机 A/B（同一段地板、同一天、电量记账） The +/-50 mm lateral COM randomization meant to spread the legs coincided with legs pulling IN - rolled back per its own pre-registered contract
com-dr-rollback-on-symptomWhen adopting a DR value that covers no local measurement, write its intent and rollback trigger into the config at adoption time; roll it back as the control arm the moment the symptom contradicts the intent, and promote the symptom's metric into the acceptance battery.
Symptom
After v7 adopted the reference developer's oversized lateral COM randomization (+/-50 mm) explicitly to force leg spread, the real robot's legs narrowed instead - lateral mean 154 mm / closest 107 mm in sim (nominal 214.5), narrower still on hardware with occasional leg contact.
Context
The rollback was clean because the adoption had been honest: the robot.yaml comment recorded the intent AND that the +/-50 value covered no local measurement (only a 16/7 mm measured offset existed; even the prior widening to +/-20 was subjective), plus the reference's own reported side effect (base sway) and the note "这一项要单独跑、 单独归因". When the opposite symptom appeared, v8 returned y to +/-20 mm as the control arm ("要么没起作用、要么帮了倒忙 … 按约定退回做 对照"), kept x/z untouched (a noise-level difference not worth another variable), and named the second suspect: the landing penalty itself, via the reference's own three-link chain (landing penalty -> stance narrows -> spacing penalty needed). A gate lesson was booked in the same table: v7's sim numbers had ALREADY crossed the line (154/107 vs v5's 182/147) - "这个指标本可拦下 v7" - so foot-distance became a standing acceptance row (min >120 mm, zero leg-leg contacts).
Change
base_com_offset_m y: 0.050 -> 0.020 (x/z kept), regenerated through the export tool rather than hand-editing derived files; foot-distance acceptance row added.
Outcome
A borrowed DR lever with no local measurement basis was retired the moment its symptom contradicted its purpose, at single-variable cost; the metric that would have caught it pre-hardware entered the gate.
Mechanism
DR ranges shape behavior through the policy's robustness strategy, which is jointly determined with every reward term; a lever that forces stance width on one robot can be dominated by a stronger narrowing pressure (landing softness) on another. Levers adopted without local measurement must carry their own rollback trigger, because there is no nominal to argue from when they misbehave.
Conflicts
Causality is not fully closed in the source: the narrowing may come from the landing penalty rather than the COM lever ("腿距的第二嫌疑人是 ④ 本身"); the rollback is the pre-agreed control experiment, not a verdict that the lever caused the narrowing.
Applies when
- importing DR ranges or behavioral-forcing randomizations from references
- a DR lever's observed effect contradicts its documented purpose
- a sim metric existed that would have caught a shipped regression
“⑥ 的本意 … 是逼策略把脚分开;真机结果是脚向内收且偶发相碰——要么没起作用、要么帮了倒忙。… 注释当时就写了"这一项要单独跑、单独归因"。现在症状出现了,按约定退回做对照。”
train/WALK_V8_SPEC.md § 3. 改动 C — 质心随机化退回(撤销 v7-⑥ 的 y 项) The 1.5 Hz step-frequency gate was retracted - the author had misread his own actuator data, and the limit fought pendulum dynamics
gate-threshold-retracted-frequencyEvery gate threshold must cite its measurement and survive a first-principles sanity check; when a gate keeps failing otherwise healthy behavior, re-derive the threshold from the raw data before enforcing it again - and retract wrong gates in writing.
Symptom
An acceptance criterion "step frequency <= 1.5 Hz" kept failing healthy policies (walk_v4 at 2.33 Hz), and an earlier attempt to force slower stepping (v3) had killed stepping altogether.
Context
The threshold had been derived from the author's own actuator frequency-response measurements - but re-reading the raw table showed the misread: amplitude ratio at 2.0 Hz is 0.88 (knee) / 0.83 (ankle), acceptable; the genuinely bad point was walk_v2's 3.8 Hz at 0.58. Mechanism check agreed: the leg as a compound pendulum (L ~ 0.30 m) has a natural frequency ~1.1 Hz, swing half-period 0.45 s - the observed 2.1-2.4 Hz sits near where the leg wants to swing, and forcing 1.5 Hz "是跟摆动动力学对着干" (fights the swing dynamics). The frequency definition itself was pinned by two independent methods (contact-event counting 2.39 Hz vs FFT 2.33 Hz, agreeing): reported numbers are cycle frequency = steps per leg per second.
Change
Gate retracted in writing: "步频 ≤1.5 Hz 应删除或放宽到 ≤2.5 Hz"; frequency definition standardized before entering any config.
Outcome
walk_v4/v6's 2.1-2.4 Hz reclassified from disease to normal; the v3 failure got its probable explanation (suppressing stepping to meet a wrong gate).
Mechanism
A gate is only as good as the measurement and the reading behind it; thresholds inherited from a misread plot become invisible design constraints that later training obeys at real cost. Cross-checking a threshold against first-principles dynamics (pendulum frequency) is a cheap way to catch such misreads.
Applies when
- an acceptance threshold repeatedly fails policies that look healthy
- thresholds were set from a single person's reading of raw data
- a forced compliance with a gate degrades the behavior it guards
“我当初依据自己测的执行器频响定的,但看错了区间。… 2.0~2.4 Hz 的幅值比 0.83~0.88 是可接受的;真正不行的是 walk_v2 的 3.8 Hz。… 腿按复摆算(L≈0.30 m)自然频率约 1.1 Hz … 把它压到 1.5 Hz 是跟摆动动力学对着干(walk_v3 把迈步压没了,可能正是这个原因)。”
train/WALK_DIAGNOSIS.md § ② 撤回"步频 ≤1.5 Hz"这条验收标准 —— 是我定错了