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
87 cards matching “power-derating-cuts-full-range-contract”.
The walking lines' safety setting, power-scale 0.8, broke the recovery policy's full-range contract - it cut the ends of the joint travel (4/50 could not get up) and left the torque spikes untouched; a kp x 0.9 gain profile inside the trained kp band did the job
power-derating-cuts-full-range-contractA deployment derating knob means something only relative to the action contract: before reusing a line's "safe setting" on a new skill, check what it does to that skill's reachable range and to the term that makes the spikes, prefer a gain change inside the band the policy was randomized over, verify it in simulation, and re-decide when the contract changes.
Symptom
After the violent first real get-up (2026-08-09), the recovery policy needed a gentler setting for its next hardware test, and the walking and omni lines' standard derating - deploying at power-scale 0.8 - was the obvious candidate.
Context
The V0 recovery contract maps actions to absolute targets over the full joint range: a = +/-1 lands exactly on the URDF limits, and standing puts the knee at the clip. Candidates were compared on R3.1 in MuJoCo (5 categories x 10 seeds) on 2026-08-10 before any hardware time was spent.
Change
A new gain profile, rl_kp090 (kp x 0.9, kd unchanged), recorded in robot.yaml as the recovery hardware-test setting, with power-scale 0.8 explicitly banned for recovery.
Outcome
kp x 0.9: 48/50 got up; median torque demand on hip_pitch/knee fell from 120-125% to 100-104% of the deployment limit; leg-leg contact frames 2,152 -> 1,095; the change sits inside the +/-10% kp randomization the policy trained with. power-scale 0.8: 4/50 could not get up, because under the full-range contract it removes the ends of the travel (the deep squat's tucked legs, the straight standing knee), and the torque spikes (kp x error) did not fall at all. When the line moved to the beta-anchored contract, rl_kp090 was declared a V0-era choice that does not fit (beta is calibrated at kp 30) and deployment returned to rl_default; the deploy switch applies power scaling to the walking side only.
Mechanism
A power scale multiplies the action, which under an absolute full-range mapping shrinks the reachable workspace instead of softening the actuator; the spikes come from the proportional term on large errors, which only a gain change reduces - and a gain change inside the trained randomization band stays in distribution.
Conflicts
The undated operator runbook still carries an R3.1 "B comparison" command at power-scale 0.8 beside the rl_default baseline; the sources do not say whether it was written before the ban or was ever run.
Applies when
- reusing a power, torque or action scale from one skill on another
- a policy whose actions map to absolute targets over the full joint range
- choosing a gentler setting for a first or second hardware trial
“kp×0.9 / kd 不动 —— recovery_r3_1 成功 48/50, τ 需求中位 hip_pitch/knee 120~125% -> 100~104% 部署限, 腿-腿接触 2152 -> 1095 帧; ±10% 在训练 kp DR 带内. ⚠️ power-scale 0.8 对 recovery **禁用**: 全 ROM 契约下 0.8 砍的是行程 端点 (深蹲收腿/站直够不到), 实测 4/50 起不来, 且尖峰 (kp·err) 一点不降 —— 它是 walk/omni 的安全档, 不是 recovery 的.”
git:Lucen-recovery@origin/recovery:robot.yaml § gain_profiles 注释: recovery 真机测试安全档 (2026-08-10) / rl_kp090 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 Anchoring the action on the measured joint angle (target = q + beta*a), with a beta curriculum down to tau_limit/kp, bounded torque by construction, removed the re-falls and later stood the robot up on hardware
beta-anchored-action-targetFor large-motion skills on position-controlled actuators, bound the action relative to the measured joint angle with a per-joint authority of tau_limit/kp, curriculum the authority down from full range, keep the curriculum state out of the observation and pin acceptance at the deployed authority - and make the deployment code refuse to run the anchored contract without a measured q.
Symptom
The V0 full-range absolute action produced violent targets; the V1 command-anchored rate limit made standing oscillate. Both failure modes came from how the action becomes a target.
Context
V2.0 (user approved, from scratch): BetaAnchorJointPositionAction, target = q_measured + beta_j(m)*a, memoryless per step. beta_j(m) = floor + m*(beta0 - floor), beta0 = the contract half-range (m = 1 reproduces V0 authority), floor = min(tau_limit/kp, beta0): hip_pitch 1.309 -> 0.40, knee 1.047 -> 0.40, hip_yaw -> 0.917, the other joints unchanged - the tightening lands exactly on the joints the V0 torque account convicted. m drops 0.1 per step when a standing-share EMA exceeds 0.35. beta is NOT in the observation, so the 45-dim contract is untouched; acceptance is pinned at m = 0 because the Python curriculum state is not saved in the checkpoint. The deployment chain got a new profile (recovery_v2: action_anchor current_q, explicit per-joint beta written into the contract, independent of the gain profile), and policy_io raises if q is missing rather than silently falling back to the absolute contract; the old profile's check reproduced its pre-change deviation bit for bit.
Change
New action term and beta curriculum; later the RS06 floor was lowered 0.40 -> 0.30 -> 0.25 (kp*beta 7.5 N*m) and the stamped deployment profile was synced to 0.25.
Outcome
First acceptance at m = 0 (v2_0b): re-falls 0% in every category, the torque gate passed for the first time on the line (worst 69.9%), knee jitter 0.004; supine 98.8 / side 88.8% with prone and mid still failing (fixed by the conditional pull curriculum). MuJoCo showed demand at or under the limits (hip_pitch 11.7/12 against V0's 26.8). Lowering beta cut impact (hip_pitch demand 9.7 -> 8.5 N*m) but barely slowed the get-up - it had become coordination-limited. Enabling the policy moves the target only +/-beta around the current pose, so there is no homing fling; the 08-11 real get-up and the later v3_1p1c both run on this contract.
Mechanism
kp*beta caps the proportional torque in a single step with no build-up delay and no memory, giving both a hard impact bound and full balance bandwidth.
Applies when
- a skill needs full joint range but hardware torque limits are low
- absolute position targets cause impacts or saturation
- changing the action semantics of a contract that deployed policies share
“**动作项** `BetaAnchorJointPositionAction`:`target = q_实测 + β_j(m)·a`, 逐步无记忆 … **Play/验收钉 m=0(= floor = 部署档)**:python 课程状态不进 checkpoint, Play cfg 显式 `beta_m_start=0` … 判读:**结构赌注兑现** —— 站姿零再摔 + 力矩账首过(kp·β 封顶按构造)”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §33 V2.0 预注册(2026-08-10,用户点头开工):β 锚定动作空间,从零训 Freeze the deployment contract, stamp every export, and let an automated checker catch wiring bugs
contract-freeze-and-checkerFreeze and fingerprint the policy I/O contract; ship contract changes as new versioned profiles that leave old artifacts bit-identical; and extend the automated contract checker with every pipeline change, forcing the new path to execute in the check.
Symptom
Contract-level changes (observation layout, action pipeline) are where silent sim/real divergence is born; two real wiring bugs appeared the one time the action pipeline was extended.
Context
The 215-dim observation contract was frozen ("纪元 3，三机 digest" - an era number plus a digest agreed across three machines); proposals that would break it (e.g. a GRU memory) were rejected on contract grounds. Every exported ONNX is stamped and verified with a manifest (onnx_manifest --stamp / --verify), and deployment refuses mismatched combinations. When C4 added the lateral feed-forward, it went in as a NEW profile (omni_ff) leaving the existing omni profile's behavior bit-identical; the checker (check_contract) was extended to force the feed-forward path to actually execute (cmd_vy=0.13) and promptly caught two genuine bugs: (1) re-clamping with soft_joint_pos_limits after the feed-forward (0.23 rad deviation) instead of reusing the parent's clip; (2) indexing processed actions by asset.joint_names instead of the action term's own contract-ordered _joint_names, which landed the feed-forward on the wrong joints (l_hip_yaw / r_ankle_pitch).
Change
Contract discipline as implemented: frozen dims + digest; manifest stamping and refusal; contract changes only via new versioned profiles; checker updated in the same commit as any pipeline change, with inputs chosen so new code paths are exercised.
Outcome
Both wiring bugs caught before any training or deployment ("两个都是 check_contract 当场抓出来的 —— 这次它值回票价"); old deployments provably unaffected by the new profile.
Mechanism
The contract is the only interface the policy and robot share; freezing plus fingerprinting makes divergence detectable, and an executable checker turns "the contract holds" from a belief into a test - but only if its inputs actually drive the new code path.
Applies when
- modifying the action or observation pipeline of a deployed policy
- exporting policies for hardware
- proposals that would change observation dims or history structure
“契约校验抓到的两个真错误（记账，别再犯）：1. 前馈后误用 soft_joint_pos_limits（URDF 限位 ×0.9）重钳 → 0.23 rad 偏差 … 2. 用 asset.joint_names 索引 _processed_actions → 前馈落到 l_hip_yaw/r_ankle_pitch 上 … 两个都是 check_contract 当场抓出来的 —— 这次它值回票价。”
train/C_LADDER_RUN.md § 3j. 契约级改动 / 契约校验抓到的两个真错误 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 real robot's right-leg kicking was over-trained-delay times loop gain - irreducible pipeline latency is plant, model it fully from day one
pipeline-latency-is-plant-not-drMeasure the end-to-end action pipeline delay and build it into the nominal plant and every acceptance gate from day one; treat power/scale deratings that "fix" oscillation as gain-reduction crutches flagging an unmodeled delay, and expect higher-feedback-gain policies to be MORE delay-fragile.
Symptom
On hardware, s1c/s1d at action scale 1.0 always kicked wildly with the right leg (s1c only ran as SOTA at power 0.8; s1d only at 0.7) - while sim showed nothing under default evaluation.
Context
Sim reproduced the incident item by item once the real pipeline delay was injected: s1d@1.0 with --delay 1 fell at 10.2 s, --delay 2 at 5.2 s; s1c@1.0 stressed (r_hip_roll saturation 5 -> 16%; "右脚" = the policy's chirality makes the right leg its high-gain leg); and the combos that worked on hardware (s1c@0.8+delay2, s1d@0.7+delay2) all survived in sim. Mechanism: the real pipeline is ~1-2 ticks (BusWorker next-cycle pickup + CAN round trip) but S1.1 trained only to 1 tick - "超训延迟 × 全环路增益 = 振荡;衰减 = 压环路增益换稳定" (delay beyond training x full loop gain = oscillation; the power derating had been buying stability by compressing loop gain). s1d was MORE fragile than s1c because its yaw 3-layer stack had learned higher feedback gain - higher gain, lower delay tolerance. Three changes: latency DR widened to cover reality; acceptance gates and smoke runs moved permanently to --delay 2 ("门必须在真机条件下预测 真机"); and the doctrine written twice-paid: "不可约的管线属性(延迟、 限速)不是'随机化选项',是 plant 本体,第一天就该全额建模" - S1's nominal-then-robust staging falsified by hardware for the second time. The later s1e hardware run at power 1.0 (no kicking, normal force) closed the loop: "0.8 = 旧代拐杖" - the derating had been a crutch for the under-modeled delay, not a real requirement.
Change
Latency modeled as plant from day one of any lineage (measured 1-2 ticks covered, bridge-layer rate limits likewise modeled by default); every gate and smoke evaluation issued under --delay 2.
Outcome
Kicking reproduced, explained, and eliminated in the s1e generation at full scale and full power; the deploy-side crutches (0.7/0.8) retired for the new lineage.
Mechanism
Feedback oscillation onset is a product of loop gain and phase lag; a policy trained below the real delay learns gains that sit past the real stability margin, and any output derating masks it by scaling gain down. Since pipeline delay is deterministic hardware property - not an uncertainty - it belongs in the nominal plant, and every evaluation must include it or the gate predicts a robot that does not exist.
Applies when
- hardware oscillation/kicking that sim only reproduces with added delay
- a policy only runs on hardware at reduced power/scale
- defining what belongs in the nominal plant vs the DR list
“真实链路延迟 ~1~2 拍 … S1.1 只训到 1 拍——超训延迟 × 全环路增益 = 振荡;衰减 = 压环路增益换稳定。s1d 比 s1c 更脆 = yaw 三层栈学出更高反馈增益,增益越高延迟容忍越低。… 教训入账:S1「先标称后鲁棒」第二次被真机证伪——不可约的管线属性(延迟、限速)不是"随机化选项",是 plant 本体,第一天就该全额建模。”
train/OMNI_V0_SPEC.md § 3. S1.4(真机右脚乱踢事故强制) Size each joint's action authority to its measured working range - lock a channel to zero only when its job is provably elsewhere
per-joint-action-scale-lockdownSet per-joint action scales from measured target ranges and momentum decompositions: full authority for working channels, working-range authority for balance channels, zero for channels whose contribution is measured negligible - and prefer this structural quieting over perpetual reward penalties, keeping the contract dimensions intact.
Symptom
"Walks crooked" - roll and yaw channels wandered; hip_yaw peak-to-peak reached 22.3 deg in v11 while contributing essentially nothing to locomotion; a uniform action scale of 0.5 gave every joint the same authority regardless of its actual job.
Context
The v12 design replaced the scalar action scale with per-joint scales justified by measurements: pitch-class 0.5 (the gait's entire working channel - untouched); hip_yaw 0.0 - lossless because it carries only 1.4% of yaw momentum (v6 decomposition) and straight-line targets use merely +/-0.006-0.03 ("乱动纯属浪费"); roll 0.2 - NOT zero, because lateral balance and weight transfer are roll's unique job (locking it would degenerate into foot-edge rocking, "比现在更歪"), and 0.2 covers the measured working range +/-0.06-0.19 while "0.5 的另一半全是 '歪歪扭扭'的来源". Costs were accepted consciously: turning demoted to an observation row with a fallback (yaw 0 -> 0.1 in v13). Contract preserved: the 12-dim action interface unchanged, yaw values simply neutralized. The structural lockdown also RETIRED the reward-side hip_yaw_quiet penalty - "A 的 yaw=0 结构性取代,不再付奖励塑形成本".
Change
action_scale_joint pitch 0.5 / roll 0.2 / yaw 0.0 wired through robot.yaml -> policy_io (verified: all-ones action gives hip_yaw target exactly 0) -> Isaac action term, guarded by the contract checker ("它就是抓这种双侧不一致的").
Outcome
Designed and verified on the shared side before the lineage freeze; stands as the pattern for authority sizing: structure replaces reward shaping wherever a channel should simply not act.
Mechanism
Action scale is a per-channel authority budget; uniform budgets give noise channels the same voice as working channels, and reward-side quieting then pays a permanent shaping tax for what a zero scale provides for free. But zeroing is only lossless when decomposition proves the channel's contribution negligible AND no unique function (balance) lives there.
Conflicts
Wired and verified on the config/deploy side but never trained - the 2026-08-05 reset suspended v12 before the Isaac-side run.
Applies when
- some joints wander without contributing to the task
- a quieting penalty (deviation/L1) taxes every step forever
- deciding action-space authority for a new task or robot
“yaw=0 是无损的:实测它只贡献 1.4% 偏航动量、直行目标只 ±0.006~0.03,乱动纯属浪费。… roll 不能为 0:横向平衡/重心换脚是它的独有职责,锁死会退化成脚缘摇摆(比现在更歪)。0.2 的依据:各代实测 roll 目标只用 ±0.06~0.19,0.5 的另一半全是"歪歪扭扭"的来源。”
train/WALK_V12_SPEC.md § 3. A —— 逐关节动作幅度(用户"只动 pitch"的安全版) Fall recovery was defined as the whole chain - any fallen pose, a stable stand, a clean hand-back to walking - and built as a second policy behind a deploy-side switch, not folded into the walking PPO
recovery-two-policies-and-a-state-machineDefine a recovery skill by the whole chain it must complete, including the hand-back to the next controller; if it is built as a separate policy, make the switching logic and its handoff contract a deliverable of their own, and keep the recovery observation contract a subset of the locomotion one so a unified policy stays possible later.
Symptom
A walking robot that falls needs a human to stand it back up. The design question on 2026-08-09 was whether to teach getting up inside the existing omni walking policy or beside it.
Context
The user set the goal as "any fallen pose -> stand up alone -> stand stably", and the spec named the real difficulty as the full chain fall -> recovery -> stable stand -> correctly initialised walking history and clock -> walking, making the deploy state machine a first-class deliverable. A unified single policy had a real-robot precedent (arXiv:2605.18611, a state-dependent gate near 37 deg tilt) but was deferred until a recovery policy and an omni policy were each reliable. The line ran on its own branch and worktree with every walk/stand/omni/run config path untouched. The development path copied the G1 learned get-up logic (arXiv:2502.12152): first find any feasible get-up (ugly accepted), then add smoothing, torque and real-robot constraints. The recovery contract kept the base 45-dim observation (command slice held at 0, no gait phase, no frame history - their reasons do not apply to a skill without a clock or a velocity task), so it stays a prefix of the 215-dim omni contract and a later merge is not foreclosed.
Change
Two policies and a deploy-side switch instead of one retrained walking policy; recovery got its own minimal contract (45 dims, full-range action, later the beta-anchored profile) and its own acceptance battery.
Outcome
The split held for the whole line: on 08-14 deploy_policy gained a second (PolicyIO, ONNX) pair behind --recovery-policy, each loaded under its own manifest contract, and the runbook runs stand_v1b or omni_c4_ff800 as the locomotion side with recovery_v3_1p1c. The literature scan of 08-10 found that every verified get-up implementation deploys one end-to-end policy (or softly gated experts) and stages only on the training side - so the runtime state machine here is the walk/recovery switch, not a staged get-up.
Mechanism
A separate policy keeps each reward table single-purpose and lets a proven walking lineage stay byte-frozen; the cost moves to the handoff, where every piece of state one policy leaves behind (history, clock, last action, command) must be reset for the other.
Applies when
- adding fall recovery or get-up to a robot that already walks
- choosing between one unified policy and a switched pair of policies
- designing the observation/action contract of a secondary skill
“先做 recovery policy + omni policy 两个策略,部署侧状态机切换;不把 recovery 硬塞进现有 omni PPO。 … 任务定义:**任意跌倒姿态 → 自己站起来 → 稳定站立**。真正的难点不只是"起身", … omni walk**(§6 部署状态机是本 spec 的一等公民,不是附录)”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §0 目标口径与架构判决(用户 2026-08-09 定) A 2-degree joint-zero calibration fix moved the whole runnable envelope - re-test old "cannot run" verdicts after recalibration
zero-offset-calibration-shifts-envelopeDate every hardware verdict with the calibration state; after any zero/mount recalibration, re-test previously condemned policy-power combinations and previously "unexplainable" posture offsets before attributing either to training or model.
Symptom
s1d was on record as "only runs at power 0.7" (kicked wildly at 0.8); after a calibration pass, the same policy ran 12 s at 0.8 with no kicking at all.
Context
The calibration had fixed a 2.08 deg zero offset on r_hip_roll - exactly the constant error source on the dominant joint of the kicking oscillation loop ("恰是乱踢振荡环主导关节的常值误差源"). The three-generation post-calibration hardware sweep also closed a second case: the robot's mysterious "backward lean" disappeared after calibration, and the sim-real posture difference collapsed from opposite-sign 5+ deg to same-sign ~2 deg ("后仰案实质了结") - the lean had been a sensing/zero artifact, not a mass-model error. Booked consequence: if the s1d recovery re-verifies, "真机可跑档整体 上移" - every policy's runnable power envelope shifts up, and downstream lineages' hardware expectations get revised.
Change
Joint-zero and mount calibration promoted from setup chore to a variable that dates hardware verdicts: verdicts about which power/scale levels a policy can run are conditioned on the calibration state they were measured under.
Outcome
One policy rehabilitated at a higher power level; one standing sim-real posture discrepancy closed without touching model or training; a pending re-verification booked rather than asserted.
Mechanism
A constant joint-zero error acts as a persistent disturbance injected at the feedback loop's most-loaded joint; near an oscillation threshold, removing a 2-degree bias is the difference between a stable and an unstable loop. Since the error is additive and machine-side, it shifts every policy's stability envelope simultaneously - which is why verdicts must carry their calibration date.
Conflicts
The s1d rehabilitation awaited one confirming re-run at the time of writing ("待复核一跑坐实") - the offset-as-cause reading is the head suspect, not a closed verdict.
Applies when
- a policy oscillates at a power level others tolerate
- sim and real disagree on a constant posture offset
- deciding whether to re-test old hardware verdicts after maintenance/calibration
“发现①:s1d@0.8 能跑了(旧账「只有 0.7 能跑」)——12s 无乱踢。头号嫌疑 = 标定修正:r_hip_roll offset 修 2.08°,恰是乱踢振荡环主导关节的常值误差源。… 发现②:「后仰」标定后消失 … sim-real 姿态差从反号 5°+ 收敛到同号 2°,后仰案实质了结。”
train/README.md § 真机 @0.8 三代横评(2026-08-07 标定后) Narrowing the speed range to stop high-speed falls entrenched crouch-shuffling - judge gait quality at the speed that demands a gait
low-speed-commands-reward-draggingSet command ranges to include speeds that physically demand the target behavior, and evaluate behavior-quality gates at those speeds; when a restriction is added to suppress a failure, check what new optimum it creates at the remaining commands.
Symptom
After the command range was narrowed to (0.15, 0.35) m/s (to treat walk_v1's 134% overspeed and 8.3 s fall at 0.5), the policy settled into crouched foot-dragging; tracking rose monotonically with speed (63% at cmd 0.2, 76% at 0.3, 87% at 0.45), showing low speeds were where the degenerate gait was optimal.
Context
The narrowing advice was the author's own and is retracted in the file: it treated the symptom (falls at speed) while reinforcing the root cause (at 0.15-0.35 m/s, shuffling in a crouch is globally optimal - the Froude number is so low that even humans would not lift their feet). A zero-cost experiment confirmed the flip side: at cmd 0.5 the same policy met BOTH tracking (81%) and clearance (23.0/23.2 mm) standards.
Change
Speed range widened back toward (0.15, 0.5) - upper bound deliberately slightly above the mechanically feasible ~0.44 m/s so the policy finds the boundary itself; acceptance re-pointed: gait-quality criteria (tracking, clearance) judged at 0.45-0.5 m/s, low speed kept only as a survival check.
Outcome
v4 -> v6 progression under the widened range delivered 87% tracking with 34 mm clearance; the "low command = drag" account was confirmed by the monotone tracking-vs-speed curve.
Mechanism
Command distribution is part of the reward: physics prices gaits per speed, and at very low speed the energetic optimum is no swing phase at all. Restricting training to that regime makes the degenerate gait the correct answer to the posed problem - and grading a gait at a speed that does not require stepping measures nothing.
Applies when
- a gait degenerates after a command-range restriction
- quality metrics improve monotonically toward the range boundary
- writing acceptance criteria for gait quality vs survival
“现在看那个建议可能起了反作用:0.15~0.35 m/s 下蹲着蹭就是全局最优,抬腿反而亏。收窄治的是"高速摔倒"的症状,却强化了拖地的病根。… 验收标准里的 cmd 0.2 本身就是拖地速度(Froude 数极低,人在那个速度下也不抬脚)。accept_v2 应把速度跟踪与 clearance 的判定点改到 0.45~0.5 m/s”
train/WALK_DIAGNOSIS.md § ② 放宽速度区间 / ① 零成本实验 Every power cycle starts with the same read-only pre-flight - read the buses, check the torque limits against 12/17/11, verify the IMU axes, check the ports after any new USB device - and any reassembly re-measures the joint zeros
power-cycle-preflightStart every powered session with a fixed, read-only pre-flight - bus responses, torque limits equal to the simulated ones, IMU axes, device identities - and re-measure joint zeros after any mechanical reassembly before running a policy.
Symptom
Hardware state drifts between sessions in ways no policy can see: a motor that stops answering after a power cycle, a torque limit that differs from the one simulated, an IMU axis flipped, two USB devices swapping identities, a joint zero moved by reassembly.
Context
The runbook's session order before any policy runs: read every motor on both CAN buses without enabling them (the first command after every power cycle); set_torque --check, all twelve motors must read 12/17/11 N*m, and any difference is written back; imu_reader --verify-axes, where the operator tilts the robot forward and to the right and every check must pass before continuing; check_ports after plugging in any new USB device (the IMU and a CAN adapter once collided on USB identity). After re-mounting motors: read the buses, then re-measure the calibration offsets (three repeats, written back) - "skipping it means running everything on the wrong zero". Hanging checklists repeat the torque-limit check (the deploy script also self-checks at start).
Change
A fixed, read-only pre-flight run in the same order every session.
Outcome
The runbook records one earlier hardware check in the same spirit: all 12 motors' implied kp fell within 18.4-22.0 for a commanded 20, inside the kp randomization range used in training.
Mechanism
A policy transfers only if the plant matches the one it was evaluated on; the pre-flight turns silent hardware drift into a failed check before the robot moves.
Applies when
- the first command after powering a robot on
- after swapping adapters, cables or motors
- a policy that worked last session suddenly behaves differently
“python tools/set_torque.py --check # 12 颗应全对 12/17/11, 有 diff 就 --write … 插任何新 USB 设备后都先跑一次 check_ports.py(IMU 和 CANable 的 USB 身份撞过车) … python tools/calib_stance.py --repeat 3 --write # 重标 offset —— 8/9/10 重新装, 机械零位变了”
RL系统/FOLLOW THIS copy 2.md § WALK / STAND 每次开始前 / 换CAN / 装回后必做两件 The latency DR range must cover the measured deployment pipeline - 0-20 ms could not even reach the real 1-2 control steps
latency-dr-covers-measured-pipelineMeasure end-to-end action latency in control steps on your own stack (including cross-process queue boundaries), set the DR range to cover it with margin, and never import a delay count without its control frequency.
Symptom
Action latency was randomized over 0-20 ms (0-1 control step at 50 Hz), but the measured deployment path is 1-2 steps: the deploy process writes the target, an independently running BusWorker picks it up on its NEXT cycle, plus CAN round-trip - the training range could not cover the robot's actual latency at all.
Context
Fix: widen action_latency_s to 0-0.06 (0-3 steps). The external reference's "uniform 6 steps" was explicitly NOT copied - that number depends on his unknown control frequency; locally, a sweep at 0/1/2/3 steps showed walk_v5 survives all with insensitive metrics, so 6 steps "在我们这里没有依据" (has no local basis). The range was set from the measured pipeline with margin, not from a foreign constant.
Change
action_latency_s (0, 0.02) -> (0, 0.06), justified by pipeline analysis (writer/worker cycle boundary + bus time) and bounded by the local latency sweep.
Outcome
The DR band now brackets the true deployment latency; the policy trains against the delay it will actually face instead of a fictional sub-step world.
Mechanism
Latency DR only immunizes against delays inside its support; a range below the physical pipeline guarantees an untrained distribution shift at deployment. The correct range comes from tracing the pipeline's worst case (queueing boundaries + transport), and foreign step-counts are meaningless without the control rate they were measured at.
Applies when
- setting or auditing action-delay randomization
- deployment uses a separate bus/worker process from the policy loop
- importing delay-modeling numbers from other projects
“现行 0~20 ms = 0~1 个 50Hz 控制步, 而实测部署链路是 1~2 步(deploy 写 STATE.target 后, 独立跑的 BusWorker 下一轮才取走下发, 再加 CAN 往返)——现在的区间覆盖不到真机的实际延迟。… 不照抄参考来源的"统一 6 步": 那取决于他的控制频率(未知), 而我们扫过 0/1/2/3 步 … 6 步在我们这里没有依据。”
train/WALK_V7_SPEC.md § ⑤ action_latency_s 0~0.02 → 0~0.06 When hardware underperforms, audit deployment knobs before prescribing retraining
deploy-knob-attribution-before-retrainingBefore any "retrain it" decision, reproduce the symptom in sim under the exact deployment configuration; if the symptom follows the deployment knob rather than the checkpoint, fix the knob or randomize it in training - never top-up-train the skill.
Symptom
Real-robot feedback after the C4 deployment - "turning is weak" - with two retraining options on the table: top up turn training, or restart from the s1e root.
Context
The sim account showed the policy turned well (75-81% at pw1.0); the robot was deployed at power-scale 0.8. The 3-6 pp difference between C2 and C4 policies at the same power was noise; the 40-50 pp difference between power levels was the entire effect. Both proposed retraining paths would have burned budget on a non-existent training gap, and restarting from s1e would additionally have discarded the sidewalk skill that took four rungs and a coordinate-bug hunt to obtain.
Change
Decision: retrain nothing. (1) Try pw1.0 on hardware first - sim says net gain; (2) only if 1.0 is unacceptable (heat/feel), the correct training fix is power/torque randomization in the S2 plant line (one variable, fixes turn and backward together) - not skill top-up; (3) restart-from-root explicitly ranked worst.
Outcome
The "weakness" was fully explained by the deployment knob; the sim/real signatures matched the earlier power-derating law verbatim ("与 C2 时代 power 衰减主要伤非前进轴 逐字吻合").
Mechanism
The policy's competence is defined under its training plant; deployment knobs (power scale, teleop mapping, command bands) silently define a different plant. Attributing a deploy-plant effect to a training gap produces exactly the wrong fix - more training on the wrong variable.
Applies when
- real robot underperforms a skill that sim says is fine
- proposals on the table include retraining or re-rooting
- deployment uses any override the trainer never saw (power scale, remapped commands, different control rate)
“正确的训练修法不是补训转向，而是训练时加 power/力矩随机化让策略在 0.8 下自己补偿 —— 单变量，属 S2 plant 线，一次同时修好转向与后退；从 s1e 重训是最差选项：丢掉四轮 + 一个指标 bug 才换来的侧走，而 C2 的转向本来就没问题。”
train/C_LADDER_RUN.md § 3p. 三 处置顺序（回答「补训转向 还是 回 s1e 重训」：都不该） 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 A policy's gain profile is part of its contract - the one-leg policy needs per-joint gains the default profile lacks, and the manifest refused an evaluation under the default once; the recovery contract's beta was never stamped, a known gap not to repeat
gain-profile-belongs-in-the-stampStamp everything that defines the closed loop a policy was trained in - gains included - into its manifest, and make every consumer refuse a mismatch; a profile field that is not in the stamp is a silent misconfiguration waiting for an operator to forget a flag.
Symptom
A policy trained with hip_roll kp 80 and ankle_roll kp 60 behaves differently, or falls, under the default kp 20/12 profile - and the gain profile is a command-line flag an operator can forget.
Context
The one-leg line added a gain_profile field to the contract so the stamped manifest carries it; the spec's deployment note says the manifest guard blocks rl_default and that it had already bitten once in simulation (an evaluation run without the one-leg profile). The same spec states the general rule - any new profile field must be synced into the manifest builder - and names the counter-example: the recovery line's beta was never put into the manifest. The recovery line itself had decided that its anchored authority is computed from the base rl gains and written into the contract so it cannot drift with the gain flag, and that the older kp x 0.9 profile chosen in the V0 era does not match the beta contract and must not be used.
Change
Gain profile as a contract field checked at load; per-contract gain choices written into the run sheets.
Outcome
Evaluations and hardware runs of the one-leg policy run under rl_oneleg or are refused; the recovery beta gap stayed recorded as known.
Mechanism
A policy is trained against a closed loop whose gains are part of the plant; running it under other gains is an out-of-distribution plant, exactly like a wrong observation scale.
Applies when
- a skill introduces per-joint or skill-specific gains
- deployment gains are chosen by a command-line flag
- adding any new field to a policy profile
“增益档 `--profile rl_oneleg` 必须给 —— manifest 防线会拦 `rl_default`（sim 已咬合一次） … （recovery 的 β 未进 manifest 是已知缺口，不再复制）”
git:Lucen V2@origin/oneleg-line:train/ONELEG_V0_SPEC.md § §9b AGX 真机手顺 要点 / §3 契约 Friction DR was demoted after a measurement (94% success at mu 0.4 with no friction randomization) and promoted again when the action contract changed and mu 0.4 fell to 76% - DR priorities belong to a plant and contract, not to a task
friction-priority-re-measured-after-plant-changeRe-measure transfer along the friction axis for every new action contract or plant, not once per task; a DR priority settled under one action parameterization does not carry to the next.
Symptom
Getting up is all scraping and pushing against the ground, and training pinned friction at 1.0, so friction looked like the first thing to randomize.
Context
The MuJoCo gate on R0.5 (5 categories x 10 seeds x 4 friction levels) measured 100/100/98/94% at mu 1.0/0.8/0.6/0.4: degradation showed first as time (prone 3.2 -> 5.3 s), not failure, so friction DR was demoted and the DR budget earmarked for mass/COM. After the switch to the beta-anchored action space, V2.2 read 90/94/90/76%: mu 0.4 was now the weak row.
Change
V2.3 (single variable): friction DR static (1.0, 1.0) -> (0.2, 2.0), dynamic (0.15, 1.6), the HiFAR range keeping the base dynamic/static ratio; restitution untouched. Continued from v2_2.
Outcome
Isaac nominal 99.8% (DR did not hurt the nominal plant); MuJoCo 98/98/96/92% - mu 0.4 76 -> 92%, mu 1.0 back to R3.1's 98% with bounded torque.
Mechanism
How much a policy leans on friction depends on how it moves; the spec records that the sensitivity rose after the action contract changed but does not establish why.
Applies when
- changing the action space, gains or authority of an existing skill
- deciding which DR axis to spend the next rung on
- an earlier sweep justified leaving an axis unrandomized
“**μ 砍到 0.4(训练值的 40%)仍有 94%**,退化先体现在**用时**(prone 3.2→5.3 s) 而不是成败。μ≥0.8 完全无损。→ **§17 曾把"摩擦随机化提到 R4 第一项"当作优先 事项,这条实测把它降级了**”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §21 MuJoCo 复核门 ② 摩擦依赖 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) 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 The action-delay was implemented as lerp - beyond one step it extrapolated BACKWARD, so a whole lineage trained on a fictitious actuator
latency-lerp-reverse-extrapolationUnit-test plant-model code (delays, filters, randomizers) against hand-computed truth across its FULL configured range, not just the nominal case - a delay must be a queue, and any interpolation used outside [0,1] is a silent plant corruption that training will faithfully absorb.
Symptom
Every walk/stand model up to v11 had been trained on a silently wrong plant: the action-latency implementation lerp(cur, prev, lag) is only an interpolation for lag <= 1 - at lag 3 it computes 3*prev - 2*cur, a REVERSE extrapolation. With the configured (0, 0.06) s at 50 Hz (lag in [0,3]), about 2/3 of environments were adapting to actuator dynamics that do not exist.
Context
Listed as evidence item #1 for the full restart: "全部旧模型训在错误 plant 上" and the head suspect for the real robot's wild kicking. The fix replaced it with a true FIFO delay line (commit 7f13793) plus its own regression test (tests/test_action_latency.py) - but every exported ONNX predated the fix, which is part of why the lineage was frozen rather than patched.
Change
Delay implementation rewritten as an honest FIFO with unit tests; the restart baseline trained on the corrected plant from day one.
Outcome
A generation-scale training investment was revealed to have a corrupt plant underneath; the class of bug (plausible-looking math that silently changes meaning outside its valid range) got a permanent test.
Mechanism
lerp(a, b, w) leaves the segment for w > 1; used as a delay it fabricates high-gain inverted dynamics precisely in the largest-delay draws, so the policy learns compensation for an actuator that cannot exist - and DR then trains robustness to the artifact rather than to reality. No training metric can catch this: the sim is self-consistent, just wrong.
Applies when
- implementing or auditing action delay / filtering in a trainer
- a lineage behaves as if compensating dynamics nobody modeled
- deciding whether old checkpoints are salvageable after a plant bug
“动作延迟旧实现 lerp(cur, prev, lag) 在 lag>1 时是反向外插(w=3 → 3·prev−2·cur),配置 [0,0.06]s@50Hz 即 lag∈[0,3],约 2/3 env 在适应不存在的执行器动态。7f13793 已换真 FIFO,但所有 ONNX 均训于修复之前 —— 真机"乱踢"的头号嫌疑。”
train/OMNI_V0_SPEC.md § 0. 为什么从零 (1) 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) Two machines, one configuration - every gain, offset and torque limit changes only in robot.yaml, whoever edits pushes at once, both checkouts show the same commit before the robot moves, and a pulled policy file is size-checked and synced before power-off
two-machine-config-disciplineTreat the robot's configuration as a versioned artifact with one source of truth, push every change immediately, verify identical commits on every machine before a hardware session, keep hardware limits in the repo and the firmware in sync in both directions, and verify transferred model files (size, digest) before running them.
Symptom
The robot's onboard computer runs the bridge and deploy scripts from its own checkout while training and analysis happen on other machines; a hot fix left on one side, or a half-written file, silently makes the robot run something other than what was evaluated.
Context
The operator runbook's "wall version" of the two-machine discipline: configuration changes only in robot.yaml (calibration offset/sign, gains, torque limits), committed and pushed from the Mac, pulled on the robot, bridge restarted; code is not edited on the robot, and if it is, it is committed and pushed on the spot - nothing unpushed overnight; 30 seconds before every real-robot session both checkouts must show a clean status and the same last commit hash; changing tau_max requires writing the motor's limit_torque too (and the reverse); re-zeroed motors require re-measuring offsets. The recovery line added: after pulling on the robot, check the ONNX is not zero bytes (a lesson from a corruption incident on 08-12) and sync before cutting power; the recovery and main lines are separate worktrees, each pulled with --ff-only.
Change
Operating rules, pinned on the wall and repeated in the hanging checklists ("git pull, both machines on the same commit").
Outcome
The sources record the rules and the incident that produced the size check; they do not record a count of sessions the rules caught.
Mechanism
A policy is evaluated against one configuration; any divergence between the machines, or a truncated file, turns a hardware result into a result about an unknown configuration.
Applies when
- a robot's onboard computer and a workstation both hold the configuration
- someone hot-fixes code or gains on the robot
- model files are copied or pulled to the robot before a session
“改配置只改 robot.yaml(标定 offset/sign、增益、限扭全在里面)→ Mac git commit + push → NX git pull → 重启桥。 … 谁改完谁立刻推,永远不留未推送的改动过夜。 … 每次上真机前 30 秒检查:两边 git status 干净、git log -1 哈希一致。 … 铁律不变:改 tau_max 必须同步写电机 limit_torque(反之亦然);电机重新标零后 offset 必须重测回填 yaml。”
RL系统/FOLLOW THIS copy 2.md § ② 双机维护纪律(贴墙版) 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) 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) Record-high training reward hid a fully-failing DR subgroup - aggregate metrics average over draws, gates must test per condition
aggregate-metrics-mask-subgroup-failureNever gate on metrics aggregated across DR draws: evaluate at fixed representative conditions (especially the deployment-critical stratum), and if a difficulty axis matters, ramp it on measured per-stratum success rather than sampling the full range from iteration zero.
Symptom
omni_s1e trained under constant-wide latency DR (0, 0.06 s) posted the lineage's highest-ever Isaac reward (129) - while the --delay 2 smoke evaluation showed 3/3 falls from iter 1500 onward, persisting to early stop; the usable checkpoint window shrank to iters 500-1000.
Context
Diagnosis written plainly: "聚合奖励掩盖重延迟尾部子群体失败" - the aggregated reward averages over latency draws, so the majority of light-delay environments can mask the total failure of the heavy-delay tail. The remedy for the training side was a survival-gated ratchet curriculum (survival_gated_latency): the sampling cap starts at 0.02 s and rises +0.01 only when a 4096-reset window's survival (time_out share) reaches >=90%, capped at 0.06, ratchet up-only - "增益与延迟耐受一起长,不升到策略撑不住的 地方" (gain and delay tolerance grow together; never raise past what the policy can hold). The detection side was already in place from the noise-crutch episode: the per-condition smoke curve, not the training reward, is the health readout.
Change
Latency exposure made curriculum-gated on measured subgroup survival instead of uniform-from-zero; per-condition (--delay 2) smoke evaluation kept as the authoritative curve; watcher scoring adjusted (survival weighted 3x) so recovery during hard phases is not early-stopped away.
Outcome
The failure mode was caught by the smoke curve within one generation; the follow-up redesign (deterministic staged latency) superseded the ratchet, but the aggregate-masking lesson held through both.
Mechanism
Expected-return training weights each DR draw by probability, so a subgroup can contribute bounded loss while being catastrophically failed; any scalar averaged over the randomization cannot distinguish "uniformly decent" from "great on easy draws, dead on hard ones". Only conditioning the evaluation on the stratum reveals the split, and curricula should raise difficulty on measured stratum success, not on schedule.
Applies when
- training reward hits records while a fixed-condition eval degrades
- wide DR on an axis where deployment sits at one known value
- designing curricula for difficulty axes (delay, push, terrain)
“常量 latency DR (0,0.06) 从零训被证伪——Isaac reward 129 历代最高,但 --delay 2 冒烟 iter1500 起 3/3 全摔持续到早停(聚合奖励掩盖重延迟尾部子群体失败,可用窗口只剩 500/1000)。… 采样上限 0.02 起步 … ≥90% 才 +0.01s,0.06 封顶,棘轮只升不降。”
train/OMNI_V0_SPEC.md § 3. S1.5(s1e 训练塌方复盘) 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） Before training a one-leg stand, the accounts and a probe showed the default gains could not hold it at all - kp 20 needs 0.39 rad of error to carry the static roll moment, more than the whole adduction range - so per-joint gains came first, and thermal limits set the session length
single-support-gain-authority-probeBefore training a posture that loads one joint statically, compute the steady tracking error load/kp and the series stiffness against m*g*h, and prove with a simple hand-written controller that the posture can be held under the deployment gains - change the gains first if it cannot; then size session length from the thermal account.
Symptom
The one-leg line (standing on one foot, the other folded back, no hopping) had to decide whether the existing gain profile could hold single support before any reward was designed.
Context
Hardware accounts (9.792 kg, COM 0.234 m high, 170 x 80 mm feet, legs 80% of the mass): moving the COM over one foot needs 107 mm of shift and the 20 deg hip-roll adduction range gives 131 mm - geometrically enough. The static frontal moment is 7.8-9 N*m, within RS02's 17 N*m - torque is enough. But at kp 20 carrying 7.8 N*m needs 0.39 rad of tracking error, more than the entire adduction range, and the real robot had already shown it: commanded +0.17, actual -0.04 (0.21 rad droop) under load, 0.0008 rad hanging - load, not the motor. A probe (probe_oneleg.py) then showed open-loop PD cannot hold single support on physics grounds, so the criterion became "an equilibrium exists and a hand-written 4-gain COM feedback can hold it": single-support roll stiffness is hip and ankle in series and must exceed m*g*h_com = 22.5 N*m/rad; ankle kp 12 in series with hip kp 80 gives only 10.4 (open loop 16/16 fell), ankle 60 with hip 80 gives 34.3 (52% margin).
Change
A per-joint gain profile (rl_oneleg: hip_roll kp 80, ankle_roll kp 60, the rest as rl_default) - which needed per-joint gain support in robot.yaml, the bridge, deploy and the trainer's actuator groups - decided before training. Thermal account: single support makes hip_roll the dominant heat load (about 7.8 N*m against a 7 N*m continuous rating), so acceptance and demos run in segments of at most 60 s with a temperature check.
Outcome
Under rl_oneleg the hand-written feedback held six cells cleanly for 6 s (hip_roll steady torque 2.1-3.4 N*m, half the thermal budget); under rl_default the same feedback on the same cells fell 0/4. The trained V0 policy then passed its 40-cell acceptance.
Mechanism
With PD position control, the steady error needed to carry a static load is load/kp; when that error exceeds the joint's range the posture is unreachable whatever the policy does, and series compliance between joints lowers the effective stiffness below the gravity stiffness that single support demands.
Applies when
- single-support, crouched or one-arm-load postures on PD actuators
- a joint "droops" under load on hardware but tracks well when hanging
- deciding whether a new skill needs its own gain profile
“但 kp=20 时撑住 7.8 N·m 需要 **0.39 rad 跟踪误差 > 整个内收行程**。真机已实测: 命令 +0.17 实际 −0.04（droop 0.21 rad），悬挂时 0.0008 rad——是负载不是电机。 … 单支撑滚转是 hip/ankle **串联**刚度，必须 > m·g·h_com = 22.5 N·m/rad；ankle kp12 串 hip80 只有 10.4（开环 16/16 全摔），60 串 80 = 34.3（裕 52%） … **rl_default 同反馈同格 0/4 全摔**（增益档必要性对照）”
git:Lucen V2@origin/oneleg-line:train/ONELEG_V0_SPEC.md § §1-1 单脚站: 几何可行，卡点是 hip_roll 增益权限 / §2 A 线增益 / §5 probe 定谳 No parameter tuning on the floor - a failing config retries once, then it is out; anomalies go back to sim
no-field-tuning-protocolHardware time is for executing and measuring the pre-registered matrix, never for tuning: failing configs get one retry then elimination, anomalies get recorded and reproduced in sim, and contract-check bypass flags stay unused.
Symptom
Hardware sessions create pressure to fix problems live - nudge a gain, tweak a scale - which destroys attribution and risks the robot.
Context
The anomaly-handling section of the acceptance sheet is three fixed plays: (1) falls at start -> retry once at the same settings; falls again -> that configuration is eliminated, "不现场调参" (no on-site parameter tuning); (2) limit cycle or motor screech -> stop immediately, record the gain level and the joint, reproduce in sim before any discussion; (3) systematic disagreement with sim -> record it as a finding (hardware outranks sim) rather than adjusting anything to force agreement. Related guardrails elsewhere in the sheet: never pass --allow-unstamped / --allow-plant-drift to bypass manifest checks - if it errors, something real is wrong, stop and look.
Change
Field sessions restricted to executing the pre-written matrix; every fix path routed through sim reproduction and the normal config/rung process.
Outcome
Sessions stayed interpretable (each run matched a documented config) and safety overrides never became habit; anomalies arrived back in sim as reproducible cases instead of half-remembered floor stories.
Mechanism
Field-tuned values are measured under adrenaline on one floor with no logging or baselines - they contaminate the config lineage and are unattributable afterwards; and every bypass flag that skips a contract check converts a designed safety property into an operator promise.
Applies when
- a config fails or oscillates during a hardware session
- someone reaches for a live gain tweak or a bypass flag
- writing the anomaly-handling section of a deployment runbook
“起步即摔 → 换档重试一次, 仍摔则该档出局, 不现场调参。出现极限环/啸叫 → 立刻停, 记录档位与关节, 回 sim 复现再议。… 不要给 --allow-unstamped / --allow-plant-drift —— 三枚 ONNX 都已盖章 … 真要报错说明有别的问题, 停下来看。”
train/REAL_RUN_S2.md § 4. 异常处置 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) After seven patch-generations, freeze the lineage as a regression baseline, fix the structural debts, and retrain from zero
freeze-lineage-fix-structure-restartWhen successive rungs keep trading one symptom for another, ask whether the remaining problems are structural (contracts, latency, sampling, reward-table architecture); if so, freeze the lineage as regression baselines, pay the structural debts, and restart minimal - carrying forward laws and instruments, not weights and weights' patches.
Symptom
The v5-v11 walk lineage had accumulated interacting patches (reward terms, gates, clamps, per-joint scales) faster than it converged on the user's goal; v12's spec itself was superseded before training by an external review's verdict that the remaining problems were structural, not parametric.
Context
The 2026-08-05 status banner records the pivot: the walk profile was rolled back wholesale to v10b parameters, the v5-v11 lineage frozen "只作回归对照" (kept only as regression baselines), and four structural debts were named as prerequisites for a from-zero straight-walk baseline: the action-latency FIFO (fixed with its own test), the ONNX manifest contract, discrete command sampling, and a minimal reward table. The v12 spec - fully designed, partially implemented - was suspended: "本规格挂起,不再按此开训".
Change
Strategy switched from "one more patch generation" to freeze-fix-restart: lineage checkpoints retained as comparison anchors, infrastructure hardened first, then a clean retrain with a minimal reward table (this restart produced the s* generation that later became the real-robot SOTA line).
Outcome
A designed-and-ready training generation was deliberately not run - the review's structural findings outranked sunk design cost; the restart line inherited seven generations of laws (calibrations, gate batteries, falsified fixes) without inheriting their entangled reward table.
Mechanism
Patch lineages accumulate coupled terms whose interactions eventually cost more to reason about than a restart costs to train; the knowledge worth keeping is the laws and instruments (measured plant values, calibrated gates, falsified directions), not the entangled weights. A restart on hardened structure converts the lineage's lessons into a clean initial design instead of another delta.
Applies when
- repeated rungs shuffle symptoms without net progress
- an external review flags infrastructure/contract debts
- deciding between another patch generation and a clean retrain
“同日外部评审定调换路线:冻结 v5~v11 血统(只作回归对照),修结构性问题(latency FIFO 已修 tests/test_action_latency.py、ONNX manifest 契约、离散命令采样、最小奖励表)后从零训直行基线。本规格挂起,不再按此开训。”
train/WALK_V12_SPEC.md § ⚠️ 状态 (2026-08-05) Set torque limits per joint from measured gait peaks - a uniform percentage is the wrong shape, and training must use the deployed numbers
torque-limit-shape-by-measured-peaksMeasure per-joint torque peaks in the actual gait and set each limit as measured-peak x margin capped at rating; then propagate the same numbers into training and add an automated deploy-time consistency check - never derate by a uniform percentage, never let training assume torque deployment will not grant.
Symptom
A uniform 50% torque derating (18/8.5/7) had piled safety margin on the joints that never use it while cutting the busiest joint below half its measured demand.
Context
Per-joint gait peaks were measured (walk_v5 at cmd 0.3/0.6): RS06 (hip_pitch/knee) uses 5.5-5.9 N*m = 15-16% of its 36 N*m rating - cutting it to 12 is a free safety win; RS02's ankle_pitch runs at 16.2 N*m = 95% of its 17 N*m rating - "它是速度的硬件瓶颈", no room to cut; RS00 measured 36-44%, capped at 11. The resulting shape 12/17/11 replaced the uniform percentage. Sweeps across several limit sets (rated / 50% / 14-17-11 / 12-17-11) produced identical speed, lift, and landing force - within this range the limits do not shape the gait; what matters is consistency: "关键是训练和硬件必须是同一个数", because the exporter fills effort_limit from tau_limit, and a policy trained at rated 36/17/14 "会假设有三倍力矩可用" while deployed at 12/17/11 (exactly the v5 cross-generation inconsistency later suspected in its wild kicking).
Change
robot.yaml tau_limit set to the measured-shape 12/17/11, firmware written to match, and train/isaac_values.py regenerated so training sees the same limits; the deploy tool self-checks limits against robot.yaml on every run.
Outcome
Free safety margin captured where demand is low, the real bottleneck joint left at rating, and the train/deploy torque worlds unified with an automated consistency check.
Mechanism
Torque demand is grossly unequal across joints in a gait (15% vs 95% of rating here); a uniform percentage misallocates the safety budget by construction. And since the trainer treats effort_limit as a plant truth, any train/deploy mismatch is an invisible plant gap of exactly the mismatch ratio.
Applies when
- choosing safety torque limits for a legged platform
- training-vs-deployment actuator limit audit
- one joint runs near rating while others idle
“曾用统一 50%(18/8.5/7)是错的形状: 把余量堆在用不到的 RS06 上, 却把 ankle_pitch 砍到需求的 52%。… RS02 在 0.6 m/s 已用到额定 95%, 它是速度的硬件瓶颈 … 实测多组限幅 … 完全一致 —— 限幅在这个范围对步态零影响, 关键是训练和硬件必须是同一个数。… 若训练仍按额定 36/17/14, 学出的策略会假设有三倍力矩可用。”
train/WALK_V6_MINIMAL.md § 3. 训练侧必须同步的一件事 A binary reward band on the swing knee had zero gradient everywhere below it, so the one-leg policy parked in an unloaded "fake touchdown" that Isaac's 5 N threshold scored as success and MuJoCo showed as real pressing - a capped constant-gradient ramp, retrained from scratch, passed 40/40
binary-band-reward-fake-touchdownShape approach-to-target rewards as capped ramps with gradient from the starting posture, never as bands or indicators; and compare contact-based terms across simulators, because a policy riding just under a force threshold looks perfect in one and wrong in the other.
Symptom
At iteration 1,000 of the first one-leg run the swing foot never lifted: the policy stood with the "raised" foot resting lightly on the ground. In Isaac the contact-match term paid 96% of full marks; the same policy in MuJoCo pressed that foot on the ground for 450 frames.
Context
The swing-leg goal was "shank folded fully back" (knee 1.5-1.95 rad), rewarded as a binary band: +0.8 inside [1.5, 1.95], zero elsewhere. From knee 0.05 to 1.5 rad the term was flat. Contact is judged at a 5 N force threshold, so a foot carrying less than 5 N counts as lifted. The walk line had hit the same disease with a binary indicator (v4) and fixed it with a capped ramp (knee_swing_amplitude).
Change
swing_knee_fold changed from the binary band to a ramp clamp(|q|/1.5, 0, 1) - a constant gradient capped near 86 deg - and the policy was retrained from scratch (V0r1). After the first real-robot try showed the fold still too low, its weight went 0.8 -> 2.0 (V0.1).
Outcome
V0r1 model_2300 passed the full acceptance 40/40 (swing knee 1.72 rad, about 98.5 deg) and was stamped as oneleg_v0.onnx; the cross-simulator disagreement is recorded as the thing that caught the cheat.
Mechanism
A reward that is flat until the target is reached gives no gradient to approach it, so the policy settles for the nearest state other terms reward - here, a foot that satisfies the contact threshold without lifting; a second simulator with different contact force resolution exposes such threshold-riding.
Applies when
- rewarding a posture target with an in-band / out-of-band indicator
- a contact threshold decides whether a foot counts as lifted
- trainer-side contact terms are near full marks while the video looks wrong
“初版二值带 [1.5,1.95] 在膝 0.05→1.5 全程零梯度，策略停在"卸力虚点地"（Isaac 5N 阈下 contact_match 96% 满分 / MuJoCo 同策略 450 帧实压——跨仿真器互证抓作弊）；v4 二值指示同型病，按 knee_swing_amplitude 判例改常数梯度封顶 ramp，从零重训 … **oneleg_v0.onnx = V0r1 model_2300, 40/40 PASS**”
git:Lucen V2@origin/oneleg-line:train/ONELEG_V0_SPEC.md § §4 奖励表 swing_knee_fold 行 / §8 核查单 5 Privileged signals (true velocity, foot force, foot height) go to the critic only
observation-honesty-critic-onlyTreat the actor observation vector as a hardware contract: every element must exist on the real robot with realistic noise; privileged simulator truths belong in the critic only.
Symptom
Policies trained on ground-truth base linear velocity work in sim and fail on hardware, where only a drifting IMU and encoders exist - the policy has learned to depend on a signal that does not survive deployment.
Context
Many open-source locomotion stacks feed simulator ground-truth linear velocity to the actor. The reference team refused: the real robot has no ground-truth velocity. Asymmetric actor-critic keeps the training benefit of privileged information without deploying the dependency.
Change
Route ground-truth velocity, foot contact forces, and foot heights to the critic only; the actor observes exclusively signals that exist on hardware (IMU-derived quantities, encoders, commands, previous actions).
Outcome
Recorded as adopted doctrine in Lucen's experience log; the trained actor's input contract matches what the real robot can actually produce.
Mechanism
The critic is discarded at deployment, so it may consume any privileged state to reduce value-estimation variance; the actor's observation set is a deployment contract - anything in it that hardware cannot supply (or supplies with different noise/drift) becomes a train/deploy distribution shift the policy was never trained to handle.
Applies when
- designing actor/critic observation spaces
- reviewing a config where the actor sees base_lin_vel or contact forces
- sim policy is strong but real robot drifts, oscillates, or falls without obvious actuator cause
“很多开源代码库把真值线速度喂给策略，Asimov 团队没有，因为真机上没有真值速度，只有会漂的 IMU 和编码器；用完美速度训练出来的策略会依赖它，然后在硬件上失效。真值速度、足底力、足高统统只给 critic”
Experience.md § 观测空间的诚实性 (line 7) 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 Diagnose a behavior failure by enumerating hypotheses and auditing each against the actual config, cheapest first
hypothesis-table-code-auditBefore changing anything, write the full hypothesis list for the symptom and audit each against the resolved config and measured magnitudes, cheapest check first; train only on the survivors.
Symptom
Real robot leaned forward "wanting to walk" but dragged its feet instead of lifting them - a symptom with many plausible causes and no obvious single fix.
Context
Seven hypotheses were listed and each checked against the actual training config files (velocity_env_cfg.py, isaac_values.py), ordered by check cost: missing foot clearance term (CONFIRMED, primary - feet_air_time existed but no swing-height term at all); energy penalties dominating (REJECTED - energy terms total -0.19 vs tracking +1.2, 16%); command range too narrow (CONFIRMED - (0.15,0.35)); nominal pose too crouched / action scale too small (HALF - knee 0.5 rad = 28.6 deg deep, scale fine); mixed PD across motor types (REJECTED - already grouped); missing base-height reward (REJECTED - present at -5.0); height-drop termination (REJECTED - none exists, which itself became finding #4 of the fix list).
Change
The audit produced a ranked fix list (add clearance penalty; widen speed range; reduce nominal crouch) with each rejected hypothesis documented so it would not be re-litigated.
Outcome
Three confirmed causes fixed over v5/v6: swing height went 22-23 mm -> 34 mm, tracking 81% -> 87%; the rejected hypotheses stayed rejected (no wasted rungs on energy weights or PD grouping).
Mechanism
Multi-cause symptoms invite guess-and-train loops; a written hypothesis table forces each candidate to be confirmed or rejected against actual values (not impressions), and cost-ordering the checks means most hypotheses die for the price of reading a config.
Applies when
- a real or sim behavior failure has multiple plausible causes
- the team is about to "try a fix" without an audit
- post-mortems keep re-proposing already-rejected causes
“真机现象:躯干前倾像要走,脚抬不起来(拖着蹭)。按成本从低到高逐条核查 … | 1 | 缺 foot clearance | ✅ 成立,首要 | 有 feet_air_time,无任何摆动足高度项 | | 2 | 能量惩罚压过跟踪 | ❌ 不成立 | 能量类合计 −0.19,跟踪 +1.2,只占 16% |”
train/WALK_DIAGNOSIS.md § walk 拖地问题 — 七条假设的代码核查结果 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) 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 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) Model CAN polling skew - joint observations are 6-9 ms stale by read order
can-timing-skew-modelingIf joints are read sequentially over a shared bus, reproduce the per-group observation staleness in sim (or randomize it over the measured range) - synchronous observations are a privileged fiction.
Symptom
Policies trained with synchronous joint observations degrade on hardware where motors are polled sequentially over CAN - hip data is already 6-9 ms old by the time ankle data arrives.
Context
Menlo's core sim2real finding on a leg platform of almost identical mass to Lucen's. CAN is a sequential bus: one poll cycle reads motors in a fixed order, so the observation vector mixes timestamps. Lucen runs 6:6 motors on a dual CAN split, so the problem transfers one-to-one.
Change
Explicitly model CAN timing skew in sim: give the joint groups different observation delays matching physical read order. Menlo went further - running real firmware in the loop with a motor simulator between MuJoCo and firmware injecting 0.4-2 ms uniformly distributed delay.
Outcome
Reported by the reference team as their core sim2real enabler on a same-scale platform; recorded in Lucen's experience log as directly applicable ("这个问题一模一样").
Mechanism
A policy exploits any cross-joint temporal coherence present in training observations; when hardware breaks that coherence per bus position, the learned feedback acts on inconsistent state estimates, injecting phase error exactly at the control bandwidth.
Conflicts
Second-hand episode: outcome numbers are the reference team's report, not a Lucen-run experiment; Lucen adopted the requirement but the corpus has no Lucen A/B of skew-on vs skew-off.
Applies when
- robot polls actuators sequentially over CAN/RS485 or similar shared bus
- sim2real degradation appears as jitter or oscillation not seen in sim
- designing the observation/delay model before a training run
“电机走 CAN 是顺序轮询的，髋部电机的数据到踝部电机上报时已经陈旧了 6-9 ms，他们直接在仿真里显式建模了 CAN 时序偏斜，按读取顺序给三组关节不同的观测延迟。… 注入 0.4–2 ms 的均匀分布延迟。你们是 6:6 双 CAN 分总线，这个问题一模一样”
Experience.md § 执行器 + 时序建模 (line 6) 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 ⚠️ The deploy-side walk/recovery switch - into recovery at tilt > 65 deg held 0.3 s, back at tilt < 15 deg with angular rate < 1 rad/s and straight knees held 1 s, a 15 s timeout, last action cleared both ways - and the handoff steps the design required are only partly implemented
walk-recovery-fsm-handoffSpecify a deploy-time controller switch as hysteretic, time-filtered predicates the robot can measure (proxy what it cannot, e.g. straight knees for height), a timeout that ends in a safe stop, and a complete handoff (history, clock, last action, command ramp) - then test that the code performs every handoff step, because the design document is not the implementation.
Symptom
With a recovery policy and a locomotion policy as separate networks, the robot needs a switch: when is it "fallen", when is it "up", and what state must be reset so the next policy does not act on the previous one's history.
Context
The 08-09 design: enter recovery when fallen (tilt > 55 deg or height < 0.60 x 0.384 m) for 150 ms, leave for a stand-hold when upright (tilt < 12 deg, height > 0.85 x 0.384 m, feet steady, |omega| < 0.8) for 400 ms - wide entry, strict exit, hysteresis - then a mandatory handoff trio before walking resumes (reset the walking policy's observation history, restart its phase clock at 0, clear its previous action and latency buffer) and a command ramp instead of a jump. The 08-14 implementation in deploy_policy (--recovery-policy): each policy under its own manifest contract (walk: nominal + scale; recovery: beta-anchored), the same rl_default gains, the tilt cutoff disabled; RECOVERY at tilt > 65 deg for 0.3 s; LOCO again at tilt < 15 deg and |omega| < 1 and knees straight (< 0.35 rad) for 1 s - the robot's computer has no height estimate, and straight knees stand in for height so a V3.0-style upright kneel cannot pass as standing; RECOVERY longer than 15 s ends in a safe stop; last_action cleared on both switches; command forced to 0 during RECOVERY; power scaling applies to LOCO only.
Change
An open account was written down with it: the recovery end state (0.355 m stance, hip yaw -/+27 deg) is outside the walking policy's training start distribution, so the first test must use stand / zero command as LOCO, and the long-term fix is to widen the walking policy's initial states rather than bend recovery's stance to suit walking.
Outcome
The spec records only a successful compile (py_compile); the hang test was left to be done on site. The operator runbook carries the three-step procedure (hang with stand as LOCO, mat and push, then omni walk as LOCO) but no outcome.
Mechanism
Two policies trained separately each assume their own history, clock and last action; a switch that carries any of them across feeds the next policy a state it never saw - the design called this "the walking policy seeing a ghost history".
Conflicts
The 08-09 design requires resetting history, clock and previous action plus a command ramp; the 08-14 implementation records last_action clearing and a zero command during recovery; the one-leg spec of 2026-09-14 lists the handoff hygiene as specified but not implemented - reset_history() is called by nothing (a 215-dim policy would carry four frames of pre-fall history), the phase clock is not zeroed, and there is no command ramp back to LOCO. No hardware run of the FSM is recorded in either source.
Applies when
- switching between separately trained policies on hardware
- a policy with history or phase observations is re-enabled mid-run
- the robot lacks a sensor the switching criterion was designed around
“**判据**:进 RECOVERY = 倾角 >65°(`--fall-tilt-deg`)持续 0.3 s;回 LOCO = §5 真机可测子集:倾角 <15° ∧ |ω|<1 ∧ **膝直 <0.35 rad(NX 无高度观测, 高度门用膝直代理 —— 防 V3.0 型"跪坐但直立"误判)** 持续 1 s (`--recover-hold`);RECOVERY 单次 >15 s(`--recovery-max-time`)安全停。 … **切换卫生**:两向切换 last_action 清零;RECOVERY 态 cmd 强制 0”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §50 FSM 双策略调度(2026-08-14,用户令):deploy_policy --recovery-policy