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
144 cards matching “torque-penalty-bought-by-leg-bracing”.
The best checkpoint to SHIP is not the best checkpoint to CONTINUE FROM - maturity is capital against adaptation shock
root-maturity-vs-product-qualityDecide shipping points and fork roots separately: gates rank products, but a root candidate must prove itself by surviving a continuation under the next rung's shift (dual-arm if in doubt) - and prefer the more-trained point as root when product metrics conflict with maturity.
Symptom
A band re-audit found s1e-300 beat the incumbent root s1e-500 on nearly every quality gate (stepping 19/20 vs 13/20 with historically-best 26.9 mm swing, speed gate 14/20 vs 2/20, heading 26 vs 54 deg/20 s) - suggesting the root had been mis-picked and the younger point should take over.
Context
The dual-arm control settled it the other way: continuing the S2 PD rung from s1e-500 adapted smoothly (3/3 smoke throughout), while the b300 control arm (same config, from s1e-300) fell into a survival valley under the PD shock (+100 iters: 1/3 -> 0/3), never climbed out within budget, and its 800-iter product scored 13/20 survival - eliminated. Verdict: "幼年点自身指标再好也扛不住新 DR 适应冲击, 成熟度是本钱,s1e-500 根被数据背书" - a young point's own metrics, however good, do not survive new-DR adaptation shock; maturity is capital. The audit still yielded value: the band scan (200-1000, per-100) mapped the lineage's arc (200 dragging -> 300 peak -> 400+ decay -> 900+ drift blowout), and 300 remains the better PRODUCT answer for shipping-as-is questions.
Change
Selection doctrine split into two questions with different answers: best-product point (quality gates at the point itself) vs best-root point (survives adaptation shocks; more training age = more capital), each decided by its own evidence - and root claims settled by a dual-arm continuation test, not by point metrics.
Outcome
s1e-500 kept the root role with data behind it; the S2e ladder built on it passed rung after rung, while the b300 line was closed at the cost of one control arm.
Mechanism
Early checkpoints sit near sharp optima with less accumulated robustness structure; their headline metrics reflect the narrow training distribution, not resilience to distribution shifts. A continuation rung is itself a distribution shift, so the root property being selected for is shock tolerance - observable only by actually continuing, never by static gates.
Applies when
- a younger checkpoint outscores the current root on quality gates
- choosing the base for a robustification or command ladder
- a continuation run stalls in an early survival valley
“b300 对照臂 … PD 冲击下存活谷(+100 起 1/3→0/3),预算尽未爬出,800 档 20-seed 存活 13/20 出局——幼年点自身指标再好也扛不住新 DR 适应冲击,成熟度是本钱,s1e-500 根被数据背书”
train/README.md § omni_s2e_pd (b300 对照臂) / s1e 选点重审 Armature must be N^2 x rotor inertia, never 0 - measure it no-load
armature-n2-rotor-inertiaEvery geared actuator carries N^2 * I_rotor of reflected inertia at the joint; set armature from a no-load measurement, never leave it 0 and never guess it.
Symptom
Sim joints accelerate more easily than real joints; old MJCF had armature = 0 (rotor reflected inertia entirely unmodeled), a systematic sim2real gap on every joint.
Context
Original hand-written MJCF plant used armature 0. The team derived and then measured the correct value: torque needed at the rotor is I_rotor * N * alpha; after the N:1 gearbox the output shaft "feels" an extra N^2 * I_rotor of inertia. With a 9:1 reduction that is an 81x amplification of the rotor inertia, far too large to ignore.
Change
Set per-motor armature from no-load (motor out of the robot) measurement instead of 0: RS06 = 0.0070 kg*m^2, RS02 = 0.0032 kg*m^2, RS00 = 0.0015 kg*m^2. Landed together with measured friction as the first fully-measured plant parameter set.
Outcome
Plant parameters "第一次全部来自实测" (first time all from measurement); became the frozen plant baseline for all subsequent training generations.
Mechanism
Reflected inertia scales with the square of the gear ratio: the rotor spins N times faster than the joint, so its kinetic energy (and the torque needed to accelerate it) appears N^2 larger at the output. Omitting it makes simulated joints unrealistically fast/light, so policies learn action rates the real actuator cannot deliver.
Applies when
- building or auditing a simulation plant model for a geared/QDD actuator
- sim policy moves joints faster or snappier than the real robot can
- MJCF/URDF review shows armature or rotor inertia set to 0 or a default
“armature 转子反射惯量有问题 在sim里面一定要处理 不能是0，空机测试。转子处需要的力矩 = I_rotor × N × α 经减速箱放大 N 倍后 = N² × I_rotor × α 所以输出轴"感觉到"多了一个 N² × I_rotor 的惯量。 这就是 armature。… 关键是那个平方。减速比 9:1 就放大 81 倍。”
Experience.md § # armature 转子反射惯量有问题 (line 9) 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) 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) 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) Symmetrizing the config made the gait MORE asymmetric - the asymmetry lived in the policy weights
asymmetry-in-weights-not-configLocalize a persistent asymmetry by intervening at the config layer first: if the symptom survives (or worsens), it is in the weights - fix it with symmetry-constrained training, not with trims or offsets.
Symptom
walk_v1 on hardware: straight-line command curved 149 deg in 15 s (9.9 deg/s) with 3.06 m lateral runout; turn gain +31% one way vs +129% the other (75% difference); knee asymmetry 4.4 deg in sim, 9.6 deg on the robot.
Context
The obvious suspect was the asymmetric default pose in the config. The decisive test: symmetrize standing_pose and run the SAME policy in sim - the asymmetry got LARGER (hip_pitch 6.8 -> 9.3 deg). Root cause therefore not in the config but baked into the policy weights: PPO without a symmetry constraint commonly converges one-sided, because splitting the work 50/50 and loading one side yield the same return, and the gradient falls randomly into one of the equivalent optima.
Change
Fix redirected from config trimming to retraining with mirror data augmentation (walk_v2 spec) - a weights-level fix for a weights-level disease.
Outcome
With augmentation (and the symmetric-default precondition), stand_v1 reached 0.0 deg asymmetry on all six joint pairs (from 4.4-7.7 deg), height fluctuation 7 mm -> 1 mm, mean |action| down 33%.
Mechanism
Reward-equivalent solution families (who carries the load) leave the symmetric solution unpreferred; SGD picks an arbitrary member and entrenches it. Config changes move the coordinate frame around the entrenched asymmetric function - they cannot move the function. The counterfactual test (change config, watch symptom) localizes the layer the disease lives in.
Applies when
- a robot veers or loads one side despite a symmetric-looking config
- deciding between config trims and retraining for an asymmetry
- mirrored-turn gains differ by tens of percent
“根因不在配置里:把 standing_pose 对称化后在 sim 里跑同一策略,不对称反而变大(hip_pitch 6.8°→9.3°)—— 说明不对称烙在策略权重里。这是无对称约束的 PPO 的常见收敛结果(左右各担一半与一边多担的回报相同,梯度会随机落进其中一个)。”
train/RETRAIN_v2.md § 1. 为什么是对称增强(证据) Add a termination that makes the degenerate strategy fatal - no height cut-off meant crouch-shuffling could live forever
termination-closes-degenerate-basinFor each known degenerate strategy, check whether the termination set makes it fatal; if the robot can live indefinitely inside the degenerate posture, add a termination just past the intended operating envelope rather than escalating penalties.
Symptom
Crouched foot-dragging survived indefinitely because the termination set contained only bad_orientation (40 deg) and base contact - there was no height termination at all, so a deep squat was a viable long-term strategy.
Context
The hypothesis audit found the missing termination (hypothesis 7); the cross-check against published configs found the field practice: Booster terminates at 0.45 m (38% of body height) and the research warning is that the termination height must not be so low that crouching survives it. The proposed value: 0.32 m, just below the walk crouch base height 0.3739 - a deep squat terminates immediately, "断掉蹲着蹭的活路" (cutting off the crouch-shuffle's livelihood).
Change
Add height termination at 0.32 m as a second-priority item of the walk fix package, alongside restoring base_height_l2 to -10.
Outcome
Entered the v5/v6 fix package under which the crouch-shuffle optimum disappeared (34 mm clearance, 87% tracking by v6).
Mechanism
Termination conditions define which strategies exist at all: a reward penalty prices a behavior, but a termination deletes its future returns entirely. Degenerate basins that are merely penalized can remain optimal under enough tracking pressure; a termination placed between the degenerate posture and the intended one makes the basin unreachable as a steady state.
Applies when
- a degenerate but stable behavior persists across reward tunings
- auditing termination conditions for a locomotion task
- a policy exploits the gap between penalized and terminated states
“加终止高度:研究第 6 条"终止高度不能低到让蹲着也能活"。我们完全没有高度终止。建议 0.32 m(略低于 walk 蹲姿基座高 0.3739,深蹲即终止)。… 加终止高度 0.32 m(深蹲即终止,断掉蹲着蹭的活路)”
train/WALK_DIAGNOSIS.md § 修正 ④ / 最终改动清单 第二优先 Prone get-up sat at 0/159 until two gated hinge terms moved the seated feet - first sideways (561 -> 360 mm), then fore-aft (-168 -> +56 mm) - and success went to 158/159 with nothing else changed
prone-dead-end-is-foot-placementWhen a stuck state and a successful state differ geometrically, penalize the discriminating quantity with a gated hinge that is exactly zero in the state the policy actually reaches (measure it - not the nominal), then re-probe: flattening one axis can move the discriminant to another.
Symptom
Prone falls always righted and then sat with the feet splayed wide or tucked behind the hips, from where the policy never stood (0% for four generations).
Context
Four lines of evidence pointed at foot position: the configuration probe (ankles 215 mm apart stood 52.3%, 561 mm apart 0.0%); FK showing the action contract's nominal (a = 0) is itself a 465 mm straddle, so the action_rate and still terms were pulling toward the splits; biomechanics (feet tucked under the body cut peak hip-extension torque 148.8 -> 32.7 N*m, -78%); and HoST's foot-displacement term, which this reward table lacked. The earlier "not a reward hole" reading was corrected to "a gradient hole, not a level hole": at the dead point the heaviest term (upright) was saturated with zero gradient, still paid for not moving, and the one live gradient (base_height) pointed at the thigh-horizontal torque barrier. A prone ROM scan had already ruled out pushing up from prone.
Change
R0.4: feet_spread_excess = clamp(ankle distance - 0.215, 0, inf) x upright gate, weight -2.0, plus a height-decay factor added after measuring that the policy's real standing stance was 406 mm, not the 215 mm nominal (the plain version would have taxed every successful stand 0.38/s). R0.5: the same shape on the fore-aft axis, feet_fore_seated = |fore-aft offset - 0.05| x upright gate x height decay, target +50 mm (the measured natural offset of standing postures). One variable per rung.
Outcome
R0.4: seated ankle distance 561 -> 360 mm, supine/side exactly unchanged, prone 0 -> 1.9%, mid 45.9 -> 62.2%; a probe then showed the discriminant had moved to the fore-aft axis (standing starts +42 to +51 mm, the prone seat -168 mm). R0.5: supine 99.4, prone 99.4, side 100, mid 100%, re-falls 0%; both geometry terms collapsed to ~0 near iteration 13,100 as base_height rose, and the prone fore-aft offset went -168 -> +56 mm - the term's own target, closing the causal chain. The cost, unmeasured at the time: action jitter rose 33% (sum |da|^2 6.82 -> 9.06).
Mechanism
An upright-gated hinge is inert while the robot rolls and exactly zero in the achieved stance, so it adds gradient only inside the stuck basin; a seated robot with its feet behind or outside its COM must make a kinematically unfavourable transition to stand, and moving the feet under the body removes it.
Applies when
- a get-up or transition skill fails from one start category only
- successful and failed episodes differ in a measurable geometric quantity
- a shaping term might tax the posture successful episodes already use
“`recovery_r0_5`,唯一变量 = 追加 `feet_fore_seated`(与 R0.4 同形状,只换测量轴)。 … 对照 R0.4 的 prone(3.1%,360 mm,**−168 mm**):前后偏移从 −168 走到 +56, 正是这一项的目标量,**判别量被消掉后成功率随之到顶** —— 因果链完整。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §20 R0.5(前后向脚位置项):成功率门全过 —— prone 0/159 → 158/159 Pre-register the ladder's risks and how each future result will be read - before training
preregister-risks-and-fork-readingsBefore a training ladder or risky rung, write the risks, the stop rules, and how every plausible outcome will be interpreted - then do not edit them after seeing results.
Symptom
Without pre-registration, ladder results get rationalized after the fact; the team had already seen post-hoc reads go wrong and adopted written pre-commitment.
Context
The C-ladder execution sheet opens with three numbered pre-registered risks: (1) S2 plant robustness will not carry into omni - a full S2 redo is budgeted from the start; (2) the s1e recipe has a collapse valley at iter 1500+ (recorded twice), so every rung runs a watch_ckpt --every 100 smoke loop with stop-on-degradation; (3) the root's OOD survival edge may partly be "it is slower / commits less" - with the reading fixed in advance: if C1 training raises tracking while survival drops, the edge was bought with slowness, and root selection reopens. Later rungs went further, pre-registering a full result-to-conclusion table for the A/B arms ("预注册读法（事后不改）") and even pre-registering the author's own doubt that a level would fail and what its failure would prove.
Change
Standing practice: before each rung, write down (a) known risks with their mitigations, (b) the interpretation of each possible outcome, (c) stop criteria - all frozen before the run starts ("开训前写死，事后不许改").
Outcome
When arm-A/arm-B and redo results arrived, conclusions were read off the pre-registered table instead of argued; a predicted-likely-FAIL level (C4-redo3) was still run because its pre-registered value was eliminating the regularization hypothesis - which it did.
Mechanism
Pre-commitment converts each training run into a decisive experiment: outcomes falsify or confirm named hypotheses instead of being absorbed into a story; it also makes negative results valuable (a FAIL that eliminates a hypothesis advances the search).
Applies when
- launching a multi-rung training ladder
- running an A/B fork whose outcome will drive a fork/root decision
- a rung is expected to fail but is run for its diagnostic value
“三条预注册风险 … 若 C1 训后跟踪提上去而存活掉下来，说明这条优势是速度买的、不是通用性 → 那时重开选根。”
train/C_LADDER_RUN.md § 0. 三条预注册风险 Adapting a lineage to one plant increment needs hundreds of iterations, not thousands - long runs only buy specialization
continuation-budget-not-from-zeroBudget continuation rungs by increment class (hundreds of iterations for plant pins and smooth shifts, ~1000-1500 only for behavior-demanding changes like push), enforce a hard cap with frequent evaluation, and treat remaining budget as a reason to stop, not to continue.
Symptom
The default "6000 iterations per rung" (a from-zero-scale budget) was about to be applied to continuation rungs whose only change is one plant/DR increment - overspending compute and, worse, giving each rung thousands of iterations to specialize away retained skills.
Context
The 2026-08-07 budget table replaced the default with "最低适应窗口 + 每 100 iter 验收 + hard cap" scaled to the increment's difficulty: fixed-latency levels 300-500 (cap 500-800; the base has already seen in-band values, this only pins the plant); PD full-band 700 (cap 1000; kp+/-20%/kd+/-30% clearly widens the actuator family); COM +/-20 mm 500 (cap 800; a smooth dynamics shift); friction DR 700 (cap 1000; contact and actuator friction change the gait/contact solution together); push 1000 (cap 1500; a non-static plant change requiring recovery behavior - hardest). Rationale: "续训适应一个 plant 增量不需要从零量级的预算,跑长了只是给特化时间". The C ladder reused the scheme (per-rung caps 500-2000 by increment type), and the deep-training hazard got its own name when long runs sold quality ("深适应卖质量" - deep adaptation sells quality).
Change
Per-rung iteration budgets set by increment class with hard caps and 100-iter watch loops; checkpoint selection inside the window by the smoke curve, never "run to cap because budget remains".
Outcome
S2/C rungs completed in 300-1500 iterations each; the recurring late-run degradations (collapse valleys at 1500+, vx+0.30 decay) fell outside most rungs' caps instead of inside their runs.
Mechanism
A continuation rung's learning problem is local robustification around an existing optimum - low sample complexity; iterations past adaptation are spent sharpening onto the current distribution, which is exactly how retained skills and margins erode. Budgets sized to the increment bound both compute and the specialization damage window.
Applies when
- planning iteration budgets for a robustification or command ladder
- a continuation run keeps improving its training metric late
- retained skills decay in the back half of long continuation runs
“「最低适应窗口 + 每 100 iter 验收(watch_ckpt --every 100)+ hard cap」—— 续训适应一个 plant 增量不需要从零量级的预算,跑长了只是给特化时间 … ⑥ push | 1000 | 1500 | 非静态 plant 变化,要学 recovery 行为,最难”
train/OMNI_V0_SPEC.md § 4. 每级 iter 预算(2026-08-07 用户定) A plateau in a training curve was a population mix, not a half-learned skill - 60% standing at 0.372 m and 40% sitting at 0.19 m - and a category at hard zero stayed at zero through 3,000 more iterations
zero-partial-credit-is-not-an-iteration-problemBefore buying iterations for a plateau, split the metric by category and check whether it is bimodal; a category at hard zero with no partial credit is missing a capability or a reachable state, and more iterations under an unchanged config will only polish the categories that already work.
Symptom
After R0.1 the training-side base_height sat near 0.29 m and the curve was still climbing at the iteration cap, which read as "train it longer".
Context
Candidate A (user decision) was a child-run from R0.1's last checkpoint with zero config change - the logged env.yaml files differ only in log_dir - for 3,000 more iterations (R0.2). The per-category acceptance split was already available: prone had scored 0/156 with no partial credit.
Change
Continue training unchanged, then read the result by category rather than by the pooled curve.
Outcome
supine 91.5 -> 96.4%, side 82.2 -> 87.9%, get-up 0.96 -> 0.84 s, pose error 0.91 -> 0.54 - all improvements to categories that already stood. Prone stayed 0/153; mid 55.9 -> 41.2% was within noise (n=34). Height by category was binary - standing groups 0.372/0.373 m, seated groups 0.187/0.194 m, nothing between - so the pooled 0.29 was 0.61 x 0.372 + 0.39 x 0.19 = 0.30 (measured 0.307): six in ten standing, four in ten sitting. The rendered prone episode was still kneel-sitting at t = 8 s.
Mechanism
The pooled mean of a binary outcome only moves when the mix moves; PPO kept polishing the subpopulation that already succeeded while the failing one produced no advantage signal to follow.
Conflicts
R0.2 recorded the missing capability as "prone lacks rolling over"; R0.3's end-state confusion matrix retracted that - prone had righted its torso in 159/159 episodes and was failing to stand from the W-sit. The lesson that iterations could not fix it holds; the named cause was wrong.
Applies when
- a training curve plateaus while acceptance shows one category at zero
- deciding between "train longer" and "change something"
- pooled training metrics are read as the typical episode
“**分类别 h 中位把"平台 = 人口混合"钉死了**:数值是**二值**的 —— 站立组 0.372/0.373,坐姿组 0.187/0.194,**中间没有过渡态**。 … **这也是本仓此后读该指标的通用告诫:全体混合的期望会把 双峰分布平均成一个不存在的中间值,必须分类别看。** … **结论:A 不能过门,原因确定为 prone 缺"翻身"这一技能,不是迭代不够。**”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §13 R0.2(recovery_r0_2,child-run 续训):A 走完了 —— 推不动 prone The median of a bimodal metric lands in the empty gap - check the distribution, and never judge swing on 3 seeds
median-hides-bimodal-distributionBefore quoting a median or mean, look at the distribution; report suspected-bimodal metrics as mode share plus per-mode ranges, use small-seed smoke runs only to screen trends, and size the seed count for decisions by the share resolution you need (here: 20).
Symptom
Years of "high swing variance" and undecidable 3-seed swing readings turned out to be one fact: the metric was bimodal all along - "历代 swing 高方差与 3-seed 不可判由此定性:一直在测双稳态系统" - and every median reported from it (e.g. 12.1 mm) described a value no seed ever produced.
Context
Concrete instances: s2e_pd-1400's 20 seeds split 2.6-4.9 mm vs 19.3-24.0 mm with zero seeds between; s1e-500 read 3.9 mm on seeds 0-2 but 23.1 mm median over 20 seeds; a 3-seed reading of 19.3 was logged as "double-peak optimism, lesson recurrence #4". The selection re-audit codified the sampling rule: "3-seed 的 swing 读数不可判点, 只能筛带,选点必须 20-seed" - 3 seeds may screen a band, only 20 seeds may pick a point.
Change
Swing (and any suspected multi-modal metric) reported as mode shares plus per-mode ranges instead of a bare median; 3-seed smoke numbers demoted to band-screening; all shipping/selection decisions moved to 20-seed batteries.
Outcome
The "swing debt" bookkeeping was reinterpreted as basin probability (see swing-bistability-damping-switch), and checkpoint selection stopped being whipsawed by which basin the first three seeds happened to fall into.
Mechanism
Central-tendency statistics presuppose unimodality; on a bimodal distribution the median tracks the mode SHARE, not any achievable behavior, and small samples alias the share entirely. Mode-aware reporting (share + per-mode stats) is the only faithful summary, and the needed sample size is set by the share resolution required.
Applies when
- a quality metric shows chronic high variance across seeds
- 3-seed smoke readings contradict 20-seed batteries
- reporting swing height, clearance, or any basin-prone metric
“中位数落在空档里,「swing 债 −11mm」实为「50% 概率掉进拖地吸引子」。历代 swing 高方差与 3-seed 不可判由此定性:一直在测双稳态系统。… swing 跨 seed 双峰 (500 在 seed0~2 只读 3.9mm, 20-seed 中位 23.1) —— 3-seed 的 swing 读数不可判点, 只能筛带, 选点必须 20-seed。”
train/README.md § swing 双稳态定性 / s1e 选点重审 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） Training the final recipe from scratch in one run - every mechanism the lineage had accumulated - produced 0% and a seated robot; the order in which the lineage acquired those mechanisms was part of why it worked
curriculum-history-is-part-of-the-productA recipe that ends a lineage is not a recipe for a from-scratch run: consolidate it as ordered curriculum phases matching how the lineage acquired its mechanisms, check that every curriculum criterion is reachable from the starting policy, and read the run's raw term values, not the total reward, before calling it green.
Symptom
V3.0 trained the lineage's whole final recipe from scratch in one 9,000 iteration run - full beta curriculum, prone-conditioned pull assist, friction DR, the 3 s zero gate on standing income, flat_feet - testing the proposition "the product is defined by its configuration, not by its training history". Training looked all green (reward 26.33, episode length 500, 100% time-outs).
Context
Read in raw units against v2_6c at the same weights, the green was a seated equilibrium: base_height 0.377 vs 0.640, stand_pose 0.205 vs 0.516, flat_feet 0.0000 (zero because it sits outside its height gate, not because the feet were flat). Acceptance: 0.0% in Isaac at the deployed authority, 0.0% on every MuJoCo friction level, and still 0.0% at the training-time authority (100% seated at 0.222 m, upright and still).
Change
The full stdout (316k lines) was read: the beta curriculum's criterion (standing share over 0.35) was met zero times, so beta never left the wide setting and the policy had no experience at the deployed authority; the pull curriculum was stuck on the same criterion. The lineage had escaped the seated basin with immediate income (V2.0-V2.2) and only then added the zero gate to cure rushing (V2.5); from scratch, the zero gate removed the early "stand fast, earn more" gradient needed to escape. Verdict "the curriculum history is part of the product", limited to n = 1. v2_6c stayed the product.
Outcome
V3.1 kept the order as explicit phases: P1 from scratch with immediate income (zero gate off) until the curricula advance, P2 adding the zero gate. P1b/P1c escaped the seated basin and passed; P2 was later judged net negative and dropped (time-gate-vs-wide-stance-retire-the-fix).
Mechanism
Mechanisms that refine a competent policy (time gates, tight authority) can delete the gradient a naive policy needs, and a curriculum whose advancement criterion the naive policy never meets freezes at its first level.
Conflicts
The spec limits the falsification to "this recipe + this curriculum criterion" (n = 1, no seed sweep, no criterion tuning). V3.1's phased run succeeding is consistent with the ordering reading but changed other terms too.
Applies when
- consolidating a long lineage of continuation fixes into one clean recipe
- a from-scratch run with all mechanisms enabled plateaus early
- curriculum state is not logged or never advances
“命题:产物由配置定义,而非训练史定义。 … 训练 log(完整 stdout 316k 行)`[beta_anchor]` 仅初始 1 行,**达标 0 次** … V2.0~V2.2 靠**即时计酬**爬出坐姿盆地(§33),站立巩固后 V2.5 才装归零门 治"过快"(§39)。**课程史是产品的一部分。**”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §42 V3.0 判决(2026-08-11):从零单 run 全机制 FAIL 于坐姿盆地 A single run's drift direction may be a limit cycle, not a policy bias - check the sign distribution across seeds
multiseed-sign-test-for-driftDistinguish "bias" from "broken symmetry limit cycle" by the sign distribution over many seeds; report drift as (mean, sign split), and never compare single-run drift magnitudes across versions.
Symptom
Net yaw over 15 s appeared to worsen from -41 deg (v2) to -84 deg (v4), inviting the conclusion that the new version drifted more.
Context
The Isaac-side view across 32 environments told a different story: per-env yaw was mixed-sign (20 negative / 12 positive) with mean ~0 - the drift is a limit cycle whose direction depends on initial conditions, not a systematic policy bias. The single MuJoCo run had sampled one draw from that distribution, so its magnitude could not be compared across versions as if it were a property.
Change
Evaluation rule: before classifying drift as systematic, run multiple seeds and examine the sign distribution; single-trajectory drift magnitudes are samples, not properties.
Outcome
The v2-vs-v4 drift "regression" was reclassified as not-established; later drift work (hip_roll l+r bias) used cross-policy, cross-seed evidence instead.
Mechanism
Symmetric dynamical systems can settle into either of two mirrored limit cycles; the selected cycle is decided by noise and initial state. A statistic whose sign is initial-condition-dependent has no meaning as a single sample - only its distribution does.
Applies when
- comparing heading drift or lateral drift across policy versions
- a symmetric-looking behavior shows a consistent direction in one run
- deciding whether to fix "drift" in reward or calibration
“偏航反而变差(−41° → −84°):注意 Isaac 侧 32 env 的逐 env 偏航是正负混合(20/12)、均值 ≈0,说明这是极限环性质(方向随初值)而非策略偏置 —— MuJoCo 单次跑测到的是分布里的一个样本,不能当作系统性偏差。要判断需多种子统计。”
train/WALK_DIAGNOSIS.md § walk_v4 独立验收 读法 (偏航) Every acceptance run records video of the very rollout that produced the numbers - the seated basin, edge-standing feet, tangled legs and the narrow stance were all seen on video before, or instead of, a metric catching them
video-as-acceptance-recordMake video a default output of every acceptance run, rendered from the same rollout the metrics come from (fixed views including the feet), and watch it - posture failures are visible before any gate row exists for them; never let video replace or override the numeric gate.
Symptom
Posture failures in the recovery line kept arriving as things the numbers had no row for: a kneeling W-sit, feet standing on their outer edges, crossed legs, a stance too narrow to hold on hardware.
Context
From 2026-08-09 (user decision) accept_recovery renders offscreen by default, following one env for the whole episode and archiving the clip. The MuJoCo gate's --video renders three views (side, front, feet) of the same rollout the metrics come from, with all plant modelling (delay, push); the older replay-based renderer produced an independent trajectory without delay and was not used for acceptance. Video never gates: if rendering breaks, --no-video keeps the numeric gate running.
Change
Video as a default acceptance artifact, named per policy, category and view, reviewed by the user.
Outcome
The R0.1 prone clip showed the same kneel-sit as R0; the R3.1 failure clip showed the crossed legs; the v2_5 feet view showed edge standing and led to V2.6; the v2_6 videos led the user to order a real-robot A/B between v2_5b and v2_6. Once, the recorder did not start (P1c final acceptance) and the visual material had to be produced separately.
Mechanism
Metrics exist only for failure modes someone anticipated; video shows the unanticipated ones, and rendering the metric rollout itself guarantees the picture and the numbers describe the same episode.
Applies when
- setting up an acceptance pipeline for posture-sensitive skills
- numbers pass but a human reviewer is uneasy
- sim videos are rendered by a separate replay tool
“**验收存视频(用户定 2026-08-09)**:`accept_recovery.py` 默认开 Isaac 离屏 渲染,跟拍一个 env 的整局并归档 … 跪坐这类盆地在数字表出现前肉眼先看见,视频是验收的 定性存档,数字门不受它影响”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §5 R0 验收门(预注册)验收存视频(用户定 2026-08-09) Raising a command bucket's share does not strengthen its per-state gradient - it only starves the other modes
bucket-share-is-not-a-gradient-leverWhen a skill is not learning, first prove its per-state signal is nonzero (ignore-floor and probe checks); only rebalance sampling shares to fix genuine sample starvation, and account the regression risk to the diluted modes before doing it.
Symptom
Sidewalk was not learning, and the reflex proposal was to give the side bucket a larger share of sampled commands.
Context
The C4-redo3 rung explicitly kept the 20/40/20/20 bucket (stand/forward/turn/side) with the reasoning written out: PPO computes advantages per state, so bucket proportion does not change the per-state gradient of side states; at 4096 envs x 20% x 24 steps the rollout already contained ~19.7k sidewalk states - sample count was not the bottleneck. And the cost side was already measured: cutting forward from 60% to 40% had made vx+0.30 die at +400 in an earlier run - more cuts would only collapse it sooner.
Change
Bucket proportions held constant across the entire C4 redo series; the actual bottlenecks (metric frame bug, reward variance penalty, exploration form) were pursued instead.
Outcome
Sidewalk was eventually fixed with zero bucket changes (feed-forward delivery, +100 iters); forward/turn skills never suffered starvation-induced regressions during the redo series.
Mechanism
Policy-gradient credit is assigned per visited state; oversampling a mode multiplies its states in the batch but not the informativeness of each, so if the per-state gradient is ~0 (behavior unreachable or reward indifferent), N times zero is still zero - while the displaced modes genuinely lose data and regress.
Applies when
- proposing to oversample a failing task/command mode
- a majority mode regresses after share rebalancing
- budgeting env count vs mode share for a multi-skill policy
“比例不动：PPO 逐状态算优势，桶占比不改变单状态梯度；4096 env × 20% × 24 = 每 rollout 已有 1.97 万个侧走状态，样本数不是瓶颈；而 forward 60%→40% 已实测让 f30 在 +400 处死掉，再加码只会更早塌。”
train/C_LADDER_RUN.md § 3i. 桶 20/40/20/20 不动（比例不动） Sort external training advice into adopt / already-have / modify / would-trap by recomputing it on your own config
external-advice-audit-against-own-arithmeticNever apply external tuning advice directly: recompute each claim on your own reward table and probe data, classify it adopt / have / modify / trap, and record why - and verify external citations actually exist.
Symptom
External AI/literature advice for the omni ladder arrived plausible-sounding but was written without knowledge of this robot's actual reward table, contract, and history; following it blindly would have broken single-variable discipline and, in one case, made sidewalk unlearnable.
Context
Before the C ladder, every external suggestion was audited: 3 adopted (ellipsoid command sampling; staged wz bands; command-switch acceptance), 3 already present (unified reward; frame history - the frozen 168-dim 5-frame window; per-100-iter acceptance), 2 modified (stand share kept at 20% to avoid a second variable; back share NOT raised because the probe showed backward works untrained 20/20@67%, so oversampling would only crowd out forward), and 1 flagged as a trap: "start vy very small (0.06-0.15)" - on THIS reward table vy was only an L2 tax, so ignoring a vy=0.06 command costs 0.4% of the vx tracking scale, 28-180x cheaper than ignoring forward, with quadratic shrinkage making small commands weaker still. A separate retrieval-reliability note: two search agents returned fabricated verbatim quotes from arXiv PDFs (2 papers, verified fake and discarded); only HTML/abstract/source-verifiable material was used.
Change
Advice classified only after recomputing each claim with local numbers; the "start small" trap was replaced by adding a gated lateral tracking term (the ladder's only true reward surgery) instead of shrinking the command.
Outcome
The adopted items (ellipsoid modes, staged wz, transition acceptance) entered the ladder; the trap was avoided; one external factual error (calling s1g the mainline start - it was falsified 0/20) was caught. Later, one initially-dismissed item (sigma=0.15 too narrow) turned out right for a different reason than claimed - see cycle-average-tracking-for-gait-quantities.
Mechanism
External advice encodes the advisor's reward table and robot, not yours; the transfer-validity test is whether the claim survives recomputation under your own arithmetic (reward margins, probe baselines, contract freeze). Items that survive become experiments; items that don't become documented traps.
Applies when
- incorporating LLM or literature advice into a training plan
- advice conflicts with locally measured baselines
- an external claim depends on reward-table details the advisor cannot know
“其建议 C4「先很小，vy = ±0.06~0.15」—— 在我们这张奖励表下会让侧走学不起来 … 忽略侧走比忽略前进便宜 28~180 倍，且指令越小激励越弱（平方缩放）—— "先很小"在稳定性上对、在梯度上正好把信号缩没了。”
train/C_LADDER_RUN.md § 1. 外部 AI 训练建议的评估（采纳 / 已有 / 要改 / 会踩坑） Never referee a suspect metric with another metric from the same code - they can share the disease
independent-referee-for-metric-disputesTo adjudicate a disputed measurement, compute the quantity by an independent method from raw state; never accept a sibling column from the same pipeline as the tiebreaker.
Symptom
A triple reversal on one question: sidewalk sign diagnosis (correct) was retracted using a second metric from the same script, then the retraction itself had to be retracted when that second metric turned out to be the buggy one - two opposite-direction errors on the same problem in one day, both written into the execution sheet.
Context
The probe's net-displacement metric suggested the sidewalk reference sign was inverted. Worried about yaw-drift pollution of net displacement, the author checked the same table's body-frame vy_mean column (~0.003 everywhere, 20-50x smaller) and retracted the sign diagnosis. But vy_mean came from mj_objectVelocity, which was silently reporting vertical velocity due to a frame bug - the "referee" was the diseased measurement. Re-measured with a truly independent computation (xmat.T @ qvel, world trajectory), the original diagnosis was confirmed: saw -0.5 gave vy +0.058/-0.130 (76%/106%), consistent with the net-displacement values all along (yaw pollution was real but only 10-21 deg, nowhere near reversal-sized).
Change
Lesson written twice, verbatim, as a hard rule: when questioning a measurement, the referee must be an independent algorithm (different code path, different physical derivation), e.g. rotate qvel by the body matrix directly, or inspect the raw world trajectory.
Outcome
With the independent referee in place the frame bug was confirmed, fixed, and the whole C4 line re-scored - revealing sidewalk had been working (see body-frame-velocity-api-audit).
Mechanism
Metrics sharing a code path (or an upstream API) share failure modes; agreement between them is evidence about the code, not the world. Only a measurement with an independent derivation can break the tie, because its errors are uncorrelated with the suspect's.
Applies when
- two metrics of the same quantity disagree
- about to retract a conclusion based on a second readout
- auditing evaluation code after a surprising result
“我用一个坏指标去质疑一个好指标，并把撤回写进了执行单。教训（写死）：质疑一个测量时，不能用同一份代码里的另一个测量当裁判 —— 它们可能同源同病。裁判必须是独立算法（这次的裁判应该一开始就是 xmat.T · qvel[:3]，或直接看世界轨迹）。”
train/C_LADDER_RUN.md § 3m. 二 我今天犯了两个方向相反的错 / 3n. 五 元教训 IMU observation age cut 52-68 ms to ~4 ms by moving AHRS onto the MCU - as a single variable
imu-age-move-fusion-downstreamAudit observation age end-to-end and move time-critical fusion as close to the sensor as possible - and when you fix a latency, change only that one variable so the gain is attributable.
Symptom
IMU-derived observations reaching the policy were 52-68 ms old because attitude fusion ran in Python on the loaded host computer - stale attitude is a direct feedback-loop delay the policy was not trained with.
Context
The fix was scoped deliberately narrowly: move the AHRS computation from Python to the STM32 H7 (MC02). CAN topology explicitly unchanged, so the change is a clean single variable.
Change
AHRS fusion relocated Python -> H7. Before/after - IMU age: 52-68 ms -> ~4 ms; CAN timing: unchanged; Python load: high -> ~0.
Outcome
IMU age reduced by an order of magnitude with no confound; host CPU headroom recovered ("把计算单元搬在stm32上, 这样imu有剩余").
Mechanism
Sensor age is pipeline latency, not sensor quality: fusing on the MCU next to the sensor removes host scheduling jitter and interpreter overhead from the critical path. Keeping the bus topology fixed makes the improvement attributable to the relocation alone.
Applies when
- measured sensor-to-policy age far exceeds sensor sample period
- attitude fusion or filtering runs on a loaded host CPU in an interpreted runtime
- planning infrastructure changes during a sim2real campaign
“AHRS 搬到 H7——这个不改 CAN 拓扑，只是把一段计算从 Python 挪到 MC02，单变量：IMU age 52–68 ms → ~4 ms / CAN 时序 不变 / Python 负载 高 → ≈0”
Experience.md § AHRS 搬到 H7 (lines 28-35) A stand gate judged by survival passes a robot that wanders a meter - judge posture instead
stand-gate-posture-not-survivalFor every gate, ask what behavior the metric is a proxy for and validate its ordering against real observations; replace metrics whose ordering disagrees with reality, and demote them explicitly rather than silently.
Symptom
Real robot "standing" drifted 0.5-1.4 m across the floor while the sim stand gate scored a clean 20/20 - because the gate's metric was episode survival, which wandering does not violate.
Context
C2's stand condition was originally written as "survival regression <=2/20". A real-robot counter-example on 2026-08-08 forced the re-judgment: wandering robots survive. Cross-checking candidate sim metrics against real-robot feel showed max-tilt median ordering agreed with hands-on ranking, while displacement ordering was actually OPPOSITE to real impressions - so displacement was demoted to a reference quantity, not a gate.
Change
Stand PASS criterion rewritten from survival to posture: "stand tilt max median <= root baseline +1.5 deg"; displacement kept only as reference. Applied to all subsequent rungs (C4 and redo levels inherit it).
Outcome
Later rungs gated stand on tilt (e.g. C4 product: 7.7 deg vs parent 7.1 deg, +0.6 deg PASS); the wandering failure mode became visible to the battery instead of hidden by survival.
Mechanism
A gate metric is a proxy for an intended behavior; survival is a proxy for "did not fall", not "stood still". Metric choice must be validated against ground truth (real-robot feel/measurement), and a proxy whose ordering disagrees with reality on real data is worse than no metric - it steers selection backwards.
Applies when
- writing PASS conditions for stand/idle/hold behaviors
- a gate passes policies that visibly misbehave on hardware
- choosing between candidate metrics for an acceptance battery
“stand 必须用位姿判，不能用存活判（2026-08-08 真机反证改判）：站着乱走 0.5~1.4 m 时存活照样 20/20 —— C2 的 stand 条件原写「存活退化 ≤2/20」，选错了指标。改为: stand 倾角 max 中位 ≤ 根基线 +1.5°（倾角与真机手感排序一致；位移排序与真机相反，降为参考量）”
train/C_LADDER_RUN.md § 3b. PASS 条件 ⚠️ stand 必须用位姿判 The fallen-state reset was designed, not sampled from SO(3) - fixed category shares with jitter, a low drop that settles physically, equal left/right shares for mirror augmentation, and a numeric check before training
fallen-pose-reset-distributionBuild a fallen-start distribution from named, physically plausible categories with jitter and a settle phase, keep mirrored categories at equal probability, and check the realized shares and penetration numerically before spending a training run on it.
Symptom
A get-up policy can only learn from the fallen states its resets produce; uniformly random orientations produce ground-penetrating and limit-jammed states the robot can never be in.
Context
R0 reset_root_fallen: supine 30%, prone 30%, side_l 15%, side_r 15%, mid (random axis 50-125 deg) 10%, +/-15 deg jitter, full yaw, dropped from 0.28-0.40 m and left to settle under physics, joints uniform inside the soft limits with a 5% margin plus small random velocities. Random SO(3) was rejected (the advisor agreed). side_l and side_r must have equal probability because mirror augmentation turns a left fall into a right fall. The advisor had proposed supine and prone only for R0; the spec included side and mid because the feasibility accounts showed physical solutions for all of them, and wrote "narrow back to supine+prone" down as the first fallback. With no display on the training box the reset was checked numerically instead of by eye.
Change
Category mix as above; realized shares, settle height and penetration measured over 512 envs before the first run. A fallen-state bank (real falls, settled and stored) was pre-registered for R2.
Outcome
Realized shares 29.3/31.6/16.4/17.8% against the config, settle +0.262 m, final penetration 0/512 (a 0.10 m peak at the write instant, ankle links only, pushed out within 80 ms because the 0.28 m drop floor is shorter than a fully extended leg). R0's failure was a reward basin, not a reset artifact. The fallen-state bank stayed unbuilt through V3.1 (checklist item open); R0.3 later re-sliced the prone share into roll_l/roll_r bands, which is what forced the acceptance distribution to be frozen separately.
Mechanism
A category-structured, physically settled start distribution keeps training on states the robot can actually occupy, and equal mirrored shares keep mirror augmentation a pure doubling of data rather than a bias.
Applies when
- designing reset distributions for get-up, recovery or multi-contact skills
- mirror/symmetry augmentation is on and the task has chiral start states
- no viewport is available to inspect resets on the training machine
“角度 jitter ±15°、yaw 全域、0.28~0.40 m 低空放下由物理沉降,关节软限位内 均匀(留 5% 余量)+ 小随机速度。**不用 random SO(3)**(会采出穿地/极限卡死 等现实不可能状态,顾问同判) … side_l/side_r **概率必须相等**(镜像增强的样本同分布前提)”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §3 R0 任务定义 / §9 核查单 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) Changing the gait clock silently flipped a hardwired threshold's meaning - write derived constants as expressions
derived-constants-must-track-their-baseBefore changing any base parameter (clock, control rate, scale), enumerate every constant derived from it and every constant that must NOT change; convert derived literals into expressions of the base so the next change cannot silently flip a term's meaning.
Symptom
Slowing the clock 0.40 -> 0.50 s would have silently inverted the feet_air_time threshold's semantics: the 0.25 s threshold was hardwired, so at ct 0.40 the swing window (~0.20 s) sat below it (constant pressure to lengthen strides), while at ct 0.50 the window (~0.25 s) equals it - the term's meaning flips from "push longer" to "neutral" with no code error anywhere.
Context
The clock change audit walked every dependent quantity: most followed automatically (joint_pos_ref / clearance / contact_number cycle_time params, gait_phase observation, deploy/sim2sim/policy_io, export) - wiring confirmed, zero hand edits; the air_time threshold was the one hardwired constant, fixed by preserving the RATIO: 0.25 -> 0.3125 = 0.625 x ct, with the recommendation to commit it as the expression 0.625*ct "一劳永逸" (solved once and forever). The same audit also listed what must NOT follow the clock (50 Hz control rate, physics dt/decimation, 47-dim contract, action_latency absolute seconds, PD/torque limits) - the change's blast radius stated in both directions.
Change
feet_air_time threshold re-expressed as a fraction of cycle_time; auto-following vs must-not-change lists written into the spec for the clock migration.
Outcome
The clock migration (v10, repeated in v11) carried no silent semantic flips; the expression form removed the trap for every future clock change.
Mechanism
Constants derived from a base parameter encode a ratio at their birth; storing the evaluated number severs the dependency, so changing the base leaves stale semantics with no failing test. Expressions preserve the intent; and an explicit both-directions dependency list (follows / must-not-follow) is what makes a base-parameter change reviewable.
Applies when
- changing gait clock, control frequency, or units
- a reward threshold interacts with a phase/window duration
- config audit finds literals that encode ratios
“feet_air_time 阈值 0.25 是写死的,不跟 ct 走——0.40 时摆动窗 ~0.20s<0.25(恒拉长压力),0.50 时摆动窗 ~0.25s≈阈值(语义翻转)。按比例保原压力:0.25 → 0.3125(=0.625×ct;建议直接写成 0.625 * ct 表达式,一劳永逸)。”
train/WALK_V10_SPEC.md § 3. T —— 慢时钟 (训练侧必做一件) When the training reset distribution changes, freeze the acceptance distribution separately and pin its seed - the same checkpoint measured twice differed by 3.8 and 10.2 points
frozen-acceptance-distribution-and-pinned-seedAcceptance distributions are frozen artifacts, decoupled from whatever the training distribution becomes and evaluated with a pinned seed, and every gate row carries its sample size so that a small-n row is never read as a regression.
Symptom
R0.3 added easier roll-arc start states to the training resets, and the acceptance script shared the training category table; separately, the same checkpoint scored side 3.8 points and mid 10.2 points differently on two runs of the same script.
Context
Acceptance ran 512 parallel envs drawn from the fall categories. Had it kept following the training table, 15% of acceptance samples would have landed on states easier than prone - inflated scores and generations that could not be compared. The two same-checkpoint runs had identical per-item height medians, so the ruler had not changed; the spread was reset resampling noise (side n ~ 169, sigma 2.3%; mid n ~ 35, sigma 8.3%). The spec's "20 seeds" had always meant controlled seeds.
Change
accept_recovery.ACCEPT_CATEGORIES pinned to the four R0-R0.2 categories and decoupled from the training FALL_CATEGORIES; --seed 20260809 pinned, after which two consecutive runs were bit-identical. The mid row (n ~ 35) was labelled the bluntest gate.
Outcome
Every later generation (R0.3 through V3.1) was scored on the frozen distribution and seed, which is what let R0.3's intermediate state be read as "no measurable gain" (62.7 -> 62.1%) and R0.2's mid drop be booked as noise rather than a regression.
Mechanism
An acceptance set that follows the training distribution measures a moving target, and an unpinned reset draw adds sampling noise that small-n rows cannot absorb.
Applies when
- the training reset or command distribution changes between generations
- repeated evaluations of one checkpoint disagree
- a small category drives a pass/fail decision
“**分布冻结**:`accept_recovery.ACCEPT_CATEGORIES` 钉死 §5 四类 … 与训练侧 `FALL_CATEGORIES` **解耦**。 … **side 差 3.8 点、mid 差 10.2 点**(h 中位逐项一致,证明不是尺子变了)—— 纯 reset 重采样噪声 … 钉死后两次连跑逐位相同。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §14 验收尺子的两处加固(R0.3 起生效,向后兼容) Three times a joint-angle stand-in for a foot-level quantity was gamed or lied - the absolute ankle roll sold stance width to buy flat feet, 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
joint-space-proxy-for-task-space-quantityExpress foot-level (task-space) goals and acceptance criteria in task space - link attitude and lateral spacing from world poses - never through joint angles that assume other joints are at zero, and measure the task-space value before trusting a joint-space estimate of it.
Symptom
The line's first real-robot get-up (v2_6, 2026-08-11) was "fairly stable", but after standing the feet were too close and the robot slid into the splits and fell several times; later reports added that it also got up through a split posture.
Context
(1) flat_feet penalized sum |q_ankle_roll|, but a flat foot is ankle roll compensating hip roll; the proxy taxed the compensated wide solution, and the untaxed combination was hips straight plus ankles at zero - flat and narrow. On hardware the lateral support shrank to hip width plus centimetres, lateral balance rested on 11 N*m ankle motors, and the feet slid apart. (2) The next rung's acceptance criterion "hip roll >= 20 deg" assumed zero hip yaw; at 50 deg of yaw the lateral contribution is x cos 50 ~ 0.64 - the same substitution again, inside a criterion. (3) A new task-space diagnostic reading the foot links' world poses measured v2_6c's stance at 0.159 m where the kinematic audit from joint angles had said 0.271 m (0.271 x cos 47 ~ 0.17).
Change
Rule written into the spec: task-space quantities are never expressed through joint-space proxies. V3.1's stance terms were all task-space: flat_feet_task from the foot links' world orientation, lateral foot spacing in metres, stand_pose stripped of both roll joints.
Outcome
From scratch with task-space terms (V3.1 P1c): lateral stance 0.355 m, foot residual tilt median 0 deg / P75 2.0 deg, all six criteria passing, mu 1.0-0.4 all 100%.
Mechanism
A joint proxy bundles the goal with everything else those joints do; the optimizer finds the combination the proxy does not tax, and a joint-based criterion silently assumes the other joints sit at their nominal.
Applies when
- rewarding flat feet, stance width, foot placement or end-effector pose
- an acceptance criterion is written in joint angles for a geometric goal
- joints with large yaw or coupled axes are involved
“**病根 = 关节空间代理**:`flat_feet` 罚 Σ|q_ankle_roll|(§41 取的简易口径)。 "脚掌平"的运动学正解是 **踝滚补偿髋滚**(q_ankle_roll ≈ −q_hip_roll) … 代理把"脚平"和"站距"绑死在一起卖了。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §43 真机首试入账(2026-08-11)判读:flat_feet 的代理口径错误 Write each config's expected hardware signature before the session - and if reality disagrees, change the books, not the conclusion
preregistered-real-expectationsBefore hardware runs, write per-config expected signatures and the disagreement rule (hardware outranks sim; discrepancies get recorded, not reconciled); validate the harness by checking it reproduces at least one known real behavior.
Symptom
Hardware impressions are easily narrated after the fact; without written expectations, any real-robot outcome can be made to "match" the sim story.
Context
The S2 acceptance sheet carried a section titled "sim 侧预注册预期 (事后核对, 不许事后改)" - per-configuration behavioral signatures written before the session: s1e@0.8 the disturbance king (push 159/160, zero chirality, all-mu 20/20) at the cost of speed gates 0/20 and zero-command wander ~0.98 m with -29.5 deg/20 s rotation; fric-3000 "walks accurately but is easier to push over"; fric-2400 neither. Credibility check included: the sim harness had reproduced the already-recorded real behavior (pace in place + right drift + net rotation -30 deg/20 s), which "提高本单全部预期的可信度". The anomaly clause fixed the epistemics in advance: if results systematically disagree with sim, "不改结论改账" - don't massage the conclusion, write the discrepancy into the books, and per the earlier zero-warning lesson, hardware wins.
Change
Every hardware session ships with a pre-registered expectation table (signature per config), a baseline-match credibility check, and a written precedence rule for disagreement.
Outcome
The A/B session became falsifiable: agreement confirms the proxy, disagreement is booked as a proxy-bias finding rather than argued away.
Mechanism
Pre-registration converts qualitative hardware sessions into tests of the sim-to-real mapping itself; a reproduced known behavior calibrates trust in the remaining predictions; and fixing "who wins on disagreement" beforehand prevents authority from drifting to whichever source flatters the plan.
Applies when
- planning any hardware acceptance or A/B session
- the sim harness's credibility in this regime is unestablished
- post-session write-ups tempt narrative fitting
“⚠️ sim 复现了真机已记录的「原地踏步 + 右漂 + 净旋 −30°/20s」—— harness 与真机行为对得上, 提高本单全部预期的可信度。… 结果与 sim 系统性不符 → 不改结论改账: 写进 README 该节, 按 「Isaac 指标三次零预警」的教训, 以真机为准。”
train/REAL_RUN_S2.md § 2. sim 侧预注册预期 (事后核对, 不许事后改) / 4. 异常处置 With no term on foot attitude the robot stood on the edges of its feet (ankle roll -26 deg) from R0.5 to V2.5b; pricing it fixed that and turned stance width into the next free variable - the user saw both on video before any metric flagged them
unpriced-foot-attitude-is-a-free-variableList every posture quantity the hardware cares about (foot attitude, stance width, yaw, knee flexion) and make sure each is priced by a term with real gradient at the observed error; pricing one exposes the next, so re-inspect the feet-level video after every change.
Symptom
Watching the v2_5 video the user said the ankle roll after standing looked very strange - the feet were not flat. The feet view showed the right foot standing on its outer edge; median ankle roll at t = 8 s was -/+26 deg (limit +/-35), mirrored. This was the physical form of the ankle_roll saturation criterion that had failed since R0.5.
Context
No term priced foot attitude: feet_contact_upright counts an edge contact as contact, and stand_pose's wide exp kernel (sigma^2 = 9) gives almost no gradient at 0.45 rad. HoST carries an "ankle parallel" term (+20); this table never had one. Edge standing was made a blocking precondition for hardware (continuous ankle load, unstable contact, wear).
Change
V2.6 (single variable): flat_feet = (|q_l_ankle_roll| + |q_r_ankle_roll|) x upright gate x height gate, a linear hinge with a 5 deg margin, weight -2, continued from V2.5b.
Outcome
Ankle roll -/+26 -> 5.3/5.2 deg, ankle_roll saturation 6.6% (< 10%), feet flat on the feet-view video; success 99.6%, re-falls 0-1%. Then the user watched v2_6: hip yaw constantly tense and the legs very close together. The numbers: hip roll -/+4.9/4.8 deg against a nominal 25 - feet flat and hips open 25 deg cannot coexist without ankle compensation, the new term taxed that compensation, and nothing priced stance width. "Foot attitude as a free variable" was fixed and "stance width became the new free variable" - which the real robot then exposed as splits.
Mechanism
An optimizer spends every posture degree of freedom no term prices; closing one reallocates the slack to the next unpriced one.
Applies when
- a standing or landing posture looks wrong on video while gates pass
- a saturation criterion keeps failing on one joint
- a new posture term was just added
“用户看 v2_5 视频:"起身之后 ankle_roll 非常奇怪,脚根本不是平着站立"。 … 机理:奖励表**无任何脚掌姿态项** —— feet_contact_upright 边缘接触也算触地, stand_pose 的 exp 核(σ²=9)对 26°=0.45 rad 梯度≈0。脚掌姿态是自由变量。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §41 预注册 V2.6:flat_feet —— ⑥ 老账的物理形态被用户目视锁定 After installing the measured plant, re-run all generations paired on old and new plant - identity metrics carry verdicts, physics metrics re-baseline
plant-swap-invariants-vs-shiftsTreat every plant upgrade as an era boundary: re-run the retained policy set paired (same seeds/flags) on both plants, carry forward only verdicts whose metrics proved plant-invariant, re-baseline the rest - and mine the systematic shifts as measurements of the old plant's biases.
Symptom
With the plant finally fully measured (weighed masses 9.792 kg, bench-identified armature, in-situ friction), no historical sim number was comparable to new runs - "历史 sim 数字跨纪元不可比" - and it was unknown which historical verdicts still held.
Context
The era-2c cross-test ran all seven walk generations on the complete plant under one harness (14/14 survived), then re-ran the same 14 configurations on the OLD plant retrieved from git, same flags and seeds, with a self-check (one historical record reproduced digit-for-digit). The split was clean. Policy-identity metrics moved essentially zero across the plant swap - dominant frequency (v7's period-doubling 1.20 -> 1.20), knee amplitude (9.9 -> 10.0), foot distance (+/-2 mm), saturation (100 -> 100, 0 -> 0) - so all seven cross-generation verdicts (freeze signature, saturation-line closure, knee-collapse location, slip-penalty accounting, N2 lineage, v6's balance, v11's triad) were re-confirmed on the honest plant. Plant-physics metrics shifted systematically with ordering preserved: slip down 10-25% (measured friction makes ground-twisting costlier), landing vertical velocity down 15-50% ("旧 plant 高估落地 凶度"第二次独立证实), landing force mixed (mass up 2.2% vs friction braking the swing - two effects fighting). Exactly ONE behavior-level change: v8's low-speed period-doubling vanished (1.30 -> 2.50, lift normalizing) - confirming it had been machine-dependent bifurcation-edge behavior that armature+friction push off the knife edge, while v7's period-doubling stood untouched: saturation-freeze-driven, a policy property, not a numerical accident.
Change
Era re-baselining protocol: after any plant upgrade, one paired same-seed sweep of all retained generations on old and new plant; verdicts keyed to identity metrics carry over, thresholds re-read against the new-plant table, and the differences themselves become plant-physics findings.
Outcome
Seven verdicts survived with evidence rather than assumption; two causes of the period-doubling family were separated with plant-side proof; and the sim's landing-violence overestimate was independently confirmed a second time.
Mechanism
A policy's structural properties (frequencies, amplitudes, frozen joints) are functions of its weights and survive plant changes; contact-mediated quantities are joint properties of policy and plant and shift when the plant becomes honest. Pairing seeds across plants isolates the plant's contribution exactly, so the sweep both validates history and measures what the old plant had been lying about.
Applies when
- installing measured masses/armature/friction into the sim
- historical thresholds are cited across a plant change
- a hardware-only behavior might be bifurcation-edge sensitivity
“策略身份指标逐位不动:主频(v7 1.20→1.20)、膝摆 … 这些是策略属性,plant 换代携带无损,历史定论因此全部成立。… 唯一行为级变化:v8@0.15 的倍周期消失(主频 1.30→2.50…)——印证当时"分岔边缘、机器相关"的判定:armature+摩擦把 v8 推离刀锋;v7 的倍周期纹丝不动(1.20→1.20),它是饱和冻结驱动的深层属性,不是数值巧合。”
train/README.md § 纪元 2c 全代同机横测 (2026-08-04): 完全体 plant 上历史结论全部存活 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 The restart reward table lists a reason for every term AND a lesson for every exclusion - absent terms are removed, not zero-weighted
minimal-reward-table-with-provenanceMaintain the reward table as an evidence ledger: every term cites the episode that justifies it, every excluded term cites the episode that convicted it (including the development stage it is valid at), and retired terms are deleted from the config, never left at weight zero.
Symptom
Seven walk generations had accumulated an entangled reward table where nobody could say which term earned its place; the restart needed a table that could be audited line by line.
Context
The minimal table v2 was built under three written principles: "一项管 一件事、结构性反抬脚的项一个不留、塑形只留一套相位逻辑;不在表里的一律 不加" (one term per job; zero structurally-anti-lift terms; exactly one phase-shaping logic; nothing outside the table gets added). Every row carries its provenance (e.g. base_height target = standing height cites the v4 crouch lesson; split x/y tracking cites the merged-exp gradient hole; world-frame yaw cites the v1/v3 body-frame lesson). Every EXCLUSION carries its same-type precedent: feet_landing_vel out because it is poison while the gait is unformed (drag pays 0, lifting pays - the v4-clearance / v8a-B "reverse threshold" family) though it was fine in v7 when the gait already existed - term validity depends on developmental stage; feet_air_time out with its measured non-lever evidence (v6 had it at 2.0 and still lifted 4 mm); and replaced terms are REMOVED from the config ("置 None,不是权重 0 挂着") so audits see truth, not dormant weight.
Change
Reward table rebuilt as ~20 rows each with weight + provenance column; exclusion list maintained alongside with the falsifying episode for each; dormant terms deleted rather than zeroed.
Outcome
Later revisions (S1.2/S1.3) modified the table by citing and updating specific rows' evidence rather than re-arguing the whole design; the table doubled as the lineage's reward-lesson index.
Mechanism
A reward table is a set of standing hypotheses; attaching each row's evidence makes revisions targeted and reversible, and recording why a term is absent prevents the cycle of re-adding known poisons. Deleting vs zero-weighting matters because config audits and DR interactions see the term either way - a zero-weight term is dormant complexity waiting to be flipped on wrongly.
Applies when
- designing a reward table for a restart or new task
- someone proposes re-adding a previously removed term
- auditing which reward rows still earn their place
“原则:一项管一件事、结构性反抬脚的项一个不留、塑形只留一套相位逻辑;不在表里的一律不加 … feet_landing_vel(评审 #4):拖地时代价恒 0、抬脚才收费——与 v4-clearance/v8a-B 同属「反抬脚门槛」家族,步态未成形时是毒;v7④ 加它时步态已存在。… 已从 cfg 移除(置 None),不是权重 0 挂着。”
train/OMNI_V0_SPEC.md § 3. 最小奖励表 v2 / 明确不带 Late training leaned on sampling noise as a stability crutch - deterministic play collapsed while training metrics stayed green
noise-crutch-deterministic-collapseEvaluate the deterministic policy in an external harness on a fixed cadence during training (not just at the end), select checkpoints on that curve, and treat a collapsing noise_std with rising training reward as a warning that noise is load-bearing.
Symptom
omni_s1's final checkpoint (model_5999) fell at 4 s even in Isaac's OWN deterministic play, while checkpoints from iter 1700-4000 were fine - and no training metric flagged anything. Policy noise_std had collapsed to 0.045 by iter ~990 (final 0.033).
Context
Diagnosis: the policy had learned to use its exploration noise as a dither/stabilizer - "策略把采样噪声当稳定拐杆,训练指标看不见" (the training metrics cannot see it, because training always runs with noise on). Countermeasures: entropy_coef 0.005 -> 0.01 to slow the std collapse, and - the structural fix - an in-training smoke loop (watch_ckpt.py): every 500 iters, export ONNX directly, run 3-seed MuJoCo evaluation, log CSV/TensorBoard curves plus three-view videos. The doctrine line was written in bold: "训练指标全绿不再是发育健康的 证据,冒烟曲线才是" - green training metrics are no longer evidence of healthy development; the smoke curve is. The follow-up run s1b showed the drift metric follow a U-shape (73 -> 8.6 at iter 3500 -> 76), making checkpoint selection BY the smoke curve (early stop at 3500) the shipping mechanism, with terminal re-degradation booked as known and unresolved.
Change
entropy floor raised; watch_ckpt smoke loop instituted as standing infrastructure; checkpoint selection moved from "last iteration" to "best point on the deterministic smoke curve".
Outcome
s1b shipped from iter 3500 (the U-bottom) instead of a degraded terminus; every later lineage (s1c/s1e, the C ladder's --every 100 loops) inherited the watcher as the standard guardrail.
Mechanism
PPO evaluates and improves the stochastic policy; if noise itself stabilizes the gait (dither smoothing a marginal limit cycle), the deterministic mean policy is a different, worse controller that training never measures. External deterministic evaluation on an independent simulator is the only readout of what will actually be deployed.
Applies when
- final checkpoints underperform mid-training ones
- noise_std collapses early while training reward climbs
- deciding which checkpoint to export and ship
“训练后期确定性脆化——noise_std iter~990 收到 0.045(终 0.033),model_5999 连 Isaac 确定性 play 都 4 s 摔(1700~4000 正常):策略把采样噪声当稳定拐杖,训练指标看不见。对策:entropy_coef 0.005→0.01 + train/watch_ckpt.py 训练中冒烟曲线 … 训练指标全绿不再是发育健康的证据,冒烟曲线才是。”
train/OMNI_V0_SPEC.md § 3. S1.1 修订记录 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) Three same-shaped judging errors - task metrics (survival, tracking, displacement) cannot stand in for posture metrics
task-metrics-vs-posture-metricsKeep validated posture-class rows (tilt, per-joint L/R asymmetry, temperature) in every acceptance battery alongside task rows; when operator feel contradicts the gates, suspect the metric class before the operator - and never build a new skill on what is actually an asymmetry defect.
Symptom
The C2 product judged "full pass" on task metrics (A800: turn-gap 7 pp, vx+0.30 19/20) felt WORSE in the operator's hands than the half-pass 700: A800 tilted up to 12.90 deg (700: 6.64), drifted left while standing, showed larger per-joint asymmetries, and ran its hip_rolls 5 degC hotter.
Context
The re-judgment catalogued three same-type metric errors in one campaign: (1) stand judged by SURVIVAL - missed 0.5-1.4 m wandering; (2) stand ranked by DISPLACEMENT - ordering was opposite to real feel (tilt ordering matched); (3) chirality judged by wz-tracking GAP - measured turning symmetry while the robot's actual disease was postural left/right asymmetry, "两个不同的东西,且结论相反". Common pattern named: "我一直用「任务指标」当判据,而真机手感对应的是「姿态 指标」… 任务类指标不能替代它". The fix was already in the data: the per-joint left/right asymmetry table (printed identically by sim2sim and deploy) agreed with hardware in direction on every row - "判据可用、有预测力,我只是没把它写进 PASS 条件". Shipping decision followed the posture read: product reverted to 700 ("又一次「买到 精度、卖掉别的」"), and the C4 root moved to 700 as well, with the sharpest line of the episode: A800's left-drift "like sidewalking" is probably its frontal-plane asymmetry defect, not a capability - "在缺陷上建能力是危险的".
Change
Two posture quantities with demonstrated real-robot predictive power promoted into every PASS battery: tilt-max median and per-joint left/right asymmetry (both sim-computable, deploy-homologous); motor-temperature readout added to session close-out.
Outcome
Deployment flipped to the posture-better checkpoint; the hip_roll temperature table (43-48 degC vs 25-28) confirmed the earlier 90%-of-heat account; the run-line acceptance battery inherited the posture rows from birth ("任务类替代不了姿态类").
Mechanism
Task metrics measure goal attainment under the evaluator's episode definition; posture metrics measure the body state trajectory that operators, motors, and downstream skills actually experience. The two can rank candidates oppositely because task success tolerates postural pathology - so a battery without posture rows is blind to exactly what hardware feel reports first.
Applies when
- hardware feel disagrees with a green acceptance table
- choosing between checkpoints that split task vs posture metrics
- selecting the root for a skill that resembles an existing defect
“共同模式:我一直用「任务指标」(存活 / 跟踪率 / 位移)当判据,而真机手感对应的是「姿态指标」(倾角、逐关节左右不对称)。→ 验收判据里必须有姿态类指标,任务类指标不能替代它。… A800 的「左飘像 side walk」很可能 … 是它更大的额平面不对称的表现 —— 在缺陷上建能力是危险的。”
train/README.md § C2 选点改判 (2026-08-09): 手性判据第三次选错指标 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) When training fails repeatedly, inject the target behavior open-loop - stop tuning rewards for an unverified behavior
open-loop-probe-before-reward-tuningAfter two failed training attempts at a skill, stop training: demonstrate the behavior open-loop on the real plant/sim first, and only resume training once you hold a measured, safe, sign-verified target trajectory.
Symptom
Three sidewalk training rounds failed; hypotheses multiplied (exploration failure / wrong reference waveform / insufficient authority) with no way to pick between them by running more training.
Context
Instead of a fourth reward guess, the team wrote probe_side_ref.py: the candidate reference is injected open-loop on top of a frozen policy's output (bypassing PPO entirely), directly measuring "what happens if the robot literally does this waveform" - separating all three hypotheses in one experiment (5 seeds x 8 s per condition, several waveform families and gains). The probe immediately eliminated the authority hypothesis (full-amplitude execution, 5/5 survival) and localized the problem to the waveform/measurement side. The closing principle was written down after the saga: without a verified target behavior, tuning rewards is "在黑暗里试钥匙" (trying keys in the dark).
Change
Standing method: before opening another training rung on a failing skill, build an open-loop (or task-space) generator of the intended behavior, measure whether the physical system can express it and what it looks like - then train toward a verified, quantified target.
Outcome
The probe chain produced the verified waveform (reversed-sign triangle, half gain), quantified safe amplitude (tilt 8.2 deg at band top, foot distance clear of the wall), exposed the metric bug when probe and training disagreed, and ultimately supplied the feed-forward that made C4 pass in +100 iters.
Mechanism
Training couples exploration, reward design, and feasibility into one opaque outcome; open-loop injection cuts the loop and tests feasibility and waveform alone. A behavior demonstrated open-loop converts the remaining failure into a pure credit-assignment/reward question - and its measured trajectory becomes the reference itself.
Applies when
- repeated training failures on one skill with multiple live hypotheses
- uncertainty whether the platform can physically express the behavior
- a reference trajectory's shape/sign/amplitude is guessed, not measured
“三轮 FAIL 之后不再猜，写 train/probe_side_ref.py 把参考开环注入到策略输出之上（绕过 PPO），直接量「照这个波形做会怎样」，一次分开三个假说：甲 探索 / 乙 波形 / 丙 权限。”
train/C_LADDER_RUN.md § 3f. C4 真因定谳（开环探针） / 3k. 建议的下一步 Training-log reward values and fixed-command eval values live on different distributions - comparing them once claimed a 44% improvement that was really 6-10%
same-distribution-reward-comparisonQuote reward-term values only with their distribution attached (command range, DR on/off, environment), and compare across runs only when those match; re-measure in a common environment before claiming any improvement percentage.
Symptom
A v6-era analysis concluded slip had dropped 44% by comparing the training log's Episode_Reward against values calibrated in a fixed-command play environment; a same-condition re-measurement showed the true improvement was 6-10%.
Context
The training log's reward is an expectation over the training command distribution (vx 0.15-0.5, yaw +/-0.6, with pushes and domain randomization); play-environment calibrations are taken at a single fixed command with DR off. Subtracting one from the other compares apples to oranges - the warning was written into the v7 pre-flight: "奖励数值只能在同一指令分布下比较 … 拿它和固定 cmd 的 play 环境标定值相减会得出错误结论(v6 那轮已经栽过一次)".
Change
Rule adopted: any before/after reward-term comparison must hold the command distribution, DR state, and evaluation environment fixed; training-log values compare only against training-log values of runs with identical command/DR configs.
Outcome
The phantom 44% improvement was retracted; later term-level accounting (e.g. the C4 ignore-floor work) consistently specified its distribution before quoting numbers.
Mechanism
A reward term's expectation depends on the visited-state distribution as much as on the policy; changing the command distribution or DR moves every term's baseline. Cross-distribution differences therefore measure the distributions, not the policy change.
Applies when
- comparing reward telemetry across training runs or vs play evals
- claiming improvement percentages from training logs
- term-level reward accounting for diagnosis
“奖励数值只能在同一指令分布下比较。训练日志的 Episode_Reward 是在训练指令分布上算的(vx 0.15~0.5 / 偏航 ±0.6 / 带推力与域随机化), 拿它和固定 cmd 的 play 环境标定值相减会得出错误结论(v6 那轮已经栽过一次: 据此以为滑移降了 44%, 同条件对拍只有 6~10%)。”
train/WALK_V7_SPEC.md § 3. 开训自查 ⚠️ A get-up policy righted itself and sat - three terms paid the seated pose 84% of the return, and the only shaping term that could tell sitting from standing was an exp kernel outputting 5e-5
seated-basin-dead-exp-kernelWhen a policy parks in a degenerate posture, tabulate what each reward term pays that posture against the target (watch contact terms that reward touching rather than bearing load) and evaluate every exp kernel at the error actually observed; a kernel narrower than the real error is switched off, and widening it is a one-variable repair that adds nothing new.
Symptom
R0 converged by iteration 700 and gained 1.7% over the next 2,300; success 0.0% in all four fall categories. Three of four success conditions passed (tilt median 1.0 deg, both feet in contact 99.6%, angular rate low); height passed 0.6% (median 0.204 m against 0.326). The robot knelt in a W-sit: hip yaw +/-47 deg, knees folded to 92% of the hard limit, shins flat, pelvis on the ground, torso vertical.
Context
Minimal reward table: upright (1-g_z)/2 +2.0, base_height linear progress +1.5, stand_pose exp(-||q-q_stand||^2/std^2) x upright gate +1.0 with std 1.0, still +0.5 and feet_on_ground +0.5 both x the upright gate, plus regularizers. The upright gate is a hinge that opens below 30 deg of tilt. The pre-registered fallbacks were then checked against the measured state: tightening the tilt gate was falsified (tilt was already 1.0 deg); a success bonus contradicted the spec's own no-cliff-bounty rule; narrowing the categories was useless (all four converged to the same pose); raising init noise was too weak for a basin this deep. Only a half-rise intermediate state addressed it, and a cheaper repair existed.
Change
R0.1 (user decision, single variable): stand_pose std 1.0 -> 3.0. Not a new term and not a bounty - repairing a declared term that was numerically dead. The two runs' logged env.yaml differ in log_dir and std only.
Outcome
R0.1 58.2% overall (R0 0.0%): supine 91.5%, side 82.2%, mid 55.9%, prone 0/156; knees fully straight; ||q-q_stand||^2 9.99 -> 0.91 and the stand_pose term 4.6e-5 -> 0.90; get-up ~1 s, no re-falls, the curve still rising at the 3,000-iteration cap. Prone stayed at zero and needed a different fix (see prone-dead-end-is-foot-placement).
Mechanism
Sitting earned upright 1.98/2.0, still 0.43/0.5 and feet_on_ground 0.46/0.5 - 3.0 of a 3.57 per-second return - because feet_on_ground asked for contact, not load. The only terms separating sitting from standing were base_height (+0.70/s for standing) and stand_pose, whose kernel at the real 9.99 rad^2 error (75% of it in the two knees) was exp(-9.99) = 4.6e-5 with a gradient near 1e-4. Standing up meant risking 3.0/s to gain 0.70/s while unfolding knees at 92% of their limit under load. With std 3 the same term is exp(-9.99/9) = 0.33 - a live gradient, three quarters of it on the folded knees.
Applies when
- a policy converges early to an upright but low, seated or kneeling pose
- a posture-matching exp term reads ~0 in the training logs
- contact-based rewards saturate while the task metric does not move
“**关键:`feet_on_ground` 只问"触地"不问"承重", 跪坐时双脚确实贴地,照样满分。** 三项 3.0/s = 总回报 3.57/s 的 84%。 … **exp(−9.99) = 4.6e-5** —— 权重 1.0 的项实际输出 5e-5、梯度 ~1e-4, **不是"还没学会",是数值上根本不存在**。 … **R0.1 决定(用户 2026-08-09 定,单变量)**:`stand_pose` 的 `std` **1.0 → 3.0**。 不是加新奖励、不是悬崖悬赏,而是**修复一个已声明但数值失效的项**”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §11 R0 首跑(recovery_r0, 2026-08-09):FAIL —— 翻正了但坐着 The smoke watcher panicked at the wrong operating point and its score goes blind when gates saturate - treat it as a survival sentinel, not a judge
smoke-watcher-operating-pointConfigure every automated evaluator at the lineage's declared deployment operating point, give it graded metrics that cannot saturate, and until then scope its authority to catastrophe-detection - never let a mis-configured watcher stop or rank a rung on its own.
Symptom
Two watcher misfires in one ladder: (1) during the friction rung the watcher (evaluating at kd 1.0) reported panic-level 1/3 survival from iter 3300 - falsified by the official kd 1.2 scan, because the lineage's design operating point was kd 1.2 and the watcher lacked the --kd-scale passthrough; (2) during the PD rung the watcher's early-stop score froze at iter 1050 despite ongoing drift improvements, because with all eight gates passing (constant 0/3 failures) the score has no gradient left - "八门全过恒 0/3 时 score 对漂移改善盲, s1f 课文三现".
Context
Both are the same category: the in-training smoke loop is an instrument with its own configuration (operating point, score design), and its verdicts are only as aligned as that configuration. The booked doctrine: "冒烟只当存活哨兵" - until the watcher evaluates at the deployment operating point with graded metrics, its role is detecting catastrophes, not ranking checkpoints; ranking belongs to the full battery at the design operating point (and the watcher's scoring was separately patched to weight survival 3x so recoveries during hard phases are not early-stopped away).
Change
Watcher debt booked (--kd-scale passthrough); score saturation acknowledged with graded columns planned; selection authority kept with 20-seed batteries at the declared operating point.
Outcome
A false panic did not abort a rung that was actually passing at its design point; a frozen score did not hide real drift gains; the instrument's authority was scoped to what its configuration can actually see.
Mechanism
An evaluator is itself configured (gain profile, delay, metrics); evaluating a policy away from its design operating point measures a counterfactual robot, and bounded scores saturate once binary gates pass, losing all sensitivity. Instruments need the same operating-point discipline as deployments and graded outputs to retain gradient.
Applies when
- an automated smoke loop contradicts the official battery
- early-stop scores freeze while graded metrics still improve
- lineages with non-default deployment gain/delay profiles
“watcher (kd1.0 口径) 3300 起 1/3 恐慌被 kd1.2 正式扫描证伪为考纲外假象 —— 工作点评测口径教训: watch_ckpt 缺 --kd-scale 透传 (待补), 冒烟只当存活哨兵。… watcher score 饱和误停 @1050(八门全过恒 0/3 时 score 对漂移改善盲, s1f 课文三现)”
train/README.md § omni_s2e_fric (watcher 恐慌被证伪) / omni_s2e_pd (500 臂) Export every CAD part in the whole-machine frame so URDF rotations are zero and inertia is exact
urdf-shared-origin-exportGenerate the model so that correctness is structural: shared-origin STL export, zero rotations, subtraction-only origins, and an explicit 1e-9 g*mm^2 -> kg*m^2 conversion - never hand-rotate inertia tensors.
Symptom
Hand-assembled URDFs accumulate per-link rotation/origin errors and unit-conversion mistakes in inertia tensors - silent plant corruption that no later calibration can cleanly fix.
Context
Documented CAD -> URDF -> USD procedure from a successful Isaac Lab deployment, kept as the recipe if Lucen regenerates its model.
Change
(1) In CAD, align the whole robot to Z-up, X-forward (Isaac Lab convention) and ground the assembly; (2) export each STL with other parts hidden but the machine's shared origin kept, so all parts share one origin, every URDF rotation is 0, and inertia matrices equal CAD values directly; (3) units: Fusion 360 gives g*mm^2, URDF wants kg*m^2 - multiply by 1e-9; (4) link origin = negative of the joint position; link COM = CAD COM minus joint position; joint origin = difference of the two joint positions; (5) after URDF -> USD import, open the USD separately and set it instanceable before saving.
Outcome
A URDF whose rotations are all zero and whose inertia tensors are CAD-exact, eliminating an entire class of hand-transcription plant errors.
Mechanism
Keeping one shared origin turns every frame transform into a pure translation computable by subtraction, and leaves inertia tensors in the frame CAD already computed them in - no rotation of inertia tensors, the most error-prone manual step, is ever needed.
Applies when
- building or regenerating URDF/MJCF from CAD
- inertia or frame bugs suspected in the plant model
- importing URDF into Isaac Lab / USD
“导出 STL 时隐藏其他零件但导出整机——这样所有零件共享同一原点，URDF 里所有 rotation 全是 0，惯量矩阵直接等于 CAD 值 / 单位：Fusion 360 给 g·mm²，URDF 要 kg·m²，乘 1e-9 / link origin = 该关节坐标取负 … URDF → USD 导入后必须单独打开 USD 设成 instanceable 再存”
Experience.md § URDF 制作流程 (lines 87-92)