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
27 cards matching “seated-basin-dead-exp-kernel”.
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 Exponential tracking kernels go flat exactly when the error is largest - pair them with an L2 term for the far field
exp-kernel-needs-l2-far-fieldNever let an exp/Gaussian kernel be the only tracking pressure on a quantity that can drift far from target: pair it with an unbounded (L2) term sized as the "don't diverge" floor, and check which frame the kernel reads.
Symptom
With only an exp-type yaw tracking term (exp(-err/std^2), std 0.25), a robot whose heading had drifted badly received almost no corrective gradient: at error 0.6 rad/s the term evaluates to exp(-0.36/0.0625) = 0.003 - near zero AND flat.
Context
The exp kernel is excellent for fine tracking near zero error but its gradient vanishes at large error - precisely when correction matters most. Fix: add track_ang_vel_z_err_l2 (-0.5), a plain quadratic on the same quantity: "exp 管精细跟踪、L2 管'别发散', 互补". Both terms deliberately read WORLD-frame wz (matching the exp term's source), because this torso sways enough that body-frame wz means are systematically off (measured -0.039 while actually turning +0.152). The same far-field-gradient argument reappears in the v8 risk list: frozen joints could not climb back because their huge error put them on the exp plateau ("远端梯度消失是冻结自锁的帮凶").
Change
Added the L2 companion term at -0.5 alongside the existing exp term (a term that had been in an earlier draft and was lost in a rewrite - itself worth noticing).
Outcome
Corrective pressure restored across the whole error range; the exp+L2 pairing became the house pattern for tracking terms.
Mechanism
d/de[exp(-e^2/s^2)] -> 0 as e grows: the kernel saturates and cannot distinguish bad from terrible. A quadratic's gradient grows with error, covering the far field; summing the two yields monotone corrective pressure with fine shaping near the target.
Applies when
- tracking rewards use exp/Gaussian kernels alone
- a drifted or frozen state fails to recover during training
- designing tracking terms for quantities with large transient errors
“exp 在误差大时梯度趋零, 恰好在最需要纠正的时候失灵。… 误差 0.6 → exp(-0.36/0.0625) = 0.003, 接近零且平坦。… exp 管精细跟踪、L2 管"别发散", 互补。”
train/WALK_V7_SPEC.md § ② track_ang_vel_z_err_l2 −0.5 —— 补 exp 的梯度洞 An exponential kernel on instantaneous velocity punishes gait oscillation - track the cycle average
cycle-average-tracking-for-gait-quantitiesReward velocity tracking on gait-cycle averages (or filtered values), not instantaneous samples, whenever the desired behavior oscillates at stride frequency; widening the kernel does not fix a variance penalty.
Symptom
Even while the robot genuinely sidewalked (verified after the metric fix), the Isaac-side tracking reward sat on the ignore-floor: true sidewalk scored 0.178 vs 0.189 for ignoring the command - the reward was mildly punishing the desired behavior.
Context
Sidewalking is inherently oscillatory: per-frame vy std was 0.177 while the tracking kernel width was sigma = 0.15, applied to the instantaneous value. A kernel-width scan showed widening sigma 0.15 -> 0.50 still loses (-0.14 -> -0.07): "指数核惩罚的是方差，而侧步天生带方差" (the exponential kernel penalizes variance, and side-stepping inherently carries variance). Modeling with measured parameters: replacing instantaneous vy with the mean over one gait cycle (0.5 s) flips the margin decisively (true sidewalk 1.888 vs ignore 1.281, +0.607), half-cycle is neutral (+0.006), two cycles adds nothing more. Explicitly flagged as extrapolation pending Isaac-side implementation. This also vindicated a previously dismissed external note (sigma too small) - right conclusion, different mechanism than claimed (variance, not gradient).
Change
Proposed fix recorded: change the tracked quantity from instantaneous vy to a one-gait-cycle running average; widening sigma alone rejected by the scan.
Outcome
Diagnosis complete and quantified; the C4 product shipped via feed-forward before the reward change was implemented, so the cycle-average fix remained a verified-by-model, not-yet-trained change.
Mechanism
E[exp(-(v-c)^2/sigma^2)] decreases with Var(v) even when E[v] = c exactly; a gait's phase-locked oscillation guarantees variance at the stride frequency, so instantaneous tracking rewards structurally prefer standing still at the command mean. Averaging over exactly one cycle removes stride-frequency variance while preserving command-following error.
Conflicts
The cycle-average fix itself is model-extrapolated ("⚠️ 这一条是外推，须在 Isaac 侧实装并复量后才能当结论") - the diagnosis is measured, the remedy untested in training at the time of writing.
Applies when
- tracking rewards for lateral/turn/any oscillation-carrying velocity
- a verified behavior scores below the ignore-floor
- choosing sigma for exp-kernel tracking terms
“侧走时 vy 的逐帧摆幅 std = 0.177，而 track_lin_vel_y_exp 核宽 σ = 0.15，且作用在瞬时值上 … 真侧走（均值 66%，振荡 ±0.18）0.178 | 完全无视指令 0.189 … 真侧走的得分比无视指令还低。… σ 从 0.15 放到 0.50，侧走仍然吃亏 … 把跟踪目标从瞬时 vy 换成一个步态周期（0.5 s）的平均 vy：… 1.888 vs 1.281”
train/C_LADDER_RUN.md § 3n. 二/三 Isaac 训练奖励为何一直坐在「无视底分」/ 修法不是放宽 σ 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 Record where every failed episode ends - an end-state confusion matrix showed all failures finishing seated and overturned a "cannot roll over" diagnosis that per-category success rates hide by construction
end-state-confusion-matrixFor any multi-category acceptance, report where each failed episode ends, not only which category it started in; it costs a few lines and no extra simulation, and it separates "cannot reach the goal" from "reaches the wrong basin".
Symptom
Prone scored 0% for three generations; the working diagnosis was "prone lacks the roll-over skill", and R0.3 spent a run adding prone-to-side roll-arc start states. It bought nothing: the 45-deg roll band itself only moved from 24.2% to 26.6% after 3,000 iterations.
Context
Acceptance reported success per starting category. A final-state table (lying prone / on the side / supine / seated / standing for every failed episode) was added to accept_recovery.py at R0.3.
Change
The confusion matrix became a permanent part of the acceptance output, and the prone diagnosis was rewritten from it.
Outcome
The prone, side and supine columns were all zero - every failure ended seated - and prone had righted its torso in 159/159 episodes (tilt under 30 deg in 100%). The missing ability was standing up from one specific seated configuration, not rolling over, which redirected the next rungs to foot placement and to a configuration probe.
Mechanism
Per-category success rates collapse "reached the wrong basin" and "never reached anything" into the same zero; the end state separates them.
Applies when
- a category sits at 0% and the diagnosis rests on its label
- recovery, manipulation or navigation tasks with distinct terminal states
- an intervention aimed at the presumed cause shows no effect
“**① 末态混淆矩阵 —— 固化(已在 `accept_recovery.py`)。** 它给出的 "趴/侧躺/仰躺三列全 0、所有失败都终于坐姿"是本线最改变决策的一个事实, 而**逐类成功率按构造看不见它**。 … 成本十来行、零额外仿真。 … **② prone 病因更正(旧诊断作废)。** 旧:"缺翻身"。新:**prone 159/159 全部 把躯干翻正**”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §15 R0.3 判决 + 三件事的判断 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 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) Fix a too-deep nominal pose before adding any penalties - the default stance defines the basin training starts in
nominal-posture-before-penaltiesBefore tuning penalties on a degenerate gait, audit the nominal pose and height targets against morphology and published ratios; if the default stance encodes the degenerate behavior, fix it first - and recompute dependent quantities (init height) by FK, not by hand.
Symptom
Policy lived in a crouched shuffle; nominal knee angle was 0.5 rad (28.6 deg) - deeper than published configs (Unitree G1 0.3 rad / 17.2 deg, Booster T1 0.4 rad) - so the policy's starting point and its action-space center both sat inside the crouch basin.
Context
Initially ranked "secondary" in the local diagnosis, this was promoted to co-first priority by the cross-check against published reward tables, which states that with nominal knee flexion above ~0.4 rad, fixing the posture must precede adding any penalties ("改这个之前别加任何 惩罚都是白费"). Companion base-height items: walk profile had weakened base_height_l2 to -5.0 (base class -10, field standard -10 to -20, "the second most common cause of death"), and the height target must be the STANDING height (0.384), not the crouch height.
Change
Nominal knee 0.5 -> 0.3 rad with init_base_height recomputed by MuJoCo FK (0.3739 -> 0.3802); base_height_l2 restored to -10 with standing height target; both bundled as first-priority alongside the clearance term.
Outcome
Part of the v5/v6 package that lifted swing height to 34 mm and tracking to 87%; the crouch basin stopped being the default answer.
Mechanism
The nominal pose is the fixed point every regularizer pulls toward and the point where action=0 lands; if that point is itself the degenerate posture, every penalty fights the geometry. Correcting the attractor is prior to shaping the gradient field around it.
Applies when
- policy converges to a crouched or collapsed posture
- nominal joint angles were chosen for stability rather than gait
- base-height reward targets or weights were locally weakened
“研究明确说"nominal 膝屈超过 ~0.4 rad 必须先改,改这个之前别加任何惩罚"。我们是 0.50,超标。… base_height_l2 在 walk profile 里被减到 −5.0(基类是 −10)。研究说这是"第二常见死因"且应 −10 ~ −20。改回 −10。目标高度用站立高 0.384 是对的(研究要求 target 必须是*站立*高度而非蹲姿)。”
train/WALK_DIAGNOSIS.md § 修正 ①(升级优先级) / 修正 ④ Foot dragging is an attractor, not a low amplitude - and joint damping is the mode switch, adjustable at deploy time
swing-bistability-damping-switchWhen a quality metric is bimodal, stop treating it as an amplitude to be trained up: map the modes against initial conditions and plant parameters, find the parameter that switches basins, apply it first as a deployment lever, and only then bake it into the training distribution (as a plant-family shift, never as an execution-mapping change).
Symptom
s2e_pd-1400's swing height "median 12.1 mm" hid a perfect bimodal distribution: 20 seeds split into a drag mode (2.6-4.9 mm) and a step mode (19.3-24.0 mm) with NOT ONE seed in between - the median sat in the empty gap, and "swing debt -11 mm" really meant "50% probability of falling into the drag attractor".
Context
Two designed experiments closed the mechanism. Test A (nominal plant, 40 seeds): step 42% / drag 58% / middle 0 - at nominal gains, initial conditions alone pick the mode, both modes 100% survivable. Test B (fixed init, kp x kd grid): kd is the mode SWITCH - at kd 1.3 all surviving cells step (13-22 mm), at kd 0.7 nearly all drag (2.7-4.3), only at kd 1.0 does init get a vote; kp >= 1.2 is dangerous (5/6 falls). Global verification at kd x1.3 (20-seed, delay 2): survival 20/20 at ZERO cost, step share 42 -> 80%, swing median 12.1 -> 18.4 mm, slip record low 334, thicker tilt margin - costs: vx 85 -> 78%, saturation +5 pp. A Pareto sweep then priced the knob: step share 42/72/75/88/82/90 across kd 1.00-1.30 with a linear vx tax of -2.3 pp per 0.1 kd - the basin gain is fully collected at kd 1.20 ("1.30 是 over-damping 纯多付税"). Mechanism: low damping leaves a landing micro-oscillation / ground-slide channel the policy can exploit to drag; damping plugs the channel.
Change
Deployment lever adopted: kd-scale 1.20 (conservative 1.15) as the legitimate successor to the power-0.8 crutch ("前者削幅度保稳,后者堵 拖地通道换步态,且不牺牲存活"); training-side prescription: move the DR band to nominal-1.2 x (0.9,1.1) = [1.08,1.32], deleting the [0.7,1.0) drag-teaching zone - a contract-level change requiring digest re-baselining, gated on measuring the real robot's actual kd dispersion first.
Outcome
The kd surgery rung (s2e_kd) delivered basin 8 -> 11/20, slip 405 -> 331, vx 81 -> 85% with no out-of-band fragility (below-band check 20/20) - "拐杖烧进分布的正确姿势", explicitly contrasted with the failed s1g amplitude version: this one changes the plant family the policy has seen, that one changed the execution mapping the policy would have to relearn.
Mechanism
The gait's swing behavior is a bistable dynamical system whose basin boundaries are set by plant parameters; a policy trained across a kd band that includes the drag basin has learned to inhabit it. Shifting the deployed (and then trained) damping moves the system into the step basin without touching the policy - a plant-side fix for what looked like a training deficiency.
Applies when
- a gait quality metric splits into distinct modes across seeds
- deciding between more training and a gain/damping change
- converting a deployment crutch into a training-distribution change
“20-seed 里拖地模式 2.6~4.9mm 与迈步模式 19.3~24.0mm 各半,中间一个不落 … kd 是模式开关——kd1.3 下 6/6 存活格全迈步 … kd0.7 下几乎全拖地 … swing 债的解(至少大半)在部署端阻尼档,不在训练端 … 机理:低阻尼下落脚微振荡/贴地滑给了策略顺势拖行的通道,加阻尼堵之。”
train/README.md § swing 双稳态定性 + kd 部署杠杆 (2026-08-07, 用户设计 Test A/B) 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 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 Gate a new reward term by its command so all old modes score pointwise identical
gate-new-reward-terms-by-commandWhen a reward term must be added mid-lineage, gate it on the condition that defines the new task so every pre-existing situation scores exactly as before - and still watch for value-rescale pathologies inside the new mode.
Symptom
Adding a lateral tracking reward (track_lin_vel_y_exp) ungated would have paid 0-2.0 per step even in modes with cmd_vy = 0 (healthy gait sway of vy ~0.1 already earns 1.28), shifting the whole reward table by a large bias and rescaling the value function - no longer "just adding one mode".
Context
C4 was the C ladder's only true reward surgery. Single-variable discipline required that the change be invisible to every existing mode. The chosen construction: gate_by_cmd=True - the term pays only when |cmd_vy| > 0.02, so for all modes with cmd_vy == 0 the term is pointwise zero, i.e. the reward is pointwise identical to before the change. The same trick appeared earlier in C1: replacing the vy L2 tax with a command-error version that is "对 cmd_vy≡0 逐点同值" (pointwise equal when cmd_vy is 0), explicitly classified as not-a-reward-change.
Change
track_lin_vel_y_exp added with gate_by_cmd=True (weight +2.0, std 0.15); the residual acknowledged honestly - inside the side bucket the values DO change, so the rung still watched the known reward-reshuffle pathology signature (s1c B-arm: scatter -> half-recover -> collapse) as a stop criterion.
Outcome
Old modes provably unaffected (pointwise-equal argument); attribution for any change in old-skill metrics stayed clean through the C4 redo series.
Mechanism
PPO's critic normalizes to the reward scale it sees; an ungated additive term shifts returns in every state and re-scales advantages globally, entangling the new skill with all old ones. Command-gating confines the new term's support to the new mode's state distribution, making "pointwise identical elsewhere" a provable property rather than a hope.
Applies when
- adding a tracking/shaping term for a new command or skill to a lineage that must not regress
- reward change proposed while other skills are still being gated
- reviewing whether a config diff counts as a reward change
“只在 |cmd_vy| > 0.02 时付。不门控的话它对 cmd_vy≡0 的老模式也给 0~2.0 分（健康摇摆 vy≈0.1 → 1.28），等于给整张奖励表加一个大偏置、值函数尺度全变 … 门控后老模式逐点得 0 = 与加项前逐点同值，单变量纪律成立。… 但 side 桶内的值确实变了 —— 这仍是奖励表改版，开级盯 s1c B 臂签名”
train/C_LADDER_RUN.md § 3d. gate_by_cmd=True（重要） Decompose the offending quantity by channel first - then penalize the failure event, not the joints
penalize-the-slip-not-the-jointBefore penalizing motion to fix a side effect, measure which channels actually carry the offending quantity; prefer penalties conditioned on the failure event that are exactly zero for healthy behavior - and do not medicate behaviors that measurement shows are not sick.
Symptom
Heading drift with support-foot yaw slip (v5: 212-284 deg accumulated over 15 s); the previous v6 draft had attacked it by penalizing lateral joints (a roll 4.0 / yaw 2.0 "home" group) - which collapsed training into the standing basin.
Context
Before choosing the penalty target, the yaw angular momentum was decomposed by joint group with MuJoCo subtree_angmom weighted by real walking joint velocities: pitch-class joints (hip_pitch + knee) carry 95.3%, hip_roll 3.3%, hip_yaw 1.4%. The failed "home" group had been taxing 2.7/step to manage a 4.7% channel. The replacement, feet_yaw_slip (-0.2, |support-foot yaw rate| while in contact), targets the failure event itself and - decisively - costs a non-slipping gait exactly zero, which "横向回家组做不到". The same rung's do-not-do table applied the complementary principle to foot spacing: measured 196-214 mm, stable, no crossing - "没病不吃药" (no disease, no medicine).
Change
Removed joint-usage penalties for the drift problem; added the event-conditional slip penalty (-0.2, realized tax 0.141/step = 12% of tracking) alongside the existing linear-slip term.
Outcome
Turn-gain left/right difference improved 70% -> 19% and heading 185 -> 60.3 deg by v6 without a standing-basin collapse; the 2.7/step lateral tax never returned.
Mechanism
Penalizing joints taxes every use of a channel including healthy use, and if the channel carries little of the offending quantity the tax buys nothing while pushing the optimum toward immobility. An event-conditional penalty (slip while in contact) prices only the failure, leaving the healthy gait's cost surface untouched - and the channel decomposition tells you in advance whether a joint-side fix can even work.
Applies when
- choosing a penalty target for drift/slip/impact problems
- a proposed penalty taxes joints or motions rather than failure events
- a previous joint-penalty attempt collapsed the gait
“pitch 类 (hip_pitch + knee) 占偏航角动量 95.3% … hip_yaw 1.4% … 压 hip_yaw 是管 1.4% 的通道收 2.7/步 的税 —— 上一轮正是这样把策略推进了站立盆地。滑移项不惩罚走路: 不打滑的步态代价为零, 这是横向"回家"组做不到的。”
train/WALK_V6_MINIMAL.md § ① / ② 新增 feet_yaw_slip A training-side rate limit anchored on the last commanded target is an integrator inside the balance loop - two unrelated lineages converged to the same 34-43% re-fall rate, a soft penalty could not fix it, and the bandwidth arithmetic said safety and standing could not coexist
slew-anchor-is-an-integratorIf you constrain actions in training, anchor the constraint on the measured state, not on the previous command - a limiter with memory adds lag inside the balance loop; and when two different lineages converge to the same failure rate, treat the cause as structural and stop adding soft penalties.
Symptom
With the rate limit moved into training (V1), policies either could not stand up or stood up and kept falling again: the stand oscillated, fell and climbed back, 34-43% of the time.
Context
V1.0-B (user decision: explore new postures from scratch, hard constraint in training): target <- prev + clip(target - prev, +/-S*dt) at the TIGHT rates, anchored on the last issued target like the bridge. Four runs: v1_0 from scratch 0.8% - every category righted under the limit, then knelt (the limit also damped the exploration that had escaped the seated basin in V0); v1_0c continued from R3.1 99.8% get-up but 36-43% re-fall and knee jitter 0.604 rad/s; v1_0p with a pull-assist curriculum (56 N -> 0, fully withdrawn) 39.1% unassisted, re-fall 34-42%; v1_0w with a windup-gap penalty 25.4%, re-fall 18-43%. Removing the limiter from v1_0p gave 0.0%: the policy had co-adapted with it.
Change
The rate-limit route was declared dead after four runs and the line was re-rooted on a beta-anchored action space (V2, user approval required).
Outcome
V2.0's first acceptance at full authority already showed re-falls of 0-1% (V1: 34-43%), the structural bet paying off before any tuning.
Mechanism
Anchored on the previous command, a saturated policy becomes a rate controller - one more integrator in the loop - and active balance through that lag oscillates, while a kneeling sit needs no active control and is stable. The arithmetic: kp 30 needs 0.4 rad of error for 12 N*m; at 4 rad/s that takes 0.1 s, half the pendulum time constant sqrt(0.38/9.8) ~ 0.2 s; keeping standing bandwidth needs S of at least ~8 rad/s, within 20% of the 10 rad/s velocity limit - no bound at all. Anchoring on the measured angle (q + beta*a) makes the full kp*beta authority available in one step, with no memory, and caps the impact at the same time.
Applies when
- adding rate limits, slew limits or target filters to a policy's action path
- a policy stands but oscillates and re-falls after a constraint was added
- different lineages or curricula land on the same failure signature
“v1_0p(拉力课程):会站(prone 79.9%),再摔 34~42% —— **两条完全不同 血统、不同学习路径,收敛到同一失败率**。 … 动作饱和时它退化为 速率控制 = 环内多一个积分器;主动站姿平衡穿过该滞后必振荡(v1_0 的跪坐不需 主动控制,所以稳)。对照:**HoST 的 β 锚在当前实测 q,无记忆、无积分器**”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §31 结构病定案:slew 的目标锚 = 控制环里的积分器 Multiple changes may share one rung only if their symptom spaces are orthogonal - with the ablation order written in advance
orthogonal-batch-with-ablation-orderBatch changes into one rung only when you can name each change's private symptom space in writing; pre-register the ablation order (numeric before structural) and per-change escalation plans, so a mixed outcome decomposes without new decisions.
Symptom
v8 needed four repairs at once (saturation cheating, landing impact, leg narrowing, heading alignment) - strict one-variable laddering would have cost four training cycles for wounds that were all already diagnosed.
Context
The batch was allowed because each change owns a disjoint symptom space, stated explicitly: "A→形态/饱和, B→落地/步态高度, C→腿距/roll 摇摆, D→偏航/转向" - so a single run can attribute each outcome to its change by which symptom moved. For the failure case, the ablation order was pre-registered (A2 -> A1 -> B -> D -> C, "先撤数 值改动" - retract numeric tweaks before structural ones), and every change carried its own escalation/rollback plan (e.g. A insufficient: joint_pos_ref 1.6->2.0 or widen the exp kernel; B overdone - robot afraid to land: v_ok 0.30->0.45; D unstable: rel 0.5->0.25, not back to 0).
Change
Four-change rung executed as one run with per-change symptom ownership, per-change contingency plans, and a pre-registered global ablation order for unattributable regressions.
Outcome
The rung retained single-run attributability without paying 4x training cost; the contingency table meant no failure mode would require improvising an ablation under pressure.
Mechanism
The one-variable rule exists to keep attribution possible, not as an end in itself; attribution survives batching exactly when the changes' observable effects are separable. Orthogonality is a claim that must be argued per pair in advance - and the pre-registered ablation order is the escape hatch for the case the claim fails.
Applies when
- several diagnosed fixes are queued and ladder time is scarce
- deciding between strict laddering and a combined rung
- a combined rung shows a regression no single change explains
“四个改动症状空间基本正交,可单 run 归因:A→形态/饱和,B→落地/步态高度,C→腿距/roll 摇摆,D→偏航/转向。出现无法归因的整体退化时消融顺序 A2→A1→B→D→C(先撤数值改动)。”
train/WALK_V8_SPEC.md § 8. 风险与归因 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 Record-high training reward hid a fully-failing DR subgroup - aggregate metrics average over draws, gates must test per condition
aggregate-metrics-mask-subgroup-failureNever gate on metrics aggregated across DR draws: evaluate at fixed representative conditions (especially the deployment-critical stratum), and if a difficulty axis matters, ramp it on measured per-stratum success rather than sampling the full range from iteration zero.
Symptom
omni_s1e trained under constant-wide latency DR (0, 0.06 s) posted the lineage's highest-ever Isaac reward (129) - while the --delay 2 smoke evaluation showed 3/3 falls from iter 1500 onward, persisting to early stop; the usable checkpoint window shrank to iters 500-1000.
Context
Diagnosis written plainly: "聚合奖励掩盖重延迟尾部子群体失败" - the aggregated reward averages over latency draws, so the majority of light-delay environments can mask the total failure of the heavy-delay tail. The remedy for the training side was a survival-gated ratchet curriculum (survival_gated_latency): the sampling cap starts at 0.02 s and rises +0.01 only when a 4096-reset window's survival (time_out share) reaches >=90%, capped at 0.06, ratchet up-only - "增益与延迟耐受一起长,不升到策略撑不住的 地方" (gain and delay tolerance grow together; never raise past what the policy can hold). The detection side was already in place from the noise-crutch episode: the per-condition smoke curve, not the training reward, is the health readout.
Change
Latency exposure made curriculum-gated on measured subgroup survival instead of uniform-from-zero; per-condition (--delay 2) smoke evaluation kept as the authoritative curve; watcher scoring adjusted (survival weighted 3x) so recovery during hard phases is not early-stopped away.
Outcome
The failure mode was caught by the smoke curve within one generation; the follow-up redesign (deterministic staged latency) superseded the ratchet, but the aggregate-masking lesson held through both.
Mechanism
Expected-return training weights each DR draw by probability, so a subgroup can contribute bounded loss while being catastrophically failed; any scalar averaged over the randomization cannot distinguish "uniformly decent" from "great on easy draws, dead on hard ones". Only conditioning the evaluation on the stratum reveals the split, and curricula should raise difficulty on measured stratum success, not on schedule.
Applies when
- training reward hits records while a fixed-condition eval degrades
- wide DR on an axis where deployment sits at one known value
- designing curricula for difficulty axes (delay, push, terrain)
“常量 latency DR (0,0.06) 从零训被证伪——Isaac reward 129 历代最高,但 --delay 2 冒烟 iter1500 起 3/3 全摔持续到早停(聚合奖励掩盖重延迟尾部子群体失败,可用窗口只剩 500/1000)。… 采样上限 0.02 起步 … ≥90% 才 +0.01s,0.06 封顶,棘轮只升不降。”
train/OMNI_V0_SPEC.md § 3. S1.5(s1e 训练塌方复盘) The standing-pose reward had been pulling toward the narrow stance the whole line was fighting - a zero-training kinematic audit of the target vector found it, after first auditing the wrong nominal
pose-target-geometric-auditBefore training on a pose target, audit it with forward kinematics - is it geometrically consistent (feet flat, intended stance, intended width) and is it the frame you think it is (action nominal vs standing default)? A posture term's target may itself be the attractor you are fighting.
Symptom
Several rungs aimed at widening the stance failed; the stance stayed narrow as if something kept pulling it back.
Context
The audit (MuJoCo forward kinematics, no training): every 5 deg of hip roll widens the stance ~5.5 cm (0.271 m at 5 deg, 0.383 m at 15 deg); at 47 deg of hip yaw a wide stance cannot be flat-footed (residual foot tilt ~0.7 x hip roll), which explained the stalled rungs. The first report also said the stand_pose nominal (hip roll 25, knee 60) has a 63 deg residual foot tilt - but that was the action frame's nominal (the limit-midpoint squat), which stand_pose never used, despite a docstring warning not to mix them. stand_pose's real target was DEFAULT_JOINT_POS: the contract's all-zero pose, legs parallel, ~0.22 m apart.
Change
The disease statement was corrected in writing: the narrow stance was not an accidental by-product of proxy traps but the target stand_pose had been actively rewarding. A stored "narrow the stance" knife was marked toxic. V2.8 moved the target to a flat 15-deg stance (sigma 3 -> 1.5, flat_feet margin 5 -> 20 deg).
Outcome
V2.8 still failed in-lineage (stance unchanged, feet nearly overlapping, mu 0.4 transfer 2%) - see stance-decided-by-get-up-path - and the from-scratch V3.1 removed both roll joints from stand_pose and put width into a task-space term, which is what finally produced a 0.355 m flat stance. The 63 deg finding was kept as a warning: an action nominal used as a standing target would be a ready-made pit.
Mechanism
A posture term with a sharp kernel around the wrong target is an active attractor; every other term fighting it pays twice.
Applies when
- a posture keeps returning despite penalties against it
- a reward uses a default or nominal pose as its target
- the contract has more than one "nominal" (action frame vs standing pose)
“上文"stand_pose 的 nominal (hip25/knee60) 残倾 63°"**审计错了对象**:那是 **动作参考系 nominal**(限位中点蹲),stand_pose 从未指向它(函数 docstring 原文即警告"两者别混",还是混了 —— 记档)。 … **修正后的病根陈述:窄站距不是代理陷阱的意外副产物,而是 stand_pose 一直在主动奖励的目标本身**”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §44 勘误(实现时抓到):审计混了两个 nominal —— 真病根比误诊的更直白 Drop the frozen policy into chosen configurations - a squat 2.7 cm lower than the stuck pose stood 52% of the time, the stuck W-sit 0%, and the interpolation between them showed a wall, not a slope
configuration-probe-wall-not-slopeWhen a policy is stuck, probe the frozen policy from a grid of hand-placed start configurations, including interpolations between the stuck state and a nearby state it escapes from; one read-only experiment separates height, torque, sampling and configuration and tells you whether to prevent entry or train the exit.
Symptom
After R0.3 the policy stood from 62% of starts and never from the W-sit it fell into; height, torque, missing samples and reward were all plausible suspects.
Context
A read-only probe placed the R0.3 policy directly into specified configurations. Squats (hip, knee, ankle) = (-.65,-1.3,-.65) stood 100%, (-1.0,-2.0,-1.0) 89.8%, (-1.2,-2.4,-1.2) at 0.176 m 52.3%; the measured W-sit at 0.203 m 0.0%; the account-(3) hand-over state (146 deg tilt) 34.4%; linear interpolations from the W-sit toward the squat at 25/50/75% stood 0.0/0.0/3.1%. The squat family's quasi-static torque is 16% of the limits, and the W-sit was visited ~9 s per episode in training. FK showed the squat family (-a,-2a,-a) keeps the torso vertical, the feet flat and the COM over the feet all the way from 0.146 m to 0.384 m.
Change
Height, torque and sampling were eliminated in one experiment; the next rungs targeted entering the W-sit (foot placement) instead of escaping it, and seeding the dead point itself was ruled out because it was already visited every episode.
Outcome
Pure configuration: the W-sit (hips externally rotated +/-47 deg, knees folded 110 deg, shins flat, feet beside the body) is a different place from the sagittal squat (feet flat under the COM). The policy's standing skill was bound to a narrow sagittal family, and the wall was confirmed by the interpolation. The foot-placement rungs that followed took prone from 0/159 to 158/159.
Mechanism
A learned skill covers the neighbourhood of the states it succeeded from; a start state outside that neighbourhood fails regardless of height or torque, and an interpolation that stays at zero until close to a working state shows the boundary is sharp.
Applies when
- a policy stalls in a specific posture and several causes are plausible
- deciding between reverse-curriculum seeding and entry-prevention shaping
- a feasibility account says a path exists but the policy does not take it
“**决定性对比:比死点矮 2.7 cm 的蹲姿站立 52.3%,死点 0.0%。** 所以不是高度、 不是力矩(蹲姿族准静态力矩膝 1.96/12、踝 1.24/17,只占 16%)、也不是训练采样 (死点每局被访问 ~9 s)。**是纯位形问题** … 插值实验进一步显示这**不是坡是墙** —— 走到 75% 仍只有 3.1%”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §15 死点位形实验(只读探针,同一个 R0.3 策略放进指定位形) Before adding a command mode, compute what ignoring it costs - the lazy optimum must lose
reward-cost-of-ignoring-auditPrice the do-nothing policy for every new command or objective: compute reward-per-step for "comply" vs "ignore" from the actual table, and only train once ignoring is decisively unprofitable.
Symptom
A new command mode can be silently unlearnable if the reward table makes "ignore the command entirely" nearly free compared to the tracking reward available elsewhere.
Context
For each C rung the team computed the per-step cost of completely ignoring the new command versus ignoring forward: ignoring vx=0.25 costs 1.264/step; ignoring wz=0.20 costs 0.984/step (78% of forward - gradient sufficient, so C2 was certified "zero reward surgery"); but ignoring vy=0.10 cost only 0.020/step - 50-60x weaker, because vy entered the table only as an L2 tax, not a tracking term.
Change
Rule instituted: a rung may claim "no reward change needed" only after this arithmetic shows the ignore-cost is the same order as forward's. For C4 the audit failed, so track_lin_vel_y_exp (+2.0, std 0.15, same form as vx) was added - raising the ignore-cost at cmd_vy=0.10 from 0.020 to 0.718/step (36x).
Outcome
C1/C2/C3 proceeded with zero reward edits, keeping single-variable attribution clean; C4's needed surgery was identified before training instead of after a failed run.
Mechanism
PPO converges to whatever costs least; if the reward margin for obeying a new command is a rounding error against existing terms, the "ignore" policy is the optimum and no amount of training fixes it. The audit prices the lazy optimum explicitly before spending compute.
Applies when
- adding a command axis or task mode to an existing reward table
- a new skill trains flat while other skills stay healthy
- certifying a rung as "no reward change"
“cmd wz 0.20 → 0.984（coarse .473 + fine .491 + L2 .020）… 对照：忽略 vx=0.25 = 1.264（本级 78%，同量级）；忽略 vy=0.10 = 0.020（弱 50 倍——那才是 C4 必须加 track_lin_vel_y_exp 的原因）。本级不动奖励表。”
train/C_LADDER_RUN.md § 4. 原地转级（C2）② 奖励梯度已验够 Compare the achieved reward to the computed ignore-floor to tell "never learned" from "learned but unprofitable"
ignore-floor-diagnosisFor any skill that trains flat, compute the reward the null policy would earn on that term; achieved==floor means the behavior never paid out (find why: exploration, reward observability, or feasibility) - do not tune weights first.
Symptom
C4 sidewalk failed on both arms; the question was whether the policy had found sidewalk and rejected it as unprofitable, or never found it at all - two diagnoses with opposite fixes.
Context
The theoretical value of the sidewalk tracking term for a policy that completely ignores the command was computable from the command distribution: 0.189. Trained final values landed at 0.187 (arm A) and 0.204 (arm B) - sitting exactly on the ignore-floor - while the honest balance at the optimum actually favored sidewalking (net +0.88/step inside the side bucket). Later the same arithmetic closed the whole saga: for the true reward landscape, doing real sidewalk scored 0.153 vs 0.944 for ignoring - the policy's refusal "是理性最优，不是探索失败" (rational optimum, not exploration failure) under one hypothesis, and under the final measurement-corrected account the policy had "每一次都在 做理性选择" (made the rational choice every time).
Change
Diagnostic rule adopted: compute the ignore-floor for the new term; if the achieved value sits on it, the behavior was never expressed in useful volume (or the reward cannot distinguish it - check both); if the achieved value is above floor but the behavior is absent at deployment, the policy sampled it and priced it out - then the reward balance, not exploration, is the lever.
Outcome
Correctly identified that PPO had not merely under-valued sidewalk; each subsequent hypothesis (waveform sign, regularization cage, exploration form, reward kernel) was tested against this floor arithmetic, which kept the search honest through three reversals.
Mechanism
Every reward term has a computable value under the null behavior; the achieved-vs-floor gap is a one-number audit of whether the optimizer ever monetized the target behavior. It converts "training failed" into one of two mechanistically distinct states with different fixes.
Applies when
- a new skill's tracking reward plateaus early
- deciding between exploration fixes and reward-weight fixes
- post-mortem of a failed curriculum rung
“track_lin_vel_y_exp 训练终值恰好坐在「完全无视指令」的底分上（A 0.187 / B 0.204，理论值 0.189），而终点 balance 明明有利（side 桶内净 +0.88/步）。不是学会了不划算，是根本没学到。”
train/C_LADDER_RUN.md § 3e. 为什么首战 FAIL Torque caps cannot soften footfalls - impact is falling-mass momentum, only the reward can treat it
landing-impact-not-fixed-by-torque-capsClassify each hardware symptom by the physics that sets it: quantities fixed by ballistic momentum at contact must be treated through the policy's trajectory (reward terms on approach velocity/force), never through actuator caps - and size such penalty weights against your own tracking reward, not a lighter robot's.
Symptom
Footfalls slammed at 1.78x body weight in sim baseline (human walking: 1.2-1.5x); the tempting hardware-side fix was cutting actuator torque limits.
Context
Measured directly: scaling torque limits from x1.0 down to x0.4 left peak landing force essentially unchanged (1.75 -> 1.78x body weight) - the impact force comes from the momentum of the falling mass at touchdown, not from motor effort. The fix has to change the trajectory, i.e. the policy, i.e. the reward: feet_contact_forces penalty above a threshold of 113 N (= 1.2x the 9.58 kg robot's weight), clipped, weight -0.005. The weight was sized locally, not copied: the reference robot's -0.001 would amount to 0.9% of tracking reward on this robot ("策略不会理它" - the policy would ignore it); -0.005 gives 4.4%.
Change
Added threshold-type contact-force penalty (-0.005, threshold 1.2x body weight) as one of v6-minimal's three changes; hardware torque cuts explicitly rejected as a footfall treatment.
Outcome
Landing force 1.72x -> 1.55x by v6 (target <1.5x, missed by 3% - progress booked honestly); the torque-cap dead end was documented so it would not be retried.
Mechanism
At touchdown the ground stops a ballistic mass; the impulse is set by approach velocity and effective inertia, which motors can no longer influence in the final instant. Only earlier trajectory choices (approach velocity, timing) reduce it - and those are selected by the reward, not by actuator limits.
Applies when
- footfall impact or landing noise on hardware
- proposals to derate torque as a softness fix
- importing contact-force penalty weights from another robot
“⚠️ 硬件限扭降不了落脚力 —— 砸地力来自下落质量的动量: 实测 tau ×1.0→×0.4, 落脚力 1.75→1.78× 体重纹丝不动。只有这条奖励能治。… ⚠️ 权重不能用 Pi 的 −0.001 —— 实测在我们身上只占跟踪奖励的 0.9%, 策略不会理它 (Pi 6.94 kg 更轻)。−0.005 给到 4.4%。”
train/WALK_V6_MINIMAL.md § ③ 新增 feet_contact_forces Every rung gets written stop criteria - hit any one, stop; tightened when priors say results should come fast
preregistered-stop-criteria-per-rungFreeze per-rung stop criteria (old-skill floors, new-skill deadline, oscillation signature, known pathology signatures) before training, stop on first hit - and shorten the deadline in proportion to how fast your mechanism says results should appear.
Symptom
Continued training past the point of degradation had previously destroyed capabilities (C1 trained away the root's backward skill); without hard stop rules, sunk-cost reasoning keeps runs alive too long.
Context
Standard rung protocol - eval_c_matrix every 100 iters at 5 seeds with a fixed criteria list, frozen before training ("开训前写死，事后不许改"): (1) stand or vx+0.15 at <=3/5 for two consecutive points; (2) vx+0.15 tracking <70% for two consecutive points (vs recorded root baseline); (3) oscillation signature - survival repeatedly crossing zero between adjacent checkpoints, stop on first occurrence, do not wait for confirmation; (4) new-skill-no-progress deadline (e.g. wz tracking <30% or still same-signed after iter 400); (5) a known lineage pathology signature (s1c arm-B: scatter -> half-recover -> collapse).
Change
Stop budgets scale with prior knowledge: when C4-redo4's feed-forward was already proven open-loop, the no-progress deadline was tightened from +500 to +100 iters ("前馈是开环就给 71~96% 的 … 若 +100 还没有 … 不值得再烧").
Outcome
Multiple rungs were stopped exactly on criterion (C4-redo criterion 4 at iter 1200; redo2/redo3 at absolute 900), converting each into a clean hypothesis test instead of a drifting run; no rung burned its full cap on a dead hypothesis.
Mechanism
Degradation of retained skills is a lineage-level injury that later training often cannot undo, so detection latency is capital loss; and a stop rule written before the run cannot be bent by hope. Tightening deadlines when priors predict immediate results converts "no result yet" into evidence against the mechanism.
Applies when
- starting any resumed/curriculum training rung
- deciding whether to keep training a run that shows early regression
- a mechanism-backed change should produce results immediately
“每 100 iter eval_c_matrix.py --seeds 5，命中任一即停 … 存活在相邻 checkpoint 间反复跨越 0（出现即停）… 收紧版：绝对 iter 900（= +200）时 vy±0.10 跟踪仍 <30% 即停”
train/C_LADDER_RUN.md § 3h. 预注册停梯判据（开训前写死，事后不许改） 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 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 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 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)