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
101 cards matching “stop-stacking-roll-back-and-audit”.
Four consecutive fixes were each continued from the previous fix's degraded state until the user stopped the ladder - "change parameters, don't stack errors" - rolled back to the last good checkpoint and audited the target geometry first
stop-stacking-roll-back-and-auditWhen successive rungs each start from the previous rung's output and the target symptom does not move, stop, roll back to the last good checkpoint and re-derive the next change from an audit; keep the measurements, discard the stacked remedies.
Symptom
After the real-robot splits, the V2.7 ladder tried to widen the stance: a term swap (A), a new stance knife (b), more iterations (甲), a doubled weight (乙). Stance barely moved while hip yaw ratcheted 46.7 -> 49.9 -> 52.5 deg toward its 60 deg limit.
Context
Each rung started from the previous rung's output. The user ruled on 2026-08-11 that things had gone wrong from V2.7-A: go back to v2_6 and rethink which parameters to change instead of stacking errors.
Change
乙 was killed at start and not counted; the product baseline rolled back to v2_6c model_29399; every measurement and law learned on the ladder was kept ("the data is real; what stacked was the treatment"). Before any new training, a zero-training kinematic audit of the stance targets was run.
Outcome
The audit found the stand_pose target itself rewarding the narrow stance (pose-target-geometric-audit) and showed geometrically why the yawed stance could not be widened with flat feet - so 乙 was proven unnecessary without running it. The next in-lineage attempts still failed, which is what established that the stance is set by the get-up path.
Mechanism
A rung continued from a degraded state inherits its compensations, so each new fix answers the previous fix's side effects; the yaw ratchet was the visible trace of that stacking.
Applies when
- three or more corrective rungs in a row without progress on the target metric
- a side-effect metric ratchets in one direction across rungs
- a new rung is being planned from the latest (not the best) checkpoint
“用户裁:"从 V2.7-A 开始就出问题了,应该回到 2.6 再思考如何改变参数而不是 错误叠加。"认账:A 的补丁 → b 的新刀 → 甲的加时 → 乙的加权,每级都从上级 的**退化状态**续(yaw 46.7→52.5° 的棘轮就是叠加痕迹)。 … 数据是真的,叠加的是处置。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §44 方法论裁定(2026-08-11,用户):V2.7 全阶梯叫停,回滚 v2_6 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） Audit which joints your imitation term constrains - a task that needs deviation is fighting the reference
imitation-term-scope-auditList which joints your imitation/deviation terms actually constrain and check the new skill's required motion against that list; for balance-coupled joints deliver references as feed-forward residuals, not absolute-position targets - and never assume "reference = 0" is neutral.
Symptom
Sidewalk would not learn despite a dedicated tracking reward; meanwhile the gait-shaping imitation term (joint_pos_ref) computed its error norm over ALL 12 joints while its reference covered only the 6 sagittal joints - roll/yaw reference was constantly 0.
Context
Two prior generations had shown the forward gait itself was taught by joint_pos_ref, not discovered by PPO (v6 halved the shaping and swing height collapsed 35 mm -> 4 mm). So the reference is load-bearing - but sidewalk requires hip_roll to deviate from nominal, and the all-joints norm punished exactly that deviation: "一边悬赏一边罚过程" (posting a bounty while punishing the process). A follow-up experiment (C4-redo3, free_roll=True releasing the 4 roll joints from the norm) raised the regularization headroom 6x -> 27x yet sidewalk stayed flat and released hip_roll wandered, killing other skills - net negative, withdrawn. A --roll-absolute probe showed the converse failure: pinning roll to a clock-driven absolute trajectory drove tilt 6.9 -> 13.7 deg. Conclusion recorded: absolute-position imitation cannot teach actions that must be superimposed on state feedback.
Change
The audit reframed the problem: neither punishing roll deviation nor freeing roll nor absolute roll tracking works; the reference for a balance-coupled joint must be delivered as feed-forward under the policy's residual control (see feedforward-for-phase-locked-skills).
Outcome
free_roll rung: joint_pos_ref term rose 0.887 -> 1.104 (release confirmed effective) but vy stayed flat; regularization hypothesis eliminated by experiment.
Mechanism
An imitation error norm defines a cage: joints inside it are pulled to the reference in absolute position, so any skill requiring systematic deviation is taxed per step; but joints carrying active balance cannot follow absolute references either, since their correct position depends on state. The scope and the delivery mechanism of the reference are therefore design decisions per joint, not defaults.
Applies when
- adding a skill that moves joints your reference sets to zero/nominal
- an imitation or deviation penalty coexists with a new tracking reward
- considering releasing joints from a shaping term mid-lineage
“前进步态也不是 PPO 自己发现的，是 joint_pos_ref 教出来的（v6 砍半塑形 → 抬脚 35 mm 塌到 4 mm…）。而 ref_joint_offset 原本只写 6 个矢状面关节，roll/yaw 参考恒 0 —— 侧走既没被教，roll 一偏离 nominal 反被 joint_pos_ref 扣分。一边悬赏一边罚过程。”
train/C_LADDER_RUN.md § 3e. 为什么首战 FAIL / 3i. 解锁笼子 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 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 Close a question with an audit, then freeze the wording - later symptoms may not reopen it without new hard evidence
frozen-verdicts-semantic-boundariesWhen an audit closes a hardware-vs-policy question, record the closing evidence, freeze a citable wording for future recurrences, and set the reopening bar explicitly; separate robustness perturbations from plant-truth questions so a DR rung's failure can never silently reopen a closed measurement.
Symptom
Recurring directional bias on the robot kept re-suggesting "maybe the hardware/COM/mechanics are asymmetric", threatening to re-litigate questions that audits had already closed - burning attention each time a descendant policy leaned or drifted.
Context
Two boundary decisions were written as permanent: (1) semantic separation - "S2④ COM ±20mm = 纯鲁棒性扰动,不再承担「解释真机后仰」任务" - if the COM-DR rung degrades, the ONLY allowed conclusion is "policy insufficiently robust to COM uncertainty"; reopening "is the CAD COM wrong" is forbidden because the mass audit was completed and closed (@63f9212). (2) a frozen wording for chirality, to be quoted verbatim whenever left/right bias appears in later rungs: observed directional bias = policy-level spontaneous symmetry breaking; plant asymmetry = no supporting evidence after the mass + model symmetry audit; mitigation candidate pi_sym queued, not blocking. The evidential basis was quantitative: the root policy was perfectly symmetric under +/-6 N*s pushes (40/40) while descendants broke (17/40, 13/40) - "手性是 S2 训练中获得的, 根没有; 机械侧已双 PASS 关案, 不重开".
Change
Closed questions carry (a) the audit commit that closed them, (b) a frozen citable wording for recurrences, and (c) an explicit evidence bar for reopening ("无新硬证据不得重开").
Outcome
Later chirality observations (C2's 15 pp turn gap, hip_roll drift bias) were handled as policy-lineage properties with policy-side mitigations, without a single hardware re-audit cycle.
Mechanism
Symptom classes recur under different guises; without a frozen verdict each recurrence re-runs the same expensive investigation and risks a different (worse-informed) conclusion. Freezing verdict plus wording converts recurring symptoms into citations, while the evidence bar keeps the closure honest rather than dogmatic - the root/descendant symmetry comparison is what makes "it's the training, not the machine" checkable at any time.
Applies when
- a recurring symptom keeps suggesting an already-audited hardware cause
- writing conclusions for a completed calibration/audit
- a DR rung's degradation invites re-measuring the plant
“若 S2④ 退化,结论只能是「当前 policy 对 COM 不确定性不够鲁棒」,不得重开「CAD COM 是不是错了」… 手性冻结表述 … Plant asymmetry: no supporting evidence after mass + model symmetry audit … 无新硬证据不得重开机械不对称”
train/OMNI_V0_SPEC.md § 4. 语义分界与手性冻结表述(2026-08-07 用户定,永久) Three times a joint-angle stand-in for a foot-level quantity was gamed or lied - the absolute ankle roll sold stance width to buy flat feet, a hip-roll criterion was confounded by 50 deg of yaw, and the joint table said 0.271 m where the feet were 0.159 m apart
joint-space-proxy-for-task-space-quantityExpress foot-level (task-space) goals and acceptance criteria in task space - link attitude and lateral spacing from world poses - never through joint angles that assume other joints are at zero, and measure the task-space value before trusting a joint-space estimate of it.
Symptom
The line's first real-robot get-up (v2_6, 2026-08-11) was "fairly stable", but after standing the feet were too close and the robot slid into the splits and fell several times; later reports added that it also got up through a split posture.
Context
(1) flat_feet penalized sum |q_ankle_roll|, but a flat foot is ankle roll compensating hip roll; the proxy taxed the compensated wide solution, and the untaxed combination was hips straight plus ankles at zero - flat and narrow. On hardware the lateral support shrank to hip width plus centimetres, lateral balance rested on 11 N*m ankle motors, and the feet slid apart. (2) The next rung's acceptance criterion "hip roll >= 20 deg" assumed zero hip yaw; at 50 deg of yaw the lateral contribution is x cos 50 ~ 0.64 - the same substitution again, inside a criterion. (3) A new task-space diagnostic reading the foot links' world poses measured v2_6c's stance at 0.159 m where the kinematic audit from joint angles had said 0.271 m (0.271 x cos 47 ~ 0.17).
Change
Rule written into the spec: task-space quantities are never expressed through joint-space proxies. V3.1's stance terms were all task-space: flat_feet_task from the foot links' world orientation, lateral foot spacing in metres, stand_pose stripped of both roll joints.
Outcome
From scratch with task-space terms (V3.1 P1c): lateral stance 0.355 m, foot residual tilt median 0 deg / P75 2.0 deg, all six criteria passing, mu 1.0-0.4 all 100%.
Mechanism
A joint proxy bundles the goal with everything else those joints do; the optimizer finds the combination the proxy does not tax, and a joint-based criterion silently assumes the other joints sit at their nominal.
Applies when
- rewarding flat feet, stance width, foot placement or end-effector pose
- an acceptance criterion is written in joint angles for a geometric goal
- joints with large yaw or coupled axes are involved
“**病根 = 关节空间代理**:`flat_feet` 罚 Σ|q_ankle_roll|(§41 取的简易口径)。 "脚掌平"的运动学正解是 **踝滚补偿髋滚**(q_ankle_roll ≈ −q_hip_roll) … 代理把"脚平"和"站距"绑死在一起卖了。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §43 真机首试入账(2026-08-11)判读:flat_feet 的代理口径错误 Diagnose a behavior failure by enumerating hypotheses and auditing each against the actual config, cheapest first
hypothesis-table-code-auditBefore changing anything, write the full hypothesis list for the symptom and audit each against the resolved config and measured magnitudes, cheapest check first; train only on the survivors.
Symptom
Real robot leaned forward "wanting to walk" but dragged its feet instead of lifting them - a symptom with many plausible causes and no obvious single fix.
Context
Seven hypotheses were listed and each checked against the actual training config files (velocity_env_cfg.py, isaac_values.py), ordered by check cost: missing foot clearance term (CONFIRMED, primary - feet_air_time existed but no swing-height term at all); energy penalties dominating (REJECTED - energy terms total -0.19 vs tracking +1.2, 16%); command range too narrow (CONFIRMED - (0.15,0.35)); nominal pose too crouched / action scale too small (HALF - knee 0.5 rad = 28.6 deg deep, scale fine); mixed PD across motor types (REJECTED - already grouped); missing base-height reward (REJECTED - present at -5.0); height-drop termination (REJECTED - none exists, which itself became finding #4 of the fix list).
Change
The audit produced a ranked fix list (add clearance penalty; widen speed range; reduce nominal crouch) with each rejected hypothesis documented so it would not be re-litigated.
Outcome
Three confirmed causes fixed over v5/v6: swing height went 22-23 mm -> 34 mm, tracking 81% -> 87%; the rejected hypotheses stayed rejected (no wasted rungs on energy weights or PD grouping).
Mechanism
Multi-cause symptoms invite guess-and-train loops; a written hypothesis table forces each candidate to be confirmed or rejected against actual values (not impressions), and cost-ordering the checks means most hypotheses die for the price of reading a config.
Applies when
- a real or sim behavior failure has multiple plausible causes
- the team is about to "try a fix" without an audit
- post-mortems keep re-proposing already-rejected causes
“真机现象:躯干前倾像要走,脚抬不起来(拖着蹭)。按成本从低到高逐条核查 … | 1 | 缺 foot clearance | ✅ 成立,首要 | 有 feet_air_time,无任何摆动足高度项 | | 2 | 能量惩罚压过跟踪 | ❌ 不成立 | 能量类合计 −0.19,跟踪 +1.2,只占 16% |”
train/WALK_DIAGNOSIS.md § walk 拖地问题 — 七条假设的代码核查结果 Before training a one-leg stand, the accounts and a probe showed the default gains could not hold it at all - kp 20 needs 0.39 rad of error to carry the static roll moment, more than the whole adduction range - so per-joint gains came first, and thermal limits set the session length
single-support-gain-authority-probeBefore training a posture that loads one joint statically, compute the steady tracking error load/kp and the series stiffness against m*g*h, and prove with a simple hand-written controller that the posture can be held under the deployment gains - change the gains first if it cannot; then size session length from the thermal account.
Symptom
The one-leg line (standing on one foot, the other folded back, no hopping) had to decide whether the existing gain profile could hold single support before any reward was designed.
Context
Hardware accounts (9.792 kg, COM 0.234 m high, 170 x 80 mm feet, legs 80% of the mass): moving the COM over one foot needs 107 mm of shift and the 20 deg hip-roll adduction range gives 131 mm - geometrically enough. The static frontal moment is 7.8-9 N*m, within RS02's 17 N*m - torque is enough. But at kp 20 carrying 7.8 N*m needs 0.39 rad of tracking error, more than the entire adduction range, and the real robot had already shown it: commanded +0.17, actual -0.04 (0.21 rad droop) under load, 0.0008 rad hanging - load, not the motor. A probe (probe_oneleg.py) then showed open-loop PD cannot hold single support on physics grounds, so the criterion became "an equilibrium exists and a hand-written 4-gain COM feedback can hold it": single-support roll stiffness is hip and ankle in series and must exceed m*g*h_com = 22.5 N*m/rad; ankle kp 12 in series with hip kp 80 gives only 10.4 (open loop 16/16 fell), ankle 60 with hip 80 gives 34.3 (52% margin).
Change
A per-joint gain profile (rl_oneleg: hip_roll kp 80, ankle_roll kp 60, the rest as rl_default) - which needed per-joint gain support in robot.yaml, the bridge, deploy and the trainer's actuator groups - decided before training. Thermal account: single support makes hip_roll the dominant heat load (about 7.8 N*m against a 7 N*m continuous rating), so acceptance and demos run in segments of at most 60 s with a temperature check.
Outcome
Under rl_oneleg the hand-written feedback held six cells cleanly for 6 s (hip_roll steady torque 2.1-3.4 N*m, half the thermal budget); under rl_default the same feedback on the same cells fell 0/4. The trained V0 policy then passed its 40-cell acceptance.
Mechanism
With PD position control, the steady error needed to carry a static load is load/kp; when that error exceeds the joint's range the posture is unreachable whatever the policy does, and series compliance between joints lowers the effective stiffness below the gravity stiffness that single support demands.
Applies when
- single-support, crouched or one-arm-load postures on PD actuators
- a joint "droops" under load on hardware but tracks well when hanging
- deciding whether a new skill needs its own gain profile
“但 kp=20 时撑住 7.8 N·m 需要 **0.39 rad 跟踪误差 > 整个内收行程**。真机已实测: 命令 +0.17 实际 −0.04（droop 0.21 rad），悬挂时 0.0008 rad——是负载不是电机。 … 单支撑滚转是 hip/ankle **串联**刚度，必须 > m·g·h_com = 22.5 N·m/rad；ankle kp12 串 hip80 只有 10.4（开环 16/16 全摔），60 串 80 = 34.3（裕 52%） … **rl_default 同反馈同格 0/4 全摔**（增益档必要性对照）”
git:Lucen V2@origin/oneleg-line:train/ONELEG_V0_SPEC.md § §1-1 单脚站: 几何可行，卡点是 hip_roll 增益权限 / §2 A 线增益 / §5 probe 定谳 Before resuming a checkpoint, diff the current cfg against what the checkpoint was trained with
resume-state-dr-audit"One variable per rung" counts variables against what the checkpoint actually experienced: audit the checkpoint's logged training config and align every unintended difference before resuming.
Symptom
Two consecutive rungs (C1 back-mode, C2' forward-turn) failed from the same root with the same full-regression signature despite adding different new modes - so the mode was not the cause.
Context
Both runs resumed s1e-500 with the then-current cfg, which carried PD band (0.8,1.2) plus three DR events (base_com, joint_friction, push_robot) accumulated by later lineages. Verified on the training machine from the source of truth (the run's logged params/env.yaml): s1e-500's actual training state was PD +/-10% (0.9,1.1) and all three DR events None. Resuming it under the new cfg meant eating 4 new plant variables plus a new mode at once - the intended "1 variable" was actually 5. A worse variant (c1_redo from s2e_pd-1400) added push +/-0.3 to a root that had never seen it: near-total collapse within +100 iters.
Change
C2 aligned the cfg to the checkpoint's training state before resuming (PD back to (0.9,1.1), three DR events off) - making the new mode the only true variable. Permanent rule recorded: compare the checkpoint's training-time DR with the current cfg before any resume.
Outcome
C2 trained successfully from the same root that had "failed" twice (wz 20/20 with genuine sign-antisymmetric response by iter 700-800); the A/B falsification ("两个不同模式同签名崩") plus the env.yaml verification closed the attribution.
Mechanism
A resumed policy is instantly evaluated (and its value function trained) under whatever plant distribution the cfg specifies; every DR term the checkpoint never adapted to is a distribution shift applied on day one, compounding with the intended change. Single-variable discipline is therefore a property of (cfg diff) x (checkpoint history), not of the cfg diff alone.
Applies when
- resuming or forking any checkpoint under an evolved config
- a resumed run degrades broadly within the first few hundred iterations
- two different changes from the same root fail with the same signature
“A/B 定谳：两个不同模式同签名崩 → 病因不是模式，是「从 s1e-500 续训」。… s1e-500 训练态 = kp/kd ±10% (0.9,1.1)，base_com / joint_friction / push_robot 全 None；而 cfg 里带着 (0.8,1.2) + … 三个 DR —— 从它续训等于一次吃 4 个新 plant 变量 + 新模式 … 永久教训：续训前必须比对 checkpoint 的训练态 DR 与现行 cfg。单变量纪律不只看「我改了什么」，还要看「checkpoint 见过什么」。”
train/C_LADDER_RUN.md § 3b. 这不是重复实验 —— 前两次的病根已定位并修掉 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 The +/-50 mm lateral COM randomization meant to spread the legs coincided with legs pulling IN - rolled back per its own pre-registered contract
com-dr-rollback-on-symptomWhen adopting a DR value that covers no local measurement, write its intent and rollback trigger into the config at adoption time; roll it back as the control arm the moment the symptom contradicts the intent, and promote the symptom's metric into the acceptance battery.
Symptom
After v7 adopted the reference developer's oversized lateral COM randomization (+/-50 mm) explicitly to force leg spread, the real robot's legs narrowed instead - lateral mean 154 mm / closest 107 mm in sim (nominal 214.5), narrower still on hardware with occasional leg contact.
Context
The rollback was clean because the adoption had been honest: the robot.yaml comment recorded the intent AND that the +/-50 value covered no local measurement (only a 16/7 mm measured offset existed; even the prior widening to +/-20 was subjective), plus the reference's own reported side effect (base sway) and the note "这一项要单独跑、 单独归因". When the opposite symptom appeared, v8 returned y to +/-20 mm as the control arm ("要么没起作用、要么帮了倒忙 … 按约定退回做 对照"), kept x/z untouched (a noise-level difference not worth another variable), and named the second suspect: the landing penalty itself, via the reference's own three-link chain (landing penalty -> stance narrows -> spacing penalty needed). A gate lesson was booked in the same table: v7's sim numbers had ALREADY crossed the line (154/107 vs v5's 182/147) - "这个指标本可拦下 v7" - so foot-distance became a standing acceptance row (min >120 mm, zero leg-leg contacts).
Change
base_com_offset_m y: 0.050 -> 0.020 (x/z kept), regenerated through the export tool rather than hand-editing derived files; foot-distance acceptance row added.
Outcome
A borrowed DR lever with no local measurement basis was retired the moment its symptom contradicted its purpose, at single-variable cost; the metric that would have caught it pre-hardware entered the gate.
Mechanism
DR ranges shape behavior through the policy's robustness strategy, which is jointly determined with every reward term; a lever that forces stance width on one robot can be dominated by a stronger narrowing pressure (landing softness) on another. Levers adopted without local measurement must carry their own rollback trigger, because there is no nominal to argue from when they misbehave.
Conflicts
Causality is not fully closed in the source: the narrowing may come from the landing penalty rather than the COM lever ("腿距的第二嫌疑人是 ④ 本身"); the rollback is the pre-agreed control experiment, not a verdict that the lever caused the narrowing.
Applies when
- importing DR ranges or behavioral-forcing randomizations from references
- a DR lever's observed effect contradicts its documented purpose
- a sim metric existed that would have caught a shipped regression
“⑥ 的本意 … 是逼策略把脚分开;真机结果是脚向内收且偶发相碰——要么没起作用、要么帮了倒忙。… 注释当时就写了"这一项要单独跑、单独归因"。现在症状出现了,按约定退回做对照。”
train/WALK_V8_SPEC.md § 3. 改动 C — 质心随机化退回(撤销 v7-⑥ 的 y 项) The smoke watcher panicked at the wrong operating point and its score goes blind when gates saturate - treat it as a survival sentinel, not a judge
smoke-watcher-operating-pointConfigure every automated evaluator at the lineage's declared deployment operating point, give it graded metrics that cannot saturate, and until then scope its authority to catastrophe-detection - never let a mis-configured watcher stop or rank a rung on its own.
Symptom
Two watcher misfires in one ladder: (1) during the friction rung the watcher (evaluating at kd 1.0) reported panic-level 1/3 survival from iter 3300 - falsified by the official kd 1.2 scan, because the lineage's design operating point was kd 1.2 and the watcher lacked the --kd-scale passthrough; (2) during the PD rung the watcher's early-stop score froze at iter 1050 despite ongoing drift improvements, because with all eight gates passing (constant 0/3 failures) the score has no gradient left - "八门全过恒 0/3 时 score 对漂移改善盲, s1f 课文三现".
Context
Both are the same category: the in-training smoke loop is an instrument with its own configuration (operating point, score design), and its verdicts are only as aligned as that configuration. The booked doctrine: "冒烟只当存活哨兵" - until the watcher evaluates at the deployment operating point with graded metrics, its role is detecting catastrophes, not ranking checkpoints; ranking belongs to the full battery at the design operating point (and the watcher's scoring was separately patched to weight survival 3x so recoveries during hard phases are not early-stopped away).
Change
Watcher debt booked (--kd-scale passthrough); score saturation acknowledged with graded columns planned; selection authority kept with 20-seed batteries at the declared operating point.
Outcome
A false panic did not abort a rung that was actually passing at its design point; a frozen score did not hide real drift gains; the instrument's authority was scoped to what its configuration can actually see.
Mechanism
An evaluator is itself configured (gain profile, delay, metrics); evaluating a policy away from its design operating point measures a counterfactual robot, and bounded scores saturate once binary gates pass, losing all sensitivity. Instruments need the same operating-point discipline as deployments and graded outputs to retain gradient.
Applies when
- an automated smoke loop contradicts the official battery
- early-stop scores freeze while graded metrics still improve
- lineages with non-default deployment gain/delay profiles
“watcher (kd1.0 口径) 3300 起 1/3 恐慌被 kd1.2 正式扫描证伪为考纲外假象 —— 工作点评测口径教训: watch_ckpt 缺 --kd-scale 透传 (待补), 冒烟只当存活哨兵。… watcher score 饱和误停 @1050(八门全过恒 0/3 时 score 对漂移改善盲, s1f 课文三现)”
train/README.md § omni_s2e_fric (watcher 恐慌被证伪) / omni_s2e_pd (500 臂) The hip_roll (l+r) asymmetry scalar predicted real-robot lateral drift - promote validated sim scalars into the gate
hip-roll-sum-predicts-lateral-driftHunt for cheap sim scalars that predict real-robot behaviors, validate them on direction AND ordering across multiple policies, then promote them into the acceptance battery; treat later violations as debt to justify in writing, not noise to ignore.
Symptom
A persistent hip_roll left/right asymmetry row in the sim2sim symmetry table had been dismissed as "calibration or mechanical asymmetry" noise; meanwhile real deployments drifted sideways by policy-dependent amounts.
Context
Forward-kinematics analysis reframed the scalar: both hip_rolls move the feet in +y for positive angle, so a same-signed (l+r) sum IS a lateral translation mode - the scalar is a direct lateral-drift bias estimate. Checked against real deployments: s1e (l+r = -0.0178, smallest magnitude) was the steadiest with least drift; 700 (+0.0253) drifted mildly left; A800 (+0.0267) drifted clearly left with the largest tilt 12.9 deg. Direction correct 3/3, ordering correct 3/3 (the log's heading calls it "四枚四中", four-for-four).
Change
The scalar was promoted into the acceptance battery as a posture-class criterion alongside tilt-max median: "hip_roll 左右不对称 |l+r| 不得比父代大" - doubling as a heat proxy (error ~ torque ~ heating).
Outcome
Used at every later gate; when the C4 product exceeded it by +0.005 rad (~+0.3 deg vs parent), the criterion was not silently waived - it was booked as explicit debt with a mechanism argument (the increment is task-required, far smaller than the sidewalk amplitude +/-2.2 deg) plus a related account (stand saturation 32.4% -> 37.2%).
Mechanism
A policy's static joint-angle bias in a translation-producing mode integrates into real-world drift; sim can measure that bias precisely and cheaply. A sim scalar earns gate status exactly when its predictions are validated against hardware in both direction and ordering - and a validated gate may only be exceeded with a written mechanism-level justification, never silently.
Conflicts
The log's heading says "四枚四中" (4/4) but the evidence table lists three policies and the text says "方向 3/3、排序 3/3"; the fourth instance is not shown in this file.
Applies when
- a real robot drifts or leans in a policy-dependent way
- deciding which sim measurements deserve gate status
- a validated gate criterion is marginally exceeded by a new product
“s1e | −0.0178（绝对值最小）| 微右、最不飘 | 三者中最稳、飘最小 ✓ … A800 | +0.0267 | 左、最飘 | 明显左飘、倾角最大 12.9° ✓ 方向 3/3、排序 3/3。 → 正式纳入验收表（与「倾角 max 中位」并列为姿态类判据）。”
train/C_LADDER_RUN.md § 3e. 顺带：hip_roll 左右不对称 (l+r) 就是横移偏置 —— 四枚四中 Fall recovery was defined as the whole chain - any fallen pose, a stable stand, a clean hand-back to walking - and built as a second policy behind a deploy-side switch, not folded into the walking PPO
recovery-two-policies-and-a-state-machineDefine a recovery skill by the whole chain it must complete, including the hand-back to the next controller; if it is built as a separate policy, make the switching logic and its handoff contract a deliverable of their own, and keep the recovery observation contract a subset of the locomotion one so a unified policy stays possible later.
Symptom
A walking robot that falls needs a human to stand it back up. The design question on 2026-08-09 was whether to teach getting up inside the existing omni walking policy or beside it.
Context
The user set the goal as "any fallen pose -> stand up alone -> stand stably", and the spec named the real difficulty as the full chain fall -> recovery -> stable stand -> correctly initialised walking history and clock -> walking, making the deploy state machine a first-class deliverable. A unified single policy had a real-robot precedent (arXiv:2605.18611, a state-dependent gate near 37 deg tilt) but was deferred until a recovery policy and an omni policy were each reliable. The line ran on its own branch and worktree with every walk/stand/omni/run config path untouched. The development path copied the G1 learned get-up logic (arXiv:2502.12152): first find any feasible get-up (ugly accepted), then add smoothing, torque and real-robot constraints. The recovery contract kept the base 45-dim observation (command slice held at 0, no gait phase, no frame history - their reasons do not apply to a skill without a clock or a velocity task), so it stays a prefix of the 215-dim omni contract and a later merge is not foreclosed.
Change
Two policies and a deploy-side switch instead of one retrained walking policy; recovery got its own minimal contract (45 dims, full-range action, later the beta-anchored profile) and its own acceptance battery.
Outcome
The split held for the whole line: on 08-14 deploy_policy gained a second (PolicyIO, ONNX) pair behind --recovery-policy, each loaded under its own manifest contract, and the runbook runs stand_v1b or omni_c4_ff800 as the locomotion side with recovery_v3_1p1c. The literature scan of 08-10 found that every verified get-up implementation deploys one end-to-end policy (or softly gated experts) and stages only on the training side - so the runtime state machine here is the walk/recovery switch, not a staged get-up.
Mechanism
A separate policy keeps each reward table single-purpose and lets a proven walking lineage stay byte-frozen; the cost moves to the handoff, where every piece of state one policy leaves behind (history, clock, last action, command) must be reset for the other.
Applies when
- adding fall recovery or get-up to a robot that already walks
- choosing between one unified policy and a switched pair of policies
- designing the observation/action contract of a secondary skill
“先做 recovery policy + omni policy 两个策略,部署侧状态机切换;不把 recovery 硬塞进现有 omni PPO。 … 任务定义:**任意跌倒姿态 → 自己站起来 → 稳定站立**。真正的难点不只是"起身", … omni walk**(§6 部署状态机是本 spec 的一等公民,不是附录)”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §0 目标口径与架构判决(用户 2026-08-09 定) The deploy-side walk/recovery switch - into recovery at tilt > 65 deg held 0.3 s, back at tilt < 15 deg with angular rate < 1 rad/s and straight knees held 1 s, a 15 s timeout, last action cleared both ways - and the handoff steps the design required are only partly implemented
walk-recovery-fsm-handoffSpecify a deploy-time controller switch as hysteretic, time-filtered predicates the robot can measure (proxy what it cannot, e.g. straight knees for height), a timeout that ends in a safe stop, and a complete handoff (history, clock, last action, command ramp) - then test that the code performs every handoff step, because the design document is not the implementation.
Symptom
With a recovery policy and a locomotion policy as separate networks, the robot needs a switch: when is it "fallen", when is it "up", and what state must be reset so the next policy does not act on the previous one's history.
Context
The 08-09 design: enter recovery when fallen (tilt > 55 deg or height < 0.60 x 0.384 m) for 150 ms, leave for a stand-hold when upright (tilt < 12 deg, height > 0.85 x 0.384 m, feet steady, |omega| < 0.8) for 400 ms - wide entry, strict exit, hysteresis - then a mandatory handoff trio before walking resumes (reset the walking policy's observation history, restart its phase clock at 0, clear its previous action and latency buffer) and a command ramp instead of a jump. The 08-14 implementation in deploy_policy (--recovery-policy): each policy under its own manifest contract (walk: nominal + scale; recovery: beta-anchored), the same rl_default gains, the tilt cutoff disabled; RECOVERY at tilt > 65 deg for 0.3 s; LOCO again at tilt < 15 deg and |omega| < 1 and knees straight (< 0.35 rad) for 1 s - the robot's computer has no height estimate, and straight knees stand in for height so a V3.0-style upright kneel cannot pass as standing; RECOVERY longer than 15 s ends in a safe stop; last_action cleared on both switches; command forced to 0 during RECOVERY; power scaling applies to LOCO only.
Change
An open account was written down with it: the recovery end state (0.355 m stance, hip yaw -/+27 deg) is outside the walking policy's training start distribution, so the first test must use stand / zero command as LOCO, and the long-term fix is to widen the walking policy's initial states rather than bend recovery's stance to suit walking.
Outcome
The spec records only a successful compile (py_compile); the hang test was left to be done on site. The operator runbook carries the three-step procedure (hang with stand as LOCO, mat and push, then omni walk as LOCO) but no outcome.
Mechanism
Two policies trained separately each assume their own history, clock and last action; a switch that carries any of them across feeds the next policy a state it never saw - the design called this "the walking policy seeing a ghost history".
Conflicts
The 08-09 design requires resetting history, clock and previous action plus a command ramp; the 08-14 implementation records last_action clearing and a zero command during recovery; the one-leg spec of 2026-09-14 lists the handoff hygiene as specified but not implemented - reset_history() is called by nothing (a 215-dim policy would carry four frames of pre-fall history), the phase clock is not zeroed, and there is no command ramp back to LOCO. No hardware run of the FSM is recorded in either source.
Applies when
- switching between separately trained policies on hardware
- a policy with history or phase observations is re-enabled mid-run
- the robot lacks a sensor the switching criterion was designed around
“**判据**:进 RECOVERY = 倾角 >65°(`--fall-tilt-deg`)持续 0.3 s;回 LOCO = §5 真机可测子集:倾角 <15° ∧ |ω|<1 ∧ **膝直 <0.35 rad(NX 无高度观测, 高度门用膝直代理 —— 防 V3.0 型"跪坐但直立"误判)** 持续 1 s (`--recover-hold`);RECOVERY 单次 >15 s(`--recovery-max-time`)安全停。 … **切换卫生**:两向切换 last_action 清零;RECOVERY 态 cmd 强制 0”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §50 FSM 双策略调度(2026-08-14,用户令):deploy_policy --recovery-policy A suspended (no-load) test acquits or convicts the actuator before you blame authority
suspended-test-isolates-actuator-authorityBefore attributing a failure to actuator authority, measure no-load tracking error and steady-state torque fraction; blame authority only if the task fails while the error grows with demanded force - and then fix gains or targets, not training.
Symptom
hip_roll sagged 0.21 rad on the ground and saturation questions loomed over the sidewalk plan - was the roll axis physically too weak (authority), or was something else limiting it?
Context
Before C4, the roll-authority question was settled by measurement triage: suspended test (--suspend, feet off ground) showed hip_roll tracking error 0.0008 rad - actuator acquitted; the entire 0.21 rad ground sag is load-induced. Steady-state torque was 25% of limit - 75% margin remains. Since sidewalk needs lateral force, not exact angles, authority was ruled "not a hard limit", with a pre-registered criterion for when it WOULD become one: sidewalk fails to track AND roll error keeps growing - then the fix is raising hip_roll kp or lowering the vy target, not more training.
Change
Hypothesis "roll authority insufficient" demoted from blocker to a monitored branch with an explicit trigger condition; C4 proceeded.
Outcome
Later open-loop probes confirmed the actuator could produce the behavior (sidewalk feed-forward ran at full amplitude, 5/5 survival), and the eventual C4 failure causes were measurement and reward, never authority.
Mechanism
Suspended vs loaded comparison separates the actuator's closed-loop competence from the load path: tiny no-load tracking error means the motor/controller is fine and any loaded deviation is statics (gravity / stiffness budget, kp trading error for force). Torque-fraction measurement then bounds how much force headroom actually remains.
Applies when
- suspecting an axis is "too weak" for a new skill
- large position sag on a loaded joint
- deciding between hardware fix, gain change, and more training
“吊挂（--suspend）实测 hip_roll 跟踪误差 0.0008 rad → 执行器无罪，地面下垂 0.21 rad 全是负载所致；稳态占限扭 25% → 仍有 75% 扭矩余量。… 判据：若 C4 出现「侧走跟不动且 roll 误差继续变大」，那才是权限账 … 解法是提 hip_roll 的 kp 或降 vy 目标，不是硬训。”
train/C_LADDER_RUN.md § 3d. roll 权限：已部分澄清，不是硬上限 When training fails repeatedly, inject the target behavior open-loop - stop tuning rewards for an unverified behavior
open-loop-probe-before-reward-tuningAfter two failed training attempts at a skill, stop training: demonstrate the behavior open-loop on the real plant/sim first, and only resume training once you hold a measured, safe, sign-verified target trajectory.
Symptom
Three sidewalk training rounds failed; hypotheses multiplied (exploration failure / wrong reference waveform / insufficient authority) with no way to pick between them by running more training.
Context
Instead of a fourth reward guess, the team wrote probe_side_ref.py: the candidate reference is injected open-loop on top of a frozen policy's output (bypassing PPO entirely), directly measuring "what happens if the robot literally does this waveform" - separating all three hypotheses in one experiment (5 seeds x 8 s per condition, several waveform families and gains). The probe immediately eliminated the authority hypothesis (full-amplitude execution, 5/5 survival) and localized the problem to the waveform/measurement side. The closing principle was written down after the saga: without a verified target behavior, tuning rewards is "在黑暗里试钥匙" (trying keys in the dark).
Change
Standing method: before opening another training rung on a failing skill, build an open-loop (or task-space) generator of the intended behavior, measure whether the physical system can express it and what it looks like - then train toward a verified, quantified target.
Outcome
The probe chain produced the verified waveform (reversed-sign triangle, half gain), quantified safe amplitude (tilt 8.2 deg at band top, foot distance clear of the wall), exposed the metric bug when probe and training disagreed, and ultimately supplied the feed-forward that made C4 pass in +100 iters.
Mechanism
Training couples exploration, reward design, and feasibility into one opaque outcome; open-loop injection cuts the loop and tests feasibility and waveform alone. A behavior demonstrated open-loop converts the remaining failure into a pure credit-assignment/reward question - and its measured trajectory becomes the reference itself.
Applies when
- repeated training failures on one skill with multiple live hypotheses
- uncertainty whether the platform can physically express the behavior
- a reference trajectory's shape/sign/amplitude is guessed, not measured
“三轮 FAIL 之后不再猜，写 train/probe_side_ref.py 把参考开环注入到策略输出之上（绕过 PPO），直接量「照这个波形做会怎样」，一次分开三个假说：甲 探索 / 乙 波形 / 丙 权限。”
train/C_LADDER_RUN.md § 3f. C4 真因定谳（开环探针） / 3k. 建议的下一步 Under continuous 3-axis uniform sampling, pure straight-line walking is a zero-measure event the policy never trained
zero-measure-commands-need-mode-samplingEnumerate the exact command points users will actually issue (straight, stop, in-place turn) and give each explicit probability mass via mode sampling with off-axes pinned to zero - never assume a continuous sampler covers its measure-zero subsets.
Symptom
"The robot drifts even in sim when told to walk straight" persisted across reward tunings - because with commands drawn as vx in [0.15,0.5] x vy ~ U(+/-0.2) x wz ~ U(+/-0.6), the event vy=0 AND wz=0 has probability zero: pure straight-line walking was never sampled even once.
Context
Restart evidence item #3: "纯直行是零测度点 … 'sim 里直行就漂'是分布的 必然,不是 reward 没调好" - the drift metric was legitimately drowned by commanded turning (v11's own comment self-documented this). The structural fix is discrete mode sampling: a custom ModeVelocityCommand that first draws a mode by share (stand/forward/back/turn/side/mixed), then draws values only on that mode's axes with all others pinned to exact zero - which is also what preserves single-variable discipline in the C ladder (native 3-axis uniform "采不出'离散模式桶' … 把 C1~C4 的单变量纪律直接毁掉"). The mixed mode later got an ellipsoid constraint rather than a cube for the same reason in reverse - corner combinations of a cube are unrepresentative extremes.
Change
Command generation moved from independent per-axis uniforms to mode-bucket sampling with pinned-zero off-axes (plus 20% rel_standing); acceptance likewise evaluates per mode.
Outcome
Straight-line behavior became a trained, testable mode instead of a measure-zero hope; the C ladder could add one mode per rung with provable isolation.
Mechanism
A policy optimizes expected reward under the command distribution; events of probability zero contribute nothing to the objective, so exact-zero-command behaviors (straight walk, stand, in-place turn) are only learned if the sampler gives them mass. Product-of-uniforms distributions concentrate mass on mixtures and give none to the pure behaviors users actually command.
Applies when
- a "simple" command (straight, stop) underperforms mixtures in sim
- designing command distributions for velocity-tracking tasks
- a ladder needs per-mode isolation for attribution
“纯直行是零测度点:最终 command 为 vx∈[0.15,0.5] × vy∈U(±0.2) × wz∈U(±0.6) 连续均匀,vy=0∧wz=0 从未被专门采样 —— "sim 里直行就漂"是分布的必然,不是 reward 没调好 … Isaac 原生 UniformVelocityCommand 是三轴各自 uniform,采不出"离散模式桶"”
train/OMNI_V0_SPEC.md § 0. 为什么从零 (3) / 三件前置 (1) A proposal in the runbook - torque and action limits as versioned safety tiers (classroom / research / expert) written to motor RAM and read back, separate from the reward's effort penalty - recorded as a proposal, its implementation unrecorded
safety-limits-are-a-layer-not-a-rewardKeep hardware limits as an explicit, versioned safety layer (tiers written and read back at start, the persisted default the safest one) and the effort penalty as a behaviour layer; when a skill needs more torque, change tier deliberately rather than trading one layer against the other.
Symptom
Running and jumping need more torque than the deployed limits allow, and the temptation is to trade the training-side effort penalty against the hardware limit, or to hand a new user a robot "tuned however the last person left it".
Context
A message pasted into the operator runbook (undated, citing Berkeley's practice of storing the full motor configuration as JSON with write and read-back scripts) proposes: configuration is a versioned artifact, not a verbal agreement; three safety tiers in robot.yaml beside the gain and policy profiles - classroom (RS06 limited to 10 N*m, lateral joints clamped: "however bad the policy, it only moves awkwardly"), research (14 N*m, clamps at twice the measured need, the default) and expert (the 36 N*m rating, joint limits only, requiring an explicit flag); deploy writes the tier to motor RAM at start and reads it back, while the stored copy stays classroom so a power cut returns to the safest state. It frames limits as the safety layer and the effort penalty as the behaviour layer - more torque for running means switching tier, not weakening the penalty.
Change
None recorded: the message ends by asking which to do first, a rollback or the tiers.
Outcome
The sources do not record the tiers being implemented; the deployed limits stayed at 12/17/11 N*m through the recovery and one-leg lines (the one-leg spec treats raising the RS06 limit as a separate, unapproved hardware decision). Related and recorded elsewhere: torque limits were written to RAM only in a scripted, self-reversing field experiment.
Mechanism
Hardware limits bound the damage any policy can do; reward terms shape what a policy prefers. Mixing them either weakens safety to buy behaviour or distorts behaviour to buy safety.
Applies when
- a new skill needs more torque than the deployed limits
- robots are handed to students or new users
- motor configuration lives in people's heads or in the firmware only
“配置是版本化的产物,不是口头约定。 … deploy_policy 启动时按档写进电机 RAM 并读回校验(落盘的那份永远保持 classroom,断电自动回到最安全状态)。 … 限幅是安全层,dof_torques_l2 是行为塑造层,它们在不同的层,不冲突。跑步要更大力矩就换档,而不是去动训练里的省力惩罚。”
RL系统/FOLLOW THIS copy 2.md § 面向 developer / 教育机构该怎么做 (pasted proposal, undated) A frame-history observation under zero DR memorizes the trainer's plant fingerprint - the estimator must see variation to learn estimation
history-obs-needs-plant-variationIf the observation carries history (stacked frames, RNN), keep at least minimal plant variation (gain/latency jitter) on from the first iteration - "nominal first, robust later" is structurally invalid for estimator-bearing contracts.
Symptom
omni_s1 (fresh 215-dim contract with a 5-frame history window, trained with DR fully off): training all green, yet the MuJoCo gate scored 0/20 on all eight doors - falls within 2 s, seven checkpoints, not one transferred.
Context
The history window exists precisely to let the actor implicitly estimate line velocity and actuator dynamics (the actor is denied base_lin_vel by observation honesty). Under a constant plant that implicit estimator has nothing to estimate - it learns the trainer's exact response fingerprint instead, and any other simulator's micro-differences are out-of-distribution: "5 帧窗按设计就是隐式估计器, plant 恒定时它学到 Isaac 精确响应的指纹". The planned "nominal-first-robust-later" staging was declared STRUCTURALLY incompatible with history observations: "估计器要见过变化才学估计, 否则学背诵" (an estimator must see variation to learn estimation, otherwise it learns recitation). Honest confound note kept: this is mixed with "zero DR does not transfer, period" - but both attributions prescribe the same fix, so no control was run.
Change
S1.1: minimum actuator jitter turned on from day one - kp/kd +/-10%, latency 0-1 frame (friction/COM/mass still nominal, no push - those stay for the S2 ladder).
Outcome
Transfer restored: survival 0/20 -> 20/20, speed 19/20, foot distance 20/20 (remaining failures moved to gait quality, a different disease); the staging doctrine was amended - history-carrying contracts never train under a frozen plant.
Mechanism
A recurrent/history channel fits whatever temporal structure minimizes loss; with a deterministic plant the cheapest structure is the plant's own impulse-response signature, yielding features that are simulator-specific rather than physics-general. Plant variation forces the channel to carry state-estimation features that transfer.
Conflicts
Attribution is explicitly confounded with the simpler "zero DR never transfers" reading ("与「零 DR 本身就不迁移」混杂 … 两种归因处方相同, 不做对照") - the source chose not to spend a control run separating them.
Applies when
- adding frame stacking or recurrence to an actor observation
- a nominal-plant policy fails a cross-simulator gate within seconds
- planning DR staging for a new contract
“frame_hist × 零 DR = plant 指纹过拟合——5 帧窗按设计就是隐式估计器, plant 恒定时它学到 Isaac 精确响应的指纹, MuJoCo 的微小差异即 OOD, 2 s 内摔, 七个 checkpoint 无一迁移。「先标称后鲁棒」的分段与历史观测结构性冲突:估计器要见过变化才学估计,否则学背诵。”
train/OMNI_V0_SPEC.md § 3. S1.1 修订记录 ① The recovery line's real-robot verdicts live in three places that disagree - the spec's "fairly stable, first real stand-up" for v2_6, an undated runbook note that only v3_1p1c works, and a first run whose details were never recorded
write-hardware-verdicts-backA hardware verdict is a dated entry in the authoritative ledger - policy file and stamp, gain profile, floor, battery, tries, log file, what was seen - written back the same day; a note in a command file is a pointer, not a verdict, and a newer verdict that contradicts an older one must say so.
Symptom
Asked "which recovery policy works on the real robot", the sources give different answers, and none of them carries the conditions of the test.
Context
08-09: the first real run was stopped as violent and dangerous; which ONNX, which gain profile and whether a log existed were marked "to be recorded" and never were. 08-11: v2_6 was "fairly stable", the line's first real get-up, with splits after standing; v2_5b's result and both CSVs were "to be reported". 08-14: v3_1p1c was stamped and pushed, with "the real first test still needs the user present"; the spec records no hardware result for it. The operator runbook (undated) puts above the v2_6 and v2_5b floor commands the note that none of the recovery policies below work, only recovery_v3_1p1c - a verdict never written back into the spec, with no date, floor, battery, number of tries or log attached.
Change
None recorded in the sources; this card records the gap.
Outcome
The line's authoritative record ends with v3_1p1c as the product awaiting its first real test, while the operator's note implies it is the only one that works and that v2_6 (recorded as a success) does not.
Mechanism
Verdicts given at the robot travel by word of mouth and command-file comments; without a record carrying the conditions, a later reader cannot tell a changed verdict from a changed floor, battery or stack.
Conflicts
§43 (2026-08-11) records v2_6 as the first successful real get-up ("fairly stable"); the undated runbook says every recovery policy except recovery_v3_1p1c does not work; §49 (2026-08-14) says v3_1p1c's first real test was still pending. The runbook's claim has no date and was never written back to the spec, so it cannot be ordered against §43.
Applies when
- choosing which policy to deploy from an operator's notes
- a hardware session ends without a written result
- two documents disagree about what worked on the robot
“下面的recovery都不行 只有recovery_v3_1p1c.onnx”
RL系统/FOLLOW THIS copy 2.md § #### Recovery Policy (operator runbook, undated) Audit rewards by realized contribution (weight x achieved value) - a weight of 2.0 was really paying 0.04
realized-contribution-auditEvaluate a reward table by each term's realized per-step contribution under the current policy, never by its weight column; if a term's realized value is ~0, escalating its weight is a no-op - change the term's structure instead.
Symptom
Foot dragging persisted through repeated weight escalation: feet_air_time had been raised 0.25 -> 1.0 -> 2.0 across versions with no behavioral change, and the training-side comment even recorded the fact ("几乎没有 单支撑相, 是拖着脚蹭") without the fix changing form.
Context
Computing realized per-term contributions in the trained state exposed the economy: feet_air_time contributed weight 2.0 x achieved 0.019 = 0.038 per step, against tracking's +1.20 - lifting the leg earned 3% of what tracking earned, so dragging was the rational optimum no matter the weight escalation. The same table acquitted the energy penalties (total -0.30 negative vs +1.74 positive) that a naive read of weights (-5.0 orientation!) would have blamed.
Change
Fix redirected from "raise the weight again" to "add a term whose realized contribution changes the optimum": a clearance penalty sized so its realized magnitude (~0.018/foot when dragging) is comparable to feet_air_time's, enough to flip the optimum without drowning tracking.
Outcome
With the term economy corrected (plus posture/range fixes), swing height reached 34 mm and tracking 87% by v6; weight escalation of the old term was abandoned.
Mechanism
A reward weight is only a multiplier on whatever the policy currently achieves on that term; when the achieved value is near zero (behavior absent), escalating the weight multiplies near-zero. Optimizer behavior is governed by realized per-step magnitudes, so audits must be conducted in that currency.
Applies when
- a behavior persists despite repeated weight increases
- auditing whether penalties are "too strong" or rewards "too weak"
- sizing a new reward term against existing ones
“把 feet_air_time 权重从 0.25 → 1.0 → 2.0 一路加,但没有加高度项。量级算下来:feet_air_time 权重 2.0 × 实得 0.019 = 0.038,而跟踪奖励是 1.2。抬腿的边际收益只有跟踪的 3%,拖地当然是最优解。… 正项 +1.74,负项 −0.30。能量惩罚不是瓶颈,抬腿没收益才是。”
train/WALK_DIAGNOSIS.md § 决定性证据(#1) / 各项奖励的实际量级 Push DR helped one lineage and hurt another at the same dose - robustness budget is conserved and gets borrowed, not created
push-dr-conditional-budget-conservationBefore opening a disturbance-DR rung, measure whether the untrained policy already meets the spec; if training it anyway, expect the benefit to be conditional on the lineage's existing DR load, grade the intensity, and audit retained margins - budget spent elsewhere will be borrowed back.
Symptom
The push rung's outcome flipped with the lineage: direct +/-0.6 m/s push failed outright on first attempt (base walking collapsed - kd1.2 scan 0/3 from iter 3300, sim2sim self-falls with pushes OFF - no PASS point existed); staged +/-0.3 then gave the narrow-kd single-working-point lineage real gains (push survival 1/5 -> 4/5) while the SAME dose made the dual-working-point balanced-band lineage WORSE (20-seed survival 18 -> 12/20 plus across-the-board push regression).
Context
The four-ladder verdict ("四梯定案", s2e/s2f at both intensities) named the pattern: "push DR 收益条件性" - the benefit is conditional on how much robustness budget the lineage has already spent. The law candidate: "DR 总预算守恒, 平衡带鲁棒性从抗扰余量借" - total DR budget is conserved; a lineage already covering a wide plant band pays for push tolerance out of its disturbance margin. Both S2 ladders therefore closed at the friction rung, with the decisive numerator: untrained push tolerance already met the 4-6 N*s requirement, so the rung was not needed at all ("⑥ push 不训(收益条件性,免训 ±0.6 已达 标)"). The same accounting later justified the C-before-S2 ordering ("push/μ 两轮已实证 DR 预算有限且会被重分配") and trimmed the second S2 pass to three rungs.
Change
Push removed from the standing ladder; graded intensity retained as the method IF a lineage ever needs push training; "does the untrained policy already meet the disturbance spec" instituted as the first check before opening any disturbance rung.
Outcome
Two rungs (push, ground mu) deleted from the second S2 pass on measured grounds; the ladder's real yield was re-stated honestly as precision, not robustness (speed gate 0 -> 20/20, zero-command drift 0.98 -> 0.06 m, but push 159 -> 125/160).
Mechanism
A fixed-capacity policy allocates representation and margin across the training distribution; adding a disturbance axis to a lineage that already spans a wide plant family forces reallocation - the new tolerance is bought with existing margins. Lineages with narrow plant coverage have free budget, so the identical DR dose lands as gain. Benefit is a property of (dose x lineage state), never of the dose alone.
Applies when
- proposing push/perturbation training on a hardened lineage
- the same DR rung helped one lineage and hurt another
- accounting where a ladder's robustness gains actually came from
“push DR 收益条件性 —— s2e⑥a (单工作点血统 kd 窄带) ±0.3 得抗推 1/5→4/5; s2f⑥ (双工作点平衡带血统) 同档反而 20-seed 存活 18→12/20 且抗推全面倒退。规律候选: DR 总预算守恒, 平衡带鲁棒性从抗扰余量借。两阶梯均以 ⑤ 摩擦级收官 … 抗推 4~6 N·s 免训已达标。”
train/OMNI_V0_SPEC.md § 4. ⑥ push 四梯定案 (2026-08-07) Knee swing collapsed because it directly trades against the slip penalty - price the conflict explicitly and clamp what reward cannot hold
knee-swing-vs-slip-pricingWhen a behavior collapses as another metric improves, look for the term pair trading them and set their price ratio deliberately (with escalation and reverse tripwires pre-registered); where the policy actively spends action budget to undo your target, stop paying more reward and clamp the target space structurally.
Symptom
Knee peak-to-peak swing collapsed across generations - v5 33 deg, v10 26-30, v10b 7-8, v11 6.5-8.6 - and rolling the clock back did not recover it, acquitting the clock; the collapse tracked the gated slip penalty instead: "屈膝与不打滑在当前奖励里直接对抗" - v10b's excellent 93 deg slip was purchased with knee amplitude.
Context
Reward-side flexion fixes had failed three times: raising reference amplitude backfired twice (v9/v11), and v11's deep-squat default was actively fought by the policy - it spent 0.68 of action budget pulling the squat straight ("被策略花 0.68 动作拉直反杀"). v12's design accepted the conflict as real and attacked on two tracks: (1) ECONOMICS - a direct knee_swing_amplitude reward (+0.3, target 0.55 rad, capped at 0.6/step = 55% of tracking), explicitly opposed to the slip penalty by design ("显式对立——这正是设计:v12 就是这场对抗的定价实验"), with an escalation ladder (K +0.3 -> +0.5, then slip -0.5 -> -0.3, one layer at a time) and a reverse tripwire (slip telemetry back at v10 levels -> slip weight to -0.8, accept ~20 deg knee compromise); (2) STRUCTURE - knee target bounds [0.2, 0.9] rad so full straightening is physically impossible (straightest 11.5 deg) and the 0.68 fighting budget is released. A bonus falsifiable prediction was attached: phase-lock strength tracks amplitude (v9_probe 48 deg locked 2.5 Hz; v11 low-amplitude 1.36 Hz unlocked), so if K works, hardware phase-lock should return - one change, two verdicts.
Change
knee_swing_amplitude reward + knee target clamp + pre-registered escalation/reverse levers; the failed reward-side-only approach retired.
Outcome
The lineage was frozen before v12 trained (strategic reset), but the diagnosis stands as the walk line's clearest example of two reward terms trading a behavior between them, with the pricing experiment and structural clamp fully designed and calibrated.
Mechanism
When two terms price opposite aspects of one motion (swing amplitude creates yaw momentum that becomes slip), the optimizer settles wherever the price ratio puts it - patching one side moves the equilibrium, not the conflict. Explicit pricing makes the trade a designed quantity; structural clamps remove the regions where the policy spends budget fighting the designer.
Conflicts
The pricing experiment (K vs slip) was designed and calibrated but never trained - the 2026-08-05 reset suspended v12; the collapse attribution table and the 0.68-action counterattack are measured, the remedy's效果 is untested.
Applies when
- one gait quality degrades in lockstep with another's improvement
- the policy visibly fights a default pose or reference
- repeated reward-side fixes for the same behavior have failed
“膝摆塌在 v10→v10b,头号嫌疑是门控滑移罚(四代实测膝 p2p:v5 33° / v10 26~30° / v10b 7~8° / v11 6.5~8.6°;退时钟没救回 → 非时钟)——"屈膝"与"不打滑"在当前奖励里直接对抗 … 奖励侧修屈膝已三败 … v11 深蹲 default 被策略花 0.68 动作拉直反杀”
train/WALK_V12_SPEC.md § 0. 定位 / 2. K —— 膝摆经济(与滑移罚的对偶) Adapting a lineage to one plant increment needs hundreds of iterations, not thousands - long runs only buy specialization
continuation-budget-not-from-zeroBudget continuation rungs by increment class (hundreds of iterations for plant pins and smooth shifts, ~1000-1500 only for behavior-demanding changes like push), enforce a hard cap with frequent evaluation, and treat remaining budget as a reason to stop, not to continue.
Symptom
The default "6000 iterations per rung" (a from-zero-scale budget) was about to be applied to continuation rungs whose only change is one plant/DR increment - overspending compute and, worse, giving each rung thousands of iterations to specialize away retained skills.
Context
The 2026-08-07 budget table replaced the default with "最低适应窗口 + 每 100 iter 验收 + hard cap" scaled to the increment's difficulty: fixed-latency levels 300-500 (cap 500-800; the base has already seen in-band values, this only pins the plant); PD full-band 700 (cap 1000; kp+/-20%/kd+/-30% clearly widens the actuator family); COM +/-20 mm 500 (cap 800; a smooth dynamics shift); friction DR 700 (cap 1000; contact and actuator friction change the gait/contact solution together); push 1000 (cap 1500; a non-static plant change requiring recovery behavior - hardest). Rationale: "续训适应一个 plant 增量不需要从零量级的预算,跑长了只是给特化时间". The C ladder reused the scheme (per-rung caps 500-2000 by increment type), and the deep-training hazard got its own name when long runs sold quality ("深适应卖质量" - deep adaptation sells quality).
Change
Per-rung iteration budgets set by increment class with hard caps and 100-iter watch loops; checkpoint selection inside the window by the smoke curve, never "run to cap because budget remains".
Outcome
S2/C rungs completed in 300-1500 iterations each; the recurring late-run degradations (collapse valleys at 1500+, vx+0.30 decay) fell outside most rungs' caps instead of inside their runs.
Mechanism
A continuation rung's learning problem is local robustification around an existing optimum - low sample complexity; iterations past adaptation are spent sharpening onto the current distribution, which is exactly how retained skills and margins erode. Budgets sized to the increment bound both compute and the specialization damage window.
Applies when
- planning iteration budgets for a robustification or command ladder
- a continuation run keeps improving its training metric late
- retained skills decay in the back half of long continuation runs
“「最低适应窗口 + 每 100 iter 验收(watch_ckpt --every 100)+ hard cap」—— 续训适应一个 plant 增量不需要从零量级的预算,跑长了只是给特化时间 … ⑥ push | 1000 | 1500 | 非静态 plant 变化,要学 recovery 行为,最难”
train/OMNI_V0_SPEC.md § 4. 每级 iter 预算(2026-08-07 用户定) A 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 Pre-register the ladder's risks and how each future result will be read - before training
preregister-risks-and-fork-readingsBefore a training ladder or risky rung, write the risks, the stop rules, and how every plausible outcome will be interpreted - then do not edit them after seeing results.
Symptom
Without pre-registration, ladder results get rationalized after the fact; the team had already seen post-hoc reads go wrong and adopted written pre-commitment.
Context
The C-ladder execution sheet opens with three numbered pre-registered risks: (1) S2 plant robustness will not carry into omni - a full S2 redo is budgeted from the start; (2) the s1e recipe has a collapse valley at iter 1500+ (recorded twice), so every rung runs a watch_ckpt --every 100 smoke loop with stop-on-degradation; (3) the root's OOD survival edge may partly be "it is slower / commits less" - with the reading fixed in advance: if C1 training raises tracking while survival drops, the edge was bought with slowness, and root selection reopens. Later rungs went further, pre-registering a full result-to-conclusion table for the A/B arms ("预注册读法（事后不改）") and even pre-registering the author's own doubt that a level would fail and what its failure would prove.
Change
Standing practice: before each rung, write down (a) known risks with their mitigations, (b) the interpretation of each possible outcome, (c) stop criteria - all frozen before the run starts ("开训前写死，事后不许改").
Outcome
When arm-A/arm-B and redo results arrived, conclusions were read off the pre-registered table instead of argued; a predicted-likely-FAIL level (C4-redo3) was still run because its pre-registered value was eliminating the regularization hypothesis - which it did.
Mechanism
Pre-commitment converts each training run into a decisive experiment: outcomes falsify or confirm named hypotheses instead of being absorbed into a story; it also makes negative results valuable (a FAIL that eliminates a hypothesis advances the search).
Applies when
- launching a multi-rung training ladder
- running an A/B fork whose outcome will drive a fork/root decision
- a rung is expected to fail but is run for its diagnostic value
“三条预注册风险 … 若 C1 训后跟踪提上去而存活掉下来，说明这条优势是速度买的、不是通用性 → 那时重开选根。”
train/C_LADDER_RUN.md § 0. 三条预注册风险 The run policy never left the ground and fell in the second simulator from the frontal plane - its DR (gains and latency only) covered the actuator axis, not the frontal-plane contact and inertia disturbances the doubled stride amplified; "is DR on" is the wrong question
thin-dr-judged-by-channel-coverageJudge a DR recipe by whether its randomized terms cover the channel where the skill can lose stability, not by whether DR is enabled; when a new skill lengthens single support or enlarges motion in one plane, add disturbances in the plane it destabilizes before training.
Symptom
run R1 (6,000 iterations, 78 min): no flight phase ever appeared, and every one of 13 checkpoints failed the eight-gate MuJoCo smoke. In Isaac: zero terminations in 6,000 iterations, 4.2 deg tilt. In MuJoCo at delay 2: 1/6 survived, falls within 1.9-6.2 s at 50.8-58.7 deg, the most saturated joints all roll joints.
Context
The run contract doubled sagittal travel (knee action scale 0.9, knee swing peak 1.14 rad) with a 0.60 s period and 0.40 duty - long single support - while roll/yaw scales were deliberately left at 0.5. DR copied the s1e recipe: kp/kd (0.9, 1.1) and latency on; mass, COM, joint friction and push all off; ground friction pinned at (1.0, 1.0). Flight was read two independent ways: Isaac's per-foot contact reward stayed 0.845-0.857, never above 0.87 - the arithmetic ceiling of a gait with zero flight - and 30 of 36 MuJoCo seeds had flight fraction exactly 0 (the nonzero six were all tumbling falls). Foot lift itself worked (46-59 mm against a 50 mm design point): the walk-era "not enough travel" failure did not recur.
Change
Verdict FAIL, with the pre-registered first knob (exploration noise 1.0 -> 1.2) explicitly rejected as aimed at a different axis. The lesson was generalized and applied at the next line's design review: the one-leg spec made push, body mass, base COM and friction DR mandatory for its permanent single support and banned the thin recipe.
Outcome
The run line did not continue past R1 in the sources. The one-leg V0 with the wider DR passed its friction-variant gate (mu 0.4 and 1.2) inside a 40/40 acceptance.
Mechanism
Randomizing gains and latency covers the actuator's axis; a skill whose failure lives in frontal-plane contact and inertia needs randomization on that channel (push, mass, COM, friction), or the trainer's exact plant becomes the only one the policy can stand on - the omni_s1 transfer trap a second time, this time with DR switched on.
Applies when
- a policy is flawless in the trainer and falls immediately in a second simulator
- reusing a DR recipe from a skill with a different support pattern
- failures concentrate on one axis (roll, yaw) the DR does not touch
“**机理**: 矢状面行程翻倍 (膝摆动峰 1.14 rad) + T 0.60 + duty 0.40 的长单支撑, 把额状面扰动放大了一个量级; 而 roll/yaw 通道按 §3 **刻意没有放大** (仍 0.5), DR 又是 s1e 复刻的薄配方 (mass/COM/关节摩擦/push **四关全关**, 地面摩擦钉死 (1.0, 1.0))。 … 说明**薄 DR 的判据不能只看"有没有开 DR"**, 要看**开的那几项 是否覆盖失稳所在的通道** —— kp/kd 与延迟是执行器轴向的, 对额状面接触/惯性 扰动零覆盖。 … 0.87 正是「零腾空的走路步态」的天花板算术”
git:Lucen V2@origin/run-line:train/README.md § run R1 FAIL (2026-08-09, run 21-30-30_run_r1): 腾空零, 但病根在额状面不在探索 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 Train with self-collisions ON (filtering nested-link ghost pairs) - the reward wall prevents, the physics makes cheating impossible
self-collision-physics-plus-reward-wallNever train a contact-risk behavior with self-collisions disabled; enable them with an audited filter list for nested/overlapping pairs (zero contacts across a pose sweep), record the fps cost, and keep a calibrated distance penalty as the preventive layer on top.
Symptom
walk_v8 logged 107 frames of leg-on-leg contact while still earning 0.751 tracking score - because training-side self-collisions were OFF, leg clipping was literally imperceptible to the policy ("碰腿在训练里 根本感知不到").
Context
Enabling self-collisions naively is its own trap: an Isaac audit had shown PhysX auto-filters adjacent bodies (base-hip clean for free) but nested links generate ghost forces - calf and ankle_roll overlap 65 mm at the zero pose, producing 12x body-weight phantom forces. The v10 recipe: enable self-collisions, explicitly filter only the two nested pairs (l/r calf-ankle_roll), then run a zero-contact audit at three poses (nominal stand, walk crouch, swing-extreme) requiring contact count = 0, adding any residual pair to the filter and re-auditing; a 500-iter sanity run for NaN and an fps-cost record (measured -8.8%). Redundancy with the reward-side foot-distance wall was argued, not assumed: "N2 离得远(奖励侧预防),SC 碰了疼(物理侧兜底)" - the reward keeps distance at range, the physics makes contact hurt - so the v8-style "clip legs and still score" outcome becomes physically impossible.
Change
enabled_self_collisions=True + 2-pair filter + three-pose zero-contact audit (re-verified at 0.00 N after the later mass update) + fps budget recorded.
Outcome
Leg contact entered the training signal; the audit protocol caught the nested-pair ghost-force hazard before it corrupted training; combined with the calibrated distance wall, later versions held contact = 0 on hardware and in sim.
Mechanism
A hazard absent from the training physics cannot be learned about, no matter the reward; but collision meshes that interpenetrate at rest inject large fictitious forces if enabled blindly. Filtered enabling plus a pose-swept zero-contact audit gives true contact physics with no phantom energy - and layering prevention (reward) with consequence (physics) covers both learning and enforcement.
Applies when
- real robot self-contacts while training scored it healthy
- enabling self-collisions on a model with nested collision meshes
- deciding between reward-side and physics-side fixes for clipping
“PhysX 自动过滤相邻体(base↔hip_pitch 免费干净),幽灵力只在 calf↔ankle_roll(零位嵌套 65mm,12 倍体重)。… 与 N2 互补不冗余:N2 离得远(奖励侧预防),SC 碰了疼(物理侧兜底)—— v8 那种 107 帧互碰拿 0.751 跟踪分的事从此物理上不可能。”
train/WALK_V10_SPEC.md § 4. SC —— 训练侧自碰撞(范围已探明,比想象便宜) A walking policy's tilt cutoff is a legal state for a recovery policy - the default 45 deg fall guard had to be raised for recovery tests and is disabled once the switch owns falls, so the abort chain becomes the recovery timeout, the operator's cut, and the firmware torque limits
fall-guard-becomes-a-stateWhen a new skill makes a safety cutoff's trigger a legal state, replace the cutoff with a bound of the skill's own (a timeout ending in a safe stop) instead of just switching it off; keep the operator's cut and the firmware limits as independent layers, and write every flag change into the run sheet.
Symptom
deploy_policy's default protection stops the robot beyond 45 deg of tilt. A recovery policy starts lying at roughly 90-97 deg, so under the default it is refused on the spot - a flag the first hanging checklist forgot.
Context
The layers in the sources: deploy_policy's tilt cutoff (default 45 deg, a line in the safety chain); for standalone recovery tests the cutoff was raised (110 deg in the spec's A/B sheet; 181 deg, effectively off, in some runbook commands); with --recovery-policy the cutoff is disabled because a fall is now a state, not an exception, and RECOVERY lasting over 15 s ends in a safe stop (the runbook calls it the line where the spotter steps in). Independent of the policy: firmware torque limits checked at start (12/17/11 N*m, set_torque --check), the operator cutting enable at any kicking or oscillation, and in the one-leg teleop a space-bar stop that puts the foot down.
Change
The flag was added to the run sheets, and the FSM replaced the removed cutoff with its own bound (the timeout).
Outcome
The spec records the flag omission and its fix; it does not record the FSM's timeout being exercised on hardware.
Mechanism
A safety cutoff encodes one policy's notion of "abnormal"; a new skill whose normal operation lies beyond it either cannot run or runs with the cutoff off, and only a replacement bound keeps the chain closed.
Applies when
- deploying recovery, fall-damage or acrobatic skills behind existing safety checks
- a run sheet disables a protection flag
- listing the abort chain for a hardware session
“--max-tilt-deg(默认 45°,安全链第 13 行写的那个)。recovery 的合法状态覆盖整个倾角域,把它抬到 181 = 实效关闭 … RECOVERY 超时 15s 会自动安全停(看护介入线)”
RL系统/FOLLOW THIS copy 2.md § FSM 吊挂首测 ② 落地测 / #### Recovery Policy (operator runbook, undated) No parameter tuning on the floor - a failing config retries once, then it is out; anomalies go back to sim
no-field-tuning-protocolHardware time is for executing and measuring the pre-registered matrix, never for tuning: failing configs get one retry then elimination, anomalies get recorded and reproduced in sim, and contract-check bypass flags stay unused.
Symptom
Hardware sessions create pressure to fix problems live - nudge a gain, tweak a scale - which destroys attribution and risks the robot.
Context
The anomaly-handling section of the acceptance sheet is three fixed plays: (1) falls at start -> retry once at the same settings; falls again -> that configuration is eliminated, "不现场调参" (no on-site parameter tuning); (2) limit cycle or motor screech -> stop immediately, record the gain level and the joint, reproduce in sim before any discussion; (3) systematic disagreement with sim -> record it as a finding (hardware outranks sim) rather than adjusting anything to force agreement. Related guardrails elsewhere in the sheet: never pass --allow-unstamped / --allow-plant-drift to bypass manifest checks - if it errors, something real is wrong, stop and look.
Change
Field sessions restricted to executing the pre-written matrix; every fix path routed through sim reproduction and the normal config/rung process.
Outcome
Sessions stayed interpretable (each run matched a documented config) and safety overrides never became habit; anomalies arrived back in sim as reproducible cases instead of half-remembered floor stories.
Mechanism
Field-tuned values are measured under adrenaline on one floor with no logging or baselines - they contaminate the config lineage and are unattributable afterwards; and every bypass flag that skips a contract check converts a designed safety property into an operator promise.
Applies when
- a config fails or oscillates during a hardware session
- someone reaches for a live gain tweak or a bypass flag
- writing the anomaly-handling section of a deployment runbook
“起步即摔 → 换档重试一次, 仍摔则该档出局, 不现场调参。出现极限环/啸叫 → 立刻停, 记录档位与关节, 回 sim 复现再议。… 不要给 --allow-unstamped / --allow-plant-drift —— 三枚 ONNX 都已盖章 … 真要报错说明有别的问题, 停下来看。”
train/REAL_RUN_S2.md § 4. 异常处置 Four in-lineage attempts to widen the standing stance failed - remove a tax, add a joint-space knife, change the target, add a task-space metric penalty - because the stance was the end state of the get-up path; trained from scratch with the right terms it grew right from day one
stance-decided-by-get-up-pathA posture a skill ends in is shaped by the path the policy takes to reach it; if several single-variable edits to the terminal-phase reward cannot move it, stop editing that phase and retrain with the terminal constraint present from the start.
Symptom
v2_6c stood with its feet 0.159 m apart (task-space) and its hips yawed 45-47 deg the same way, which split on the real robot. Standing-phase reward edits did not move it.
Context
V2.7-A removed the flat-feet tax on compensated stances (stance unchanged); V2.7b added a hip-roll lower-bound hinge (+5 deg in 3,000 iterations, yaw ratchet); V2.8 changed the stand_pose target to a wide flat stance (stance unchanged, yaw not unwound, feet nearly overlapping, mu 0.4 transfer 2%); V2.9 penalized lateral spacing in metres (the policy parked just outside the penalty's gate in a lunge, 0% success). The v2_6c get-up goes through a split and closes the feet together as it rises.
Change
In-lineage stance surgery was formally closed. V3.1 trained from scratch with task-space stance terms present from the first iteration (and, after P1, a positive width band instead of a penalty).
Outcome
V3.1 P1b: lateral stance 0.364 m, foot tilt 0.0 deg, all four categories 100%, MuJoCo mu 1.0 and 0.4 both 100% - with a symmetric toe-out the kinematic audit had not enumerated. P1c (with a yaw guard): 0.355 m, all six acceptance criteria passing, mu 1.0-0.4 all 100%; it became the product.
Mechanism
A converged policy does not rebuild the path that produced its terminal posture; a standing-phase gradient only finds the nearest hack around the posture the get-up delivers.
Applies when
- the final posture of a transition skill is wrong and resists terminal-phase shaping
- repeated continuation rungs produce hacks instead of the intended posture
- deciding between another in-lineage fix and a from-scratch retrain
“窄站距 + yaw 扭是 v2_6c 起身策略(劈叉起身 → 双脚并拢收势)的**结构性 终态**,不是站立段的孤立参数 —— 站立形态由起身路径决定,在血统内只动 站立段奖励改不动它。”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §44 结果:V2.8 判 FAIL —— 血统内站姿手术第三次证伪 Narrowing the speed range to stop high-speed falls entrenched crouch-shuffling - judge gait quality at the speed that demands a gait
low-speed-commands-reward-draggingSet command ranges to include speeds that physically demand the target behavior, and evaluate behavior-quality gates at those speeds; when a restriction is added to suppress a failure, check what new optimum it creates at the remaining commands.
Symptom
After the command range was narrowed to (0.15, 0.35) m/s (to treat walk_v1's 134% overspeed and 8.3 s fall at 0.5), the policy settled into crouched foot-dragging; tracking rose monotonically with speed (63% at cmd 0.2, 76% at 0.3, 87% at 0.45), showing low speeds were where the degenerate gait was optimal.
Context
The narrowing advice was the author's own and is retracted in the file: it treated the symptom (falls at speed) while reinforcing the root cause (at 0.15-0.35 m/s, shuffling in a crouch is globally optimal - the Froude number is so low that even humans would not lift their feet). A zero-cost experiment confirmed the flip side: at cmd 0.5 the same policy met BOTH tracking (81%) and clearance (23.0/23.2 mm) standards.
Change
Speed range widened back toward (0.15, 0.5) - upper bound deliberately slightly above the mechanically feasible ~0.44 m/s so the policy finds the boundary itself; acceptance re-pointed: gait-quality criteria (tracking, clearance) judged at 0.45-0.5 m/s, low speed kept only as a survival check.
Outcome
v4 -> v6 progression under the widened range delivered 87% tracking with 34 mm clearance; the "low command = drag" account was confirmed by the monotone tracking-vs-speed curve.
Mechanism
Command distribution is part of the reward: physics prices gaits per speed, and at very low speed the energetic optimum is no swing phase at all. Restricting training to that regime makes the degenerate gait the correct answer to the posed problem - and grading a gait at a speed that does not require stepping measures nothing.
Applies when
- a gait degenerates after a command-range restriction
- quality metrics improve monotonically toward the range boundary
- writing acceptance criteria for gait quality vs survival
“现在看那个建议可能起了反作用:0.15~0.35 m/s 下蹲着蹭就是全局最优,抬腿反而亏。收窄治的是"高速摔倒"的症状,却强化了拖地的病根。… 验收标准里的 cmd 0.2 本身就是拖地速度(Froude 数极低,人在那个速度下也不抬脚)。accept_v2 应把速度跟踪与 clearance 的判定点改到 0.45~0.5 m/s”
train/WALK_DIAGNOSIS.md § ② 放宽速度区间 / ① 零成本实验 The shipped checkpoint was chosen by scanning checkpoints on the full gate - neighbours 100 iterations apart failed 1 and 38 cells, late checkpoints degraded - never by taking the last one, and training stopped on signals, not on a schedule
checkpoint-choice-is-a-full-gate-scanChoose a release checkpoint by running the full acceptance battery over a band of checkpoints (including the transfer axis), stop training on measured signals rather than a fixed iteration count, and expect adjacent checkpoints to differ sharply.
Symptom
Gate results moved sharply and non-monotonically between checkpoints of the same run, and the last checkpoint was often not the best.
Context
One-leg V0r1: the 2,000 neighbourhood was best; from 2,500 on the nominal gates degraded (late overtraining); 2,000 itself had one real micro-hop (17.7 mm over 5 frames); 2,300 was all green and shipped. V0r2: failed cells per checkpoint 2,000:19, 2,100:38, 2,200:1, 2,300:3, 2,400:27, 2,500:12, 3,000:18 - 2,200 shipped. The recovery line learned the same from the other side: stopping v2_6 early at a scheduled point left a policy whose re-fall rate had spiked to 9-22% before consolidation healed it ("stop on signals, not on the schedule"), and a continuation's transfer decayed checkpoint by checkpoint while Isaac stayed perfect.
Change
The acceptance rule "scan checkpoints, do not look only at the last one" is written into the one-leg gates (called the S1 discipline); release candidates are chosen from the scan.
Outcome
Both one-leg releases were mid-run checkpoints (2,300 and 2,200) chosen by the full 40-cell battery.
Mechanism
PPO keeps changing the policy after the gates saturate; with no gradient toward the gate's conditions, later checkpoints wander, so gate quality is a noisy function of iteration.
Applies when
- picking which checkpoint of a run to export and stamp
- a run is stopped at a fixed iteration budget
- final-checkpoint results are worse than mid-run smoke tests
“Isaac 侧 S1 纪律: 验收扫 checkpoint，不是只看最后一个。 … 扫描判决: 2000 邻域最优——2500+ 标称面退化（⑤③② 散挂, 晚期过训）, 2000 有一例真微跳（L s100 μ1.2, 17.7mm/5帧）, 2300 全绿。”
git:Lucen V2@origin/oneleg-line:train/ONELEG_V0_SPEC.md § §6 验收门 / §8 核查单 5 与 7 Prove an armless get-up exists before training it - a connected static domain, 25% torque on the cheapest path, an 8 mm hand-over gap, a static roll-over - and write down what each scan cannot represent
get-up-feasibility-accounts-before-trainingBefore training a get-up or any multi-contact skill, compute the quasi-static accounts - connectivity of the static domain, torque along the cheapest path, hand-over gaps, COM shift available for rolling - and state which configurations each scan cannot represent; when a policy gets stuck in one of those, extend the scan before blaming the reward.
Symptom
A torso-and-legs robot has no arms to push off the ground; whether it can get up from the floor at all was unknown when the line opened.
Context
recovery_feasibility.py ran three accounts before any training (the run line's "hard accounts first" discipline): a sagittal quasi-static scan (0.05 rad grid, 44,520 configurations, MuJoCo FK, flat-foot assumption). (1) The static standing domain (COM over the feet, torques in limit) has 29,586 cells, flood-fill connected with no islands, from a 0.097 m deepest squat to the 0.384 m stand. (2) The minimum-torque path peaks at 25% of the limits (knee 2.9/12, ankle 3.7/17 N*m) - a 4x margin. (3) All 508 ground-contact configurations have the contact behind the COM; the smallest gap to pure foot support is 8 mm. A roll-over account: swinging both straight legs to one side shifts the COM 96 mm against a 62 mm torso half-width - 1.6x, so rolling needs no momentum. Three conclusions were written down for later attribution: the legs are 80% of the mass (swinging them moves the COM), prone has no flat-foot hand-over face (merge into a supine/side sit first), and supine needs no sit-up (hip flexion is limited to 75 deg).
Change
The accounts gated opening the line and were cited in every later argument about what the robot can physically do.
Outcome
They held where they applied: in V1.0 every fall category was righted under a hard rate limit, which the spec records as the quasi-static roll-over account verified by training, and the 25% torque path was the basis for pursuing a slow get-up. They also misled once: account (3) is sagittal, and on 08-09 the spec corrected its scope - it cannot represent the splayed W-sit where the policy actually stalled. A follow-up prone hip-ROM scan (471,625 cells) found 3,912 two-foot-contact cells and none with both soles within 25 deg of level (best 40.2 deg): a flat-footed push-up from prone is infeasible on this robot, so the fix became where the feet go after sitting up.
Mechanism
A get-up needs a connected path through statically feasible configurations and enough torque along it; quasi-static accounts bound both cheaply, and momentum can only make the real problem easier. A reduced-dimensional scan, though, only speaks for the configurations it can express.
Conflicts
In R0.1-R0.2 the spec read account (3)'s "prone has no front hand-over" as "prone lacks the roll-over skill"; R0.3's confusion matrix showed prone had righted its torso 159/159, and the spec then restricted account (3) to the sagittal configurations it models.
Applies when
- opening a get-up, recovery or climbing skill on a new robot
- a robot lacks arms or other obvious contact options
- a policy stalls in a configuration a feasibility scan never modelled
“本机 **torso + legs、无手臂可撑地**,开训前先证明存在不依赖手臂的物理解 … 双腿同侧直腿摆最大横移 **96 mm = 1.6×** … **静态摆腿即可翻身,无需动量**”
git:Lucen-recovery@origin/recovery:train/RECOVERY_V0_SPEC.md § §1 机械可行性判决(2026-08-09,recovery_feasibility.py 三笔账) Removing a foot-spacing wall passed every simulated gate and made the feet collide on the real robot - nothing priced stance width in the two-foot phase, the policy narrowed to the simulator's self-collision floor, and real calibration offsets closed the last millimetres; the wall came back with a gate
removed-wall-returns-on-hardwareWhen a constraint is removed, name what will govern that quantity instead and add a gate for it; never let a simulator's collision floor be the margin, and when a gate is exceeded by a hair, record the exact numbers and hand the release decision to a person instead of quietly passing it.
Symptom
On the first real-robot try of oneleg_v0 (2026-09-16) the two feet collided; the user also judged the folded foot not high enough.
Context
The V0 reward table had dropped the feet_lateral_distance wall because it seemed to conflict with the hip adduction single support needs. In the two-foot command bucket no remaining term governed stance width, so the policy drifted narrower until the simulator's self-collision stopped it; the sim acceptance had no foot-spacing gate, so 40/40 said nothing about it. On the robot, calibration offsets consumed the margin.
Change
V0.1: the wall restored (-10, minimum 0.16 m), re-checked against measured numbers (a swing-phase lateral spacing of ~148 mm costs 0.12 per step, acceptable); fold weight 0.8 -> 2.0; a ninth gate: minimum foot spacing >= 100 mm and zero leg-contact frames. The removal was kept on record.
Outcome
oneleg_v0_1 (V0r2 model_2200) passed 39/40 with the spacing gate 40/40. The single miss (a 15.4 deg tilt transient against a < 15 deg limit during a side switch, steady 6.9 deg, everything else green) was recorded with its numbers and released for the user to overrule.
Mechanism
An unpriced degree of freedom drifts to wherever the simulator stops it; if that stop is the simulator's own collision model, the policy's margin on hardware is whatever the calibration error leaves.
Applies when
- dropping a reward term that looked redundant or conflicting
- hardware shows a failure no simulated gate measures
- a release candidate misses one gate row by a small amount
“V0 撤墙被真机证伪（2026-09-16）：双脚桶没有任何项管站宽，策略贴 sim 自碰撞底线收窄，真机标定偏差一吃**双脚相碰**。 … min ≥ 100 mm 且腿碰 0 帧（eval_straight 同判据）—— … V0 真机双脚相碰暴露 sim 门未看脚距的缺口 … L s2 标称 tilt 瞬态 15.4°（门限 <15, 超 0.4°, 稳态 6.9°, 该跑其余全绿）——换侧瞬态蹭线, 判定放行留档, 用户可否决。”
git:Lucen V2@origin/oneleg-line:train/ONELEG_V0_SPEC.md § §4 feet_lateral_distance 行 / §6 验收门 ⑨ / §8 核查单 7 Every new penalty ships with a pre-registered withdrawal clause - if healthy gait must pay above the cap, the term stands down
calibration-threshold-with-withdrawal-clauseIntroduce every new penalty with: the zero-cost-option audit, a replay-calibrated weight formula (healthy pays a fixed small fraction of tracking), and a pre-registered withdrawal condition - and let the clause fire without argument when the calibration says the term cannot be afforded.
Symptom
Three same-shaped crashes had established a failure archetype: v4's clearance, v8a's landing window (weight off by 58x uncalibrated), and v6a's bare hip_yaw suppression all combined a zero-cost "don't move" option with a fee on any motion - a reverse barrier that pushes policies toward standing still.
Context
The v11 landing-window penalty was therefore introduced under a calibration-threshold protocol: (1) shape chosen with the window tightened (h_gate 0.03 -> 0.02, because 0.03 equaled the clearance target and priced the entire descent); (2) weight from a FORMULA, not judgment: measure the term's raw value on healthy replays (v5/v10b), set w = -(0.10-0.15 x tracking reward) / raw_healthy; (3) withdrawal clause pre-registered: if healthy gait must pay >15% of tracking no matter the tuning, the term is withdrawn to the next version rather than forced in - "不硬上". The companion hip_yaw quieting term ran the same protocol (calibrate on replays, healthy pays <=5%) and was later retired entirely when a structural fix (zero action scale) made its shaping tax unnecessary.
Change
Penalty introduction protocol: shape audit (what is the zero-cost option?), replay-based weight formula, healthy-pay cap with a written stand-down condition - all before training.
Outcome
The landing term was in fact withdrawn under its clause (v12 records "P5 落地窗口罚 已撤 … 维持撤下"), demonstrating the protocol firing as designed instead of the fourth same-type crash.
Mechanism
A penalty's damage mode is mispricing healthy behavior; since the healthy price is measurable in advance on replays, both the weight and the go/no-go decision can be computed rather than discovered by a ruined training run. The withdrawal clause converts "make it work" pressure into a clean deferral.
Applies when
- adding any motion-taxing penalty to a working gait
- a proposed term's weight has no measurement behind it
- a previous same-shaped term crashed training
“权重公式而非拍脑袋:先在 v5/v10b 回放上量 h_gate=0.02 的原始值,w = −(0.10~0.15 × 跟踪奖励) / raw_健康;标定门槛:若健康步态无论如何要付 >15% 跟踪,本项撤下留 v12,不硬上 (v4 clearance/v8a-B/v6a 三次同型翻车的教训:代价为零的"不动"选项 + 一动就收费 = 反向壁垒)。”
train/WALK_V11_SPEC.md § 6. P5 —— 落地窗口罚(三代欠账,标定门槛制) A policy's gain profile is part of its contract - the one-leg policy needs per-joint gains the default profile lacks, and the manifest refused an evaluation under the default once; the recovery contract's beta was never stamped, a known gap not to repeat
gain-profile-belongs-in-the-stampStamp everything that defines the closed loop a policy was trained in - gains included - into its manifest, and make every consumer refuse a mismatch; a profile field that is not in the stamp is a silent misconfiguration waiting for an operator to forget a flag.
Symptom
A policy trained with hip_roll kp 80 and ankle_roll kp 60 behaves differently, or falls, under the default kp 20/12 profile - and the gain profile is a command-line flag an operator can forget.
Context
The one-leg line added a gain_profile field to the contract so the stamped manifest carries it; the spec's deployment note says the manifest guard blocks rl_default and that it had already bitten once in simulation (an evaluation run without the one-leg profile). The same spec states the general rule - any new profile field must be synced into the manifest builder - and names the counter-example: the recovery line's beta was never put into the manifest. The recovery line itself had decided that its anchored authority is computed from the base rl gains and written into the contract so it cannot drift with the gain flag, and that the older kp x 0.9 profile chosen in the V0 era does not match the beta contract and must not be used.
Change
Gain profile as a contract field checked at load; per-contract gain choices written into the run sheets.
Outcome
Evaluations and hardware runs of the one-leg policy run under rl_oneleg or are refused; the recovery beta gap stayed recorded as known.
Mechanism
A policy is trained against a closed loop whose gains are part of the plant; running it under other gains is an out-of-distribution plant, exactly like a wrong observation scale.
Applies when
- a skill introduces per-joint or skill-specific gains
- deployment gains are chosen by a command-line flag
- adding any new field to a policy profile
“增益档 `--profile rl_oneleg` 必须给 —— manifest 防线会拦 `rl_default`（sim 已咬合一次） … （recovery 的 β 未进 manifest 是已知缺口，不再复制）”
git:Lucen V2@origin/oneleg-line:train/ONELEG_V0_SPEC.md § §9b AGX 真机手顺 要点 / §3 契约