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
142 cards matching “stance-decided-by-get-up-path”.
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 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) 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） Isaac splits Coulomb friction into static and dynamic columns - wiring only static means zero loss during motion, silently discarding the identified value
sim-api-friction-columnsWhen installing identified actuator parameters, map each measured quantity to the simulator's exact API column for the operative regime (dynamic for moving loss, viscous for damping), verify per joint after landing, and audit how randomization intervals fall on each column's nominal.
Symptom
The hardware-identified Coulomb friction (tau_c) was about to be installed into the trainer through the friction= field alone - which in Isaac 5 populates only STATIC friction, so during motion the joints would lose no torque at all: "只给 static 则运动中不损耗, 辨识的 τ_c 走路时等于没接" (the identified tau_c would effectively not be connected while walking).
Context
The v12 integration wired all three columns deliberately: armature= and friction= from the 2026-08-04 hardware identification, PLUS dynamic_friction= (Isaac 5 splits Coulomb into static/dynamic; the moving-loss column is dynamic) and viscous_friction= (= the measured joint damping 0.02, aligned to MJCF's damping). Each value was re-checked per joint after landing. A DR interaction was audited and booked rather than hidden: randomize_joint_parameters jitters ALL friction columns with ONE interval - the [-0.05, +0.10] band was calibrated against the Coulomb nominal, and landing on the viscous nominal 0.02 it becomes [0, 0.12], "偏宽但保守" (wide but conservative), accepted with the note that pre-viscous behavior was already [0, 0.10] on a base of 0.
Change
Measured actuator parameters installed across all applicable API columns (armature, static, dynamic, viscous), with the DR side effect on shared randomization intervals audited and recorded.
Outcome
The first generation where the identified plant actually acts during motion in the trainer; the silent-column failure mode documented before it cost a training run.
Mechanism
Physics engines decompose "friction" differently (single coefficient vs static/dynamic/viscous columns); a measured parameter is only installed when it reaches the column the solver reads in the regime that matters (motion, not stiction). Randomizers that share one interval across columns rescale the band by each column's nominal - a hidden unit change.
Applies when
- installing identified friction/armature into any trainer
- porting plant parameters between simulators or engine versions
- joint losses in sim do not match bench measurements during motion
“并额外传 dynamic_friction=(Isaac 5 把库仑拆 static/dynamic 两列,只给 static 则运动中不损耗,辨识的 τ_c 走路时等于没接)与 viscous_friction=(= joint_damping 0.02,对齐 MJCF damping)。… randomize_joint_parameters 用同一个 friction 区间抖三列, [-0.05,+0.10] 是按库仑标称标的,落到粘滞标称 0.02 上成了 [0,0.12]”
train/WALK_V12_SPEC.md § 7. 核查单 (Isaac 接 V.ACTUATORS 新字段) A real-robot verdict is (policy x deployment stack) - when the stack changes materially, old verdicts expire
stale-verdicts-under-old-stackDate every hardware verdict with the deployment-stack version it was measured under; after any material stack change, re-test before trusting old condemnations or old praises - with the interpretation of each possible result written down first.
Symptom
walk_v5 stood condemned as "kicks wildly" and walk_v6 as "cannot walk unassisted" - but those verdicts were issued under an earlier deployment stack (--heading did not exist yet, several fixes had just landed); only v7 had ever run under the current unified stack.
Context
New sim evidence sharpened the doubt: a same-harness four-version sweep showed v6 was the HEALTHIEST archive at the real operating point (cmd 0.15: dominant frequency locked at 2.50, steps 35:35 perfectly symmetric, foot distance 203/190 mm best of four, mean tilt 4.2 deg, saturation 15%). A full re-test under the unified stack was scheduled with per-version questions and a pre-filled interpretation table ("判读表(预填假设,回来对号)"): e.g. v6 walks + frequency ~2.5 -> old verdict was the stack's fault, v6 becomes the comparison champion; v6 walks but at ~1.25 -> period-doubling on hardware = confirmed plant gap, actuator fitting promoted to mainline; v5 no longer kicks -> the kicking was an old-stack artifact.
Change
All four versions re-queued on hardware under one stack (same torque limits, slew profile, heading loop, logging), with the version-specific legacy profile pinned; verdicts held provisional until re-issued.
Outcome
The re-test design separated policy properties from stack artifacts before any policy was permanently written off - and turned each outcome into a specific conclusion via the pre-filled table.
Mechanism
A deployed behavior is produced by the policy plus everything between it and the motors (heading loop, slew limits, torque caps, clock); verdicts implicitly condition on that whole stack. Fixing the stack invalidates the conditioning, so old failures may be stack artifacts and old successes may not survive either.
Applies when
- deployment tooling (limits, filters, loops) changed since a policy was last judged
- deciding which historical policy is the rightful baseline
- a sim sweep contradicts an old hardware verdict
“只有 v7 在完整的今日部署栈下上过真机 … v5"左右乱踢"、v6"未能自主"的判决全部来自更早的栈(--heading 尚不存在, 部分修复刚落地)——判决已过期。且 2026-08-02 四代同机仿真横测翻出了新证据:v6 在真机工况(cmd 0.15)下是四代里最健康的仿真档案”
train/REAL_SWEEP_V5_V8.md § 0. 为什么重测 Add a single-point-suspension test to acceptance - the ground is a free stabilizer that hides divergence
suspension-probe-removes-free-stabilizerInclude at least one acceptance condition that strips the environment's free stabilization (suspension, or equivalent) - the sim-passing policy that fails on hardware is often failing a condition the battery never posed.
Symptom
walk_v5 looked healthy in every on-ground sim test yet diverged on the real robot - the acceptance battery had never measured a condition that would have revealed it.
Context
The battery gained a single-point-suspension probe (robot hung, feet free): measure torso tilt while the policy runs without ground contact. v5 scored 45.9 deg mean tilt suspended - wildly unstable - which the file calls "最灵敏的失稳探针(拿掉地面这个免费稳定器)": ground reaction forces passively stabilize a marginal policy, so on-ground metrics saturate long before the policy's internal balance is actually sound. v6 halved it (23.0 deg, target <10 deg) - progress visible on a scale where on-ground numbers showed nothing.
Change
Suspended-tilt added as a standing acceptance row; run under the honest contact parameters battery (accept_v2 with measured condim 4 / torsional friction 0.035), under which v5 correctly FAILS in agreement with the real robot.
Outcome
The sim battery's verdict on v5 flipped from pass to fail, matching hardware; suspended tilt became the discriminating metric between v5 and v6 (45.9 vs 23.0 deg) when ground metrics differed little.
Mechanism
Contact with the ground closes a stabilizing feedback loop the policy gets for free; removing it exposes the policy's own attitude control authority. A metric measured only in the assisted condition cannot rank policies by the unassisted quantity that hardware will actually demand during perturbations and flight phases.
Applies when
- sim acceptance passes but hardware diverges
- designing an acceptance battery for a legged robot
- two candidates tie on ground metrics
“单点吊那条是最灵敏的失稳探针(拿掉地面这个"免费稳定器"), v5 在地上一切正常却在真机发散, 就是因为验收从没测过这个工况。”
train/WALK_V6_MINIMAL.md § 5. 验收 Fix the task first, harden the plant second - DR budget spent on a dying task is wasted
task-shaping-before-plant-hardeningFreeze the task/command distribution before spending DR budget on plant robustness; if the task will still change, schedule plant hardening as a final pass and book the interim robustness gap explicitly.
Symptom
Tempting default ordering was to keep the plant-hardened (S2) lineage and teach it new commands; but the S2 plant adaptation had been earned on the straight-walk task, and the new omni tasks (sidewalk, in-place turn) use completely different contact patterns.
Context
The team had direct evidence that DR robustness is a budget that gets reallocated when the data distribution changes ("push/μ 两轮已实证 DR 预算有限且会被重分配") - robustness trained under one task/command distribution does not persist when training continues under another.
Change
Ladder order set to: first C (task shaping - add command modes until the task family is final), then a second S2 pass (plant hardening) on the C product. The plant-robustness gap this creates mid-ladder is accepted and booked explicitly ("此处不欠账" - the debt is assigned to the second S2 pass, not denied).
Outcome
The first S2 pass was not wasted: its laws (kd bandwidth <-> low mu, push need not be trained, ground mu need not be trained, bistability) let the second pass drop from five rungs to three. The C ladder itself ran on the softer plant band without incident.
Mechanism
DR robustness is carried by the policy's visited-state distribution; changing the task changes that distribution, so robustness bought under the old task partially dissolves. Hardening before the task is final means paying for robustness on states that will no longer be visited - "给一个即将不存在的任务花预算" (spending budget on a soon-to-not-exist task).
Applies when
- deciding ordering between skill/command expansion and DR hardening
- a hardened lineage is proposed as the root for a task change
- robustness regressions appear after adding new command modes
“S2 的 plant 适应是为直行步态调的，C4 侧走/C3 原地转是完全不同的接触模式，先硬化再改任务 = 给一个即将不存在的任务花预算（push/μ 两轮已实证 DR 预算有限且会被重分配）。故顺序改为 先 C（任务定型）→ 再 S2（plant 硬化）。”
train/C_LADDER_RUN.md § 0. 决策逻辑 = 短板可不可恢复 (末段) A quadratic clearance reward stalled for 3500 iters near target - switch to an indicator on accumulated height
indicator-reward-avoids-gradient-decayWhen a shaped term plateaus near its target, check the gradient profile: replace vanishing-gradient forms with threshold/indicator forms for the final approach, and prefer delta-accumulation over absolute positions to immunize against frame offsets.
Symptom
The quadratic-error clearance term froze at -0.002 from iteration 2000 to 5500 - thousands of iterations with no progress on foot lift.
Context
Diagnosis: a quadratic penalty's gradient vanishes as the error approaches target, so exactly where the last millimeters must be earned the incentive fades to nothing. The replacement (Humanoid-Gym form): accumulate the swing-phase height climb per foot, reward a BINARY indicator |accumulated - target| < 0.01 masked to the planned swing window, reset on contact, weight +1.6 as a positive reward. Two properties: the indicator's incentive is constant until the threshold is crossed (no decay zone), and accumulating height DELTAS makes any constant sole-frame offset cancel automatically - which structurally sidesteps the earlier 0.0585 m zero-point bug ("顺带绕开我先前那个'忘了减 0.0585 导致惩罚恒为 0'的坑").
Change
Clearance reformulated from quadratic penalty on instantaneous height to indicator on per-swing accumulated climb (target 0.03 m by leg-length scaling, weight +1.6).
Outcome
Part of the v5 package under which lift finally moved (v5 29 mm, v6 34 mm vs the stalled 18-24 mm era); the offset-cancellation property removed one whole bug class from the term.
Mechanism
Policy-gradient learning follows the reward's local slope; quadratic shaping concentrates slope far from target and starves it near target, so convergence stalls precisely at the finish line. An indicator pays a constant bounty until the goal is met; formulating on deltas rather than absolutes removes sensitivity to reference- frame constants.
Applies when
- a reward term's value freezes short of target for thousands of iters
- designing clearance/height/precision terms
- reward code depends on absolute link positions
“现行二次型在接近 target 时梯度趋零 —— 这正是 clearance 从 iter 2000 到 5500 卡在 −0.002 不动的原因。… 二值指示在跨过阈值前梯度恒定,没有衰减区 … 累积 delta 让 SOLE_OFFSET 自动抵消”
train/WALK_V5_SPEC.md § 3. clearance 改峰值型(去掉二次型的梯度衰减) Mirror augmentation over an asymmetric default injects systematic error - symmetrize the default first and verify the transform bit-exact
mirror-augmentation-needs-symmetric-defaultBefore enabling any symmetry augmentation, make every constant inside the observation encoding exactly symmetric, and validate the mirror transform against forward kinematics to machine precision - an unverified augmentation is a new error source, not a regularizer.
Symptom
Mirror data augmentation was about to be added while both default poses (standing_pose, walk nominal_pose) were asymmetric - stale hand-tuned compensations from before a ground re-calibration, with hip_yaw differing 2.40 deg between sides and the foot soles actually tilted (pitch 2.88/1.35 deg, roll -2.47/+0.25 deg).
Context
The observation encodes joint_pos_rel = q - default. Under mirroring q_l -> -q_r, the relation (q-default)_l -> -(q-default)_r holds only if default_l = -default_r; with an asymmetric default, augmentation produces observation pairs that are NOT mirror images, i.e. "default 不对称时做镜像增强会引入系统性错误,比不做还糟" (worse than not doing it). The fix: adopt model geometric zero as standing default (MuJoCo FK verified: sole pitch/roll exactly 0, asymmetry 0.00 deg) and a symmetric crouch for walk (hip -0.25/knee -0.5/ankle -0.25 satisfying hip - knee + ankle = 0 to keep soles flat). The mirror transform itself was verified bit-exact before use: pseudovector vs polar-vector sign patterns (ang vel [-1,1,-1], gravity [1,-1,1], cmd [1,-1,-1]), joint swap-and-negate; FK check that left-foot pose under q equals the mirror of right-foot pose under mirror(q), measured error 0.00e+00.
Change
Defaults symmetrized first (with init heights recomputed by FK), stand policy retrained on the new default so both policies share one default; augmentation enabled only after the FK mirror test passed.
Outcome
stand_v1 achieved exact left/right pairing (l_knee -0.1013 / r_knee +0.1013), six-pair asymmetry 0.0 deg, height fluctuation 7 -> 1 mm, 33% less mean |action|.
Mechanism
Augmentation asserts an equivariance of the observation encoding; any asymmetric constant inside the encoding (the default) breaks the asserted symmetry, so the augmented data teaches a false invariance. Verifying the transform against FK geometry tests the assertion end to end, independent of the training stack.
Applies when
- adding mirror/symmetry augmentation to locomotion training
- defaults or trims were hand-tuned per side at any point
- observations are expressed relative to a default pose
“观测里 joint_pos_rel = q − default。镜像下 q_l → −q_r,要让 (q−default)_l → −(q−default)_r 成立,必须 default_l = −default_r。default 不对称时做镜像增强会引入系统性错误,比不做还糟。… 位置误差与姿态矩阵误差实测均为 0.00e+00。”
train/RETRAIN_v2.md § 2. 前提:default 姿态必须先对称化(不是可选项) / 3. 镜像变换 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 soft joint-limit penalty charged the standing pose itself - the geometric-zero knee sat on its hard limit, so stand_v1 bent its knees to dodge 0.419 per step and leaned 4.1 deg forward; excluding the knee gave 0.24 deg
soft-limit-penalty-charges-nominal-poseBefore training, evaluate every penalty at the nominal pose; if a joint's soft limit sits inside the pose the task requires (a straight knee on its hard stop), exclude that joint from the soft-limit penalty and let the action clip enforce the hard limit.
Symptom
stand_v1 (retrained after the default pose moved to the CAD geometric zero and mirror augmentation was added) fixed left/right asymmetry (6.8 -> 0.0 deg) but settled at a 4.1 deg forward lean, where pure PD at the same default settled at 0.1 deg - the policy was actively pushing itself forward, which is exactly the real robot's failure direction.
Context
soft_joint_pos_limit_factor = 0.9 shrank the knee's soft limit to -/+0.1047 rad, while the geometric-zero default has the knee at q = 0, exactly on the hard limit. Standing in the nominal pose therefore paid 0.2094 x 2.0 = 0.419 per step in dof_pos_limits (alive earned only 0.5). The policy's way out was to bend the knees to -/+0.1013 rad, and the cost was the forward lean.
Change
stand_v1b: the knees excluded from dof_pos_limits (a straight knee IS the standing pose; the hard limit is still enforced by the action clip). No other change.
Outcome
stand_v1b: max tilt 0.3 deg, steady tilt 0.24 deg, asymmetry 0.1 deg, height 0.384 m - exactly nominal - with knees at -0.0007 / +0.0005 rad. It became the standing release used on the real robot, and later the standing side of the recovery switch. The same exclusion was carried into the recovery contract (knee at the clip in the standing pose) and the one-leg reward table.
Mechanism
A limit penalty whose soft boundary lies inside the nominal pose turns the nominal into a taxed state, and the policy buys its way out with whatever posture change is cheapest - here a knee bend paid for with lean.
Applies when
- a standing or default pose has a joint at or near its hard limit
- a policy settles in a small steady tilt that pure PD does not show
- soft-limit factors shrink limits uniformly across joints
“`soft_joint_pos_limit_factor=0.9` 把膝软限位内缩到 ∓0.1047,而几何零位 default **膝盖 q=0 正好压在硬限位上** ⇒ 站在标称姿态每步白扣 `0.2094 × 2.0 = 0.419` (alive 才 +0.5)。策略只能屈膝到 ∓0.1013 躲罚,代价是躯干前倾 —— 恰好是真机的 失效方向。 … 修掉"软限位罚标称姿态"后重训(`dof_pos_limits` 排除膝盖)。**前倾问题彻底消失** … 不再屈膝躲惩罚,高度正好落回标称 0.3840。”
train/README.md § 三期: 镜像对称增强 + 站立 v1 (2026-07-28) / stand_v1b (2026-07-28): 站立定版 Bracket a real-robot A/B with a repeated reference run - battery drain is the confound
battery-bracketed-real-abOrder hardware A/B sessions as A-B-A: repeat the first condition at the end, and void the comparison if the bracket runs disagree - never let battery or venue drift ride on the second condition.
Symptom
In a two-policy teleop A/B on hardware, the second policy is measured on a lower battery voltage than the first - a systematic bias that would be read as a policy difference.
Context
C2 real A/B (checkpoint 700 vs A800, same floor, same day) was scripted as 700 -> A800 -> 700-rerun, with the explicit note that a teleop session drains the pack and the trailing policy "naturally suffers".
Change
Protocol: run the reference policy first AND last; if the two reference runs differ noticeably, declare the whole session battery/floor-polluted and void the A/B ("结论作废重来"). Also log electricity per run.
Outcome
Called out as the round's only systematic confound, closed by one extra command ("这是本轮唯一的系统性混淆源，一条命令就能堵掉").
Mechanism
Battery voltage scales available torque, and torque loss hits behavior asymmetrically (see power-scale-hurts-nonforward-axes), so drain masquerades as policy regression; a head/tail reference pair converts the unobserved drift into a measured control.
Applies when
- comparing two policies or settings on hardware in one session
- any sequential hardware evaluation where the plant drifts (battery, temperature, floor wear)
“为什么要 700 复跑：一次遥控 session 下来电池会掉压，第二枚天然吃亏。头尾各跑一次 700，若两次 700 明显不同，说明这轮 A/B 被电量污染，结论作废重来。这是本轮唯一的系统性混淆源，一条命令就能堵掉。”
train/C_LADDER_RUN.md § 3c. A-3 真机 A/B（同一段地板、同一天、电量记账） Training at scale 0.4 is NOT the twin of deploying 0.5 at power 0.8 - the algebra matches, the learned policy does not
deploy-scaling-not-training-equivalentNever assume deploy-side scalings can be folded into training-time constants ("burning the crutch into training"): the learned optimum depends on the training-time authority, so treat such conversions as full experiments with pre-registered expectations and a sim2sim gate before any hardware.
Symptom
s1g (S1.6) trained from zero at action_scale 0.4 - meant as the "training twin" of the hardware-proven s1c-at-power-0.8 (0.8 x 0.5 = 0.4) - was all green in Isaac (zero falls, reward 117) yet scored 0/3 across all eight checkpoints and 0/20 at 20 seeds in the MuJoCo gate, falling forward at median 1.57 s with a 2.9x speed overshoot.
Context
The pre-registered expectation (survival gate should pass, since the conviction matrix showed s1c@0.8+delay2 all-survive) was cleanly falsified, and the harness was acquitted by controls: --delay 0 fell identically (not a delay fragility), check_contract all green, and s1c through the same harness survived 2/3. The verdict: "「s1c@0.8 = 0.4 训练孪生」的代数等价不成立" - a policy deployed with a derated output still LIVES in the 0.5 internal model it trained under (its value function, its expectations of its own authority), while a policy that starts training with reduced authority learns a different, clip-hugging gait with zero margin for plant differences ("部署端打折的策略活在 0.5 的内模里,训练起点收权限学出的是贴 clip 的 另一套步态,对 plant 差异零余量"). Result: the policy was withdrawn before hardware ("撤回——不上真机"), the lineage root moved back to the 0.5-contract s1c-5500, and this became the C ladder's cited fact-check ("s1g 是 0/20 证伪出局的那一代").
Change
The amplitude-surgery route abandoned; contract kept at scale 0.5; the deploy-side 0.8 crutch later retired on its own merits when the delay-complete s1e generation ran at full power.
Outcome
One training run bought a clean falsification of a plausible algebraic identity; no hardware time was spent on it because the sim2sim gate caught it.
Mechanism
Output scaling commutes with the network arithmetic but not with learning: the training-time scale shapes which gait solutions are reachable and how much clip headroom the optimum keeps. A derated mature policy retains the wide-authority solution executed softly; a from-zero narrow-authority policy finds a different optimum that saturates its smaller envelope - the two are not the same controller in different units.
Applies when
- proposing to move a deployment derating into a training constant
- a scaled-down contract policy hugs the action clip
- Isaac-green / cross-sim-zero results on a re-scaled lineage
“预注册 a) 证伪——Isaac 全绿(零摔/reward 117)但 MuJoCo --delay 2 八档 checkpoint 扫描全数 0/3、iter6500 20-seed 0/20 … 「s1c@0.8 = 0.4 训练孪生」的代数等价不成立: 部署端打折的策略活在 0.5 的内模里,训练起点收权限学出的是贴 clip 的另一套步态,对 plant 差异零余量。”
train/OMNI_V0_SPEC.md § 3. S1.6 判决(2026-08-07 验收) Changing the gait clock silently flipped a hardwired threshold's meaning - write derived constants as expressions
derived-constants-must-track-their-baseBefore changing any base parameter (clock, control rate, scale), enumerate every constant derived from it and every constant that must NOT change; convert derived literals into expressions of the base so the next change cannot silently flip a term's meaning.
Symptom
Slowing the clock 0.40 -> 0.50 s would have silently inverted the feet_air_time threshold's semantics: the 0.25 s threshold was hardwired, so at ct 0.40 the swing window (~0.20 s) sat below it (constant pressure to lengthen strides), while at ct 0.50 the window (~0.25 s) equals it - the term's meaning flips from "push longer" to "neutral" with no code error anywhere.
Context
The clock change audit walked every dependent quantity: most followed automatically (joint_pos_ref / clearance / contact_number cycle_time params, gait_phase observation, deploy/sim2sim/policy_io, export) - wiring confirmed, zero hand edits; the air_time threshold was the one hardwired constant, fixed by preserving the RATIO: 0.25 -> 0.3125 = 0.625 x ct, with the recommendation to commit it as the expression 0.625*ct "一劳永逸" (solved once and forever). The same audit also listed what must NOT follow the clock (50 Hz control rate, physics dt/decimation, 47-dim contract, action_latency absolute seconds, PD/torque limits) - the change's blast radius stated in both directions.
Change
feet_air_time threshold re-expressed as a fraction of cycle_time; auto-following vs must-not-change lists written into the spec for the clock migration.
Outcome
The clock migration (v10, repeated in v11) carried no silent semantic flips; the expression form removed the trap for every future clock change.
Mechanism
Constants derived from a base parameter encode a ratio at their birth; storing the evaluated number severs the dependency, so changing the base leaves stale semantics with no failing test. Expressions preserve the intent; and an explicit both-directions dependency list (follows / must-not-follow) is what makes a base-parameter change reviewable.
Applies when
- changing gait clock, control frequency, or units
- a reward threshold interacts with a phase/window duration
- config audit finds literals that encode ratios
“feet_air_time 阈值 0.25 是写死的,不跟 ct 走——0.40 时摆动窗 ~0.20s<0.25(恒拉长压力),0.50 时摆动窗 ~0.25s≈阈值(语义翻转)。按比例保原压力:0.25 → 0.3125(=0.625×ct;建议直接写成 0.625 * ct 表达式,一劳永逸)。”
train/WALK_V10_SPEC.md § 3. T —— 慢时钟 (训练侧必做一件) The 1.5 Hz step-frequency gate was retracted - the author had misread his own actuator data, and the limit fought pendulum dynamics
gate-threshold-retracted-frequencyEvery gate threshold must cite its measurement and survive a first-principles sanity check; when a gate keeps failing otherwise healthy behavior, re-derive the threshold from the raw data before enforcing it again - and retract wrong gates in writing.
Symptom
An acceptance criterion "step frequency <= 1.5 Hz" kept failing healthy policies (walk_v4 at 2.33 Hz), and an earlier attempt to force slower stepping (v3) had killed stepping altogether.
Context
The threshold had been derived from the author's own actuator frequency-response measurements - but re-reading the raw table showed the misread: amplitude ratio at 2.0 Hz is 0.88 (knee) / 0.83 (ankle), acceptable; the genuinely bad point was walk_v2's 3.8 Hz at 0.58. Mechanism check agreed: the leg as a compound pendulum (L ~ 0.30 m) has a natural frequency ~1.1 Hz, swing half-period 0.45 s - the observed 2.1-2.4 Hz sits near where the leg wants to swing, and forcing 1.5 Hz "是跟摆动动力学对着干" (fights the swing dynamics). The frequency definition itself was pinned by two independent methods (contact-event counting 2.39 Hz vs FFT 2.33 Hz, agreeing): reported numbers are cycle frequency = steps per leg per second.
Change
Gate retracted in writing: "步频 ≤1.5 Hz 应删除或放宽到 ≤2.5 Hz"; frequency definition standardized before entering any config.
Outcome
walk_v4/v6's 2.1-2.4 Hz reclassified from disease to normal; the v3 failure got its probable explanation (suppressing stepping to meet a wrong gate).
Mechanism
A gate is only as good as the measurement and the reading behind it; thresholds inherited from a misread plot become invisible design constraints that later training obeys at real cost. Cross-checking a threshold against first-principles dynamics (pendulum frequency) is a cheap way to catch such misreads.
Applies when
- an acceptance threshold repeatedly fails policies that look healthy
- thresholds were set from a single person's reading of raw data
- a forced compliance with a gate degrades the behavior it guards
“我当初依据自己测的执行器频响定的,但看错了区间。… 2.0~2.4 Hz 的幅值比 0.83~0.88 是可接受的;真正不行的是 walk_v2 的 3.8 Hz。… 腿按复摆算(L≈0.30 m)自然频率约 1.1 Hz … 把它压到 1.5 Hz 是跟摆动动力学对着干(walk_v3 把迈步压没了,可能正是这个原因)。”
train/WALK_DIAGNOSIS.md § ② 撤回"步频 ≤1.5 Hz"这条验收标准 —— 是我定错了 A frame-history observation under zero DR memorizes the trainer's plant fingerprint - the estimator must see variation to learn estimation
history-obs-needs-plant-variationIf the observation carries history (stacked frames, RNN), keep at least minimal plant variation (gain/latency jitter) on from the first iteration - "nominal first, robust later" is structurally invalid for estimator-bearing contracts.
Symptom
omni_s1 (fresh 215-dim contract with a 5-frame history window, trained with DR fully off): training all green, yet the MuJoCo gate scored 0/20 on all eight doors - falls within 2 s, seven checkpoints, not one transferred.
Context
The history window exists precisely to let the actor implicitly estimate line velocity and actuator dynamics (the actor is denied base_lin_vel by observation honesty). Under a constant plant that implicit estimator has nothing to estimate - it learns the trainer's exact response fingerprint instead, and any other simulator's micro-differences are out-of-distribution: "5 帧窗按设计就是隐式估计器, plant 恒定时它学到 Isaac 精确响应的指纹". The planned "nominal-first-robust-later" staging was declared STRUCTURALLY incompatible with history observations: "估计器要见过变化才学估计, 否则学背诵" (an estimator must see variation to learn estimation, otherwise it learns recitation). Honest confound note kept: this is mixed with "zero DR does not transfer, period" - but both attributions prescribe the same fix, so no control was run.
Change
S1.1: minimum actuator jitter turned on from day one - kp/kd +/-10%, latency 0-1 frame (friction/COM/mass still nominal, no push - those stay for the S2 ladder).
Outcome
Transfer restored: survival 0/20 -> 20/20, speed 19/20, foot distance 20/20 (remaining failures moved to gait quality, a different disease); the staging doctrine was amended - history-carrying contracts never train under a frozen plant.
Mechanism
A recurrent/history channel fits whatever temporal structure minimizes loss; with a deterministic plant the cheapest structure is the plant's own impulse-response signature, yielding features that are simulator-specific rather than physics-general. Plant variation forces the channel to carry state-estimation features that transfer.
Conflicts
Attribution is explicitly confounded with the simpler "zero DR never transfers" reading ("与「零 DR 本身就不迁移」混杂 … 两种归因处方相同, 不做对照") - the source chose not to spend a control run separating them.
Applies when
- adding frame stacking or recurrence to an actor observation
- a nominal-plant policy fails a cross-simulator gate within seconds
- planning DR staging for a new contract
“frame_hist × 零 DR = plant 指纹过拟合——5 帧窗按设计就是隐式估计器, plant 恒定时它学到 Isaac 精确响应的指纹, MuJoCo 的微小差异即 OOD, 2 s 内摔, 七个 checkpoint 无一迁移。「先标称后鲁棒」的分段与历史观测结构性冲突:估计器要见过变化才学估计,否则学背诵。”
train/OMNI_V0_SPEC.md § 3. S1.1 修订记录 ① When a contract default changes, old policies must run under a pinned legacy profile - a silent clock swap is out-of-distribution on hardware
legacy-profile-pinningTreat every trained policy as bound to the contract values of its training era: version the deployment profiles, pin old policies to their era's profile in every command, and never let a changed default silently apply to an old artifact.
Symptom
The walk profile's gait clock moved from 0.40 s to 0.50 s for new training, but versions v5-v9 were all trained at 0.40 s - running them under the updated default would silently feed a 25% slower phase clock to policies that never saw one.
Context
The re-test runbook hard-codes --policy-profile legacy_walk_040 into every command for the old versions, with the warning not to omit the flag: the mismatch is invisible (no error, no crash) but puts the policy out of distribution on hardware, where the same file had already documented that off-clock operation collapses gait quality.
Change
Deployment profiles versioned per training era; historical policies permanently associated with their era's profile; runbooks write the profile flag explicitly rather than relying on defaults.
Outcome
Old policies stayed runnable and comparable after the contract moved on; the silent-mismatch failure mode was closed by convention.
Mechanism
Changing a shared default rebinds every old artifact to a contract it was not trained under; unlike a schema break, a value change produces no error - only degraded, unexplainable behavior. Version-pinned profiles make the binding explicit and permanent.
Applies when
- changing any default in the deployment contract (clock, scales, gains) while old policies remain in use
- writing runbooks that mix policy generations
- a re-tested old policy behaves worse than its era's records
“2026-08-02 起 walk profile 的时钟改为 0.50(WALK_V10_SPEC §3)。v5~v9 全是 0.40 训的,本文件所有命令已改带 --policy-profile legacy_walk_040 ——不要省掉这个 flag,否则是拿慢 25% 的相位时钟静默喂旧策略(分布外,真机危险)。”
train/REAL_SWEEP_V5_V8.md § 1. 预检 ⚠️ 时钟改为 0.50 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 Multiple changes may share one rung only if their symptom spaces are orthogonal - with the ablation order written in advance
orthogonal-batch-with-ablation-orderBatch changes into one rung only when you can name each change's private symptom space in writing; pre-register the ablation order (numeric before structural) and per-change escalation plans, so a mixed outcome decomposes without new decisions.
Symptom
v8 needed four repairs at once (saturation cheating, landing impact, leg narrowing, heading alignment) - strict one-variable laddering would have cost four training cycles for wounds that were all already diagnosed.
Context
The batch was allowed because each change owns a disjoint symptom space, stated explicitly: "A→形态/饱和, B→落地/步态高度, C→腿距/roll 摇摆, D→偏航/转向" - so a single run can attribute each outcome to its change by which symptom moved. For the failure case, the ablation order was pre-registered (A2 -> A1 -> B -> D -> C, "先撤数 值改动" - retract numeric tweaks before structural ones), and every change carried its own escalation/rollback plan (e.g. A insufficient: joint_pos_ref 1.6->2.0 or widen the exp kernel; B overdone - robot afraid to land: v_ok 0.30->0.45; D unstable: rel 0.5->0.25, not back to 0).
Change
Four-change rung executed as one run with per-change symptom ownership, per-change contingency plans, and a pre-registered global ablation order for unattributable regressions.
Outcome
The rung retained single-run attributability without paying 4x training cost; the contingency table meant no failure mode would require improvising an ablation under pressure.
Mechanism
The one-variable rule exists to keep attribution possible, not as an end in itself; attribution survives batching exactly when the changes' observable effects are separable. Orthogonality is a claim that must be argued per pair in advance - and the pre-registered ablation order is the escape hatch for the case the claim fails.
Applies when
- several diagnosed fixes are queued and ladder time is scarce
- deciding between strict laddering and a combined rung
- a combined rung shows a regression no single change explains
“四个改动症状空间基本正交,可单 run 归因:A→形态/饱和,B→落地/步态高度,C→腿距/roll 摇摆,D→偏航/转向。出现无法归因的整体退化时消融顺序 A2→A1→B→D→C(先撤数值改动)。”
train/WALK_V8_SPEC.md § 8. 风险与归因 Push-test protocol - positive side first, fragile side spotted, axes aligned in the log, and cross-machine push counts stay qualitative
push-test-chirality-protocolOrder disturbance tests from the robust side to the fragile side with protection scaled to sim-measured asymmetry, align and log frame conventions before testing, and treat cross-domain disturbance counts as qualitative evidence only.
Symptom
Hand-push testing on hardware risked falls on a side sim had already flagged as fragile, and push counts invited apples-to-oranges comparison with sim numbers.
Context
Sim chirality was explicit: descendants were far more fragile in -y (fric-3000@kd1.2: +6 N*s survived 15/20 vs -6 N*s only 3-9/20) while the s1e control was perfectly symmetric (40/40). The protocol therefore: push the positive direction first, keep a spotter for the negative side; before any push, record which real-robot side corresponds to sim's +y in the log ("上机前对一次坐标"); and - citing the chaos lesson ("混沌课文") - real push results are used only as qualitative corroboration, never compared numerically with sim survival counts across machines.
Change
Push testing became a scripted, chirality-aware protocol with frame alignment as a logged precondition and an explicit epistemic limit on cross-domain count comparison.
Outcome
The fragile side was tested with protection informed by sim's quantified asymmetry; logs stayed interpretable because the frame correspondence was recorded before the first push.
Mechanism
Disturbance-response chirality is a real, quantifiable lineage property, so test order should follow measured fragility; and perturbation outcomes are chaotic in the details (divergent trajectories from tiny differences), so counts do not transfer across domains even when qualitative rankings do.
Applies when
- planning push/disturbance tests on hardware
- sim shows directional asymmetry in disturbance survival
- someone proposes comparing real push counts to sim counts
“先正向后负向, 负向留人扶 —— sim 手性明确: 后代在负 y 向显著更脆 (fric-3000 @kd1.2: +6 N·s 15/20 vs −6 N·s 3~9/20), 而 s1e@0.8 两向 40/40 完全对称。上机前对一次坐标 … 跨机不做二值结论 (混沌课文): 真机推力只作定性对照, 不与 sim 计数对比。”
train/REAL_RUN_S2.md § 3. 抗推 (可选, 人手推; 做则按此协议) A field experiment is allowed when it is pre-scripted, single-variable, and self-reversing (RAM-only writes)
reversible-single-variable-field-experimentsPermit hardware-side experiments only when scripted in advance with one variable, a log, and automatic reversion (volatile writes, git restore); keep every config mirror (yaml vs firmware) changed and restored as a unit.
Symptom
A hypothesis needed a hardware test - v5's "wild kicking" might trace to the RS00 torque cap being deployed at 11 N*m vs its trained 14 (-21%), on exactly the ankle-roll/hip-yaw joints doing lateral-yaw work - but changing limits in the field is the classic way to lose track of robot state.
Context
The experiment was written to be safe by construction: change exactly one number (robot.yaml RS00 tau_limit 11.0 -> 14.0), write to firmware RAM only (--write without --save, so a power cycle automatically rolls back to the saved 12/17/11), run the single logged trial, then restore both yaml (git checkout) and RAM immediately. The consistency requirement is explicit: the deploy tool's torque self-check compares against robot.yaml, so yaml and firmware must change and restore together; and the hypothesis scoping is itself single-variable - RS06's 67% cut was measured irrelevant (gait uses only 16% of rated) and hip torque was left alone for safety.
Change
Field experimentation policy refined: not "never touch hardware settings" but "only pre-scripted, one-variable, logged, auto-reverting changes with config/firmware kept consistent".
Outcome
The sub-experiment could answer the torque-cap hypothesis without any risk of the robot persisting in an undocumented state - forgetting to restore costs nothing but a git checkout.
Mechanism
The danger of field changes is state divergence (robot config drifting from the repo's record), not the change itself; volatile (RAM-only) writes bound the divergence lifetime to one power cycle, and single-variable scoping preserves attributability even in a field setting.
Applies when
- a hypothesis requires changing firmware limits or gains on the robot
- field debugging tempts persistent config writes
- designing safe escape hatches for deployment tooling
“程序(--write 不带 --save = 只写 RAM,断电自动回滚)… deploy 的限扭自检是对 robot.yaml 比对的,所以 yaml 和固件必须同改同还原;忘了还原也没事,断电重启即回 12/17/11(上次 --save 的值),但 yaml 要 git checkout。”
train/REAL_SWEEP_V5_V8.md § 4. 限扭子实验(可选二期,只对 v5,单变量) Add a termination that makes the degenerate strategy fatal - no height cut-off meant crouch-shuffling could live forever
termination-closes-degenerate-basinFor each known degenerate strategy, check whether the termination set makes it fatal; if the robot can live indefinitely inside the degenerate posture, add a termination just past the intended operating envelope rather than escalating penalties.
Symptom
Crouched foot-dragging survived indefinitely because the termination set contained only bad_orientation (40 deg) and base contact - there was no height termination at all, so a deep squat was a viable long-term strategy.
Context
The hypothesis audit found the missing termination (hypothesis 7); the cross-check against published configs found the field practice: Booster terminates at 0.45 m (38% of body height) and the research warning is that the termination height must not be so low that crouching survives it. The proposed value: 0.32 m, just below the walk crouch base height 0.3739 - a deep squat terminates immediately, "断掉蹲着蹭的活路" (cutting off the crouch-shuffle's livelihood).
Change
Add height termination at 0.32 m as a second-priority item of the walk fix package, alongside restoring base_height_l2 to -10.
Outcome
Entered the v5/v6 fix package under which the crouch-shuffle optimum disappeared (34 mm clearance, 87% tracking by v6).
Mechanism
Termination conditions define which strategies exist at all: a reward penalty prices a behavior, but a termination deletes its future returns entirely. Degenerate basins that are merely penalized can remain optimal under enough tracking pressure; a termination placed between the degenerate posture and the intended one makes the basin unreachable as a steady state.
Applies when
- a degenerate but stable behavior persists across reward tunings
- auditing termination conditions for a locomotion task
- a policy exploits the gap between penalized and terminated states
“加终止高度:研究第 6 条"终止高度不能低到让蹲着也能活"。我们完全没有高度终止。建议 0.32 m(略低于 walk 蹲姿基座高 0.3739,深蹲即终止)。… 加终止高度 0.32 m(深蹲即终止,断掉蹲着蹭的活路)”
train/WALK_DIAGNOSIS.md § 修正 ④ / 最终改动清单 第二优先