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
131 cards matching “get-up-feasibility-accounts-before-training”.
Calibrate a wall penalty by measuring healthy and sick policies - healthy pays ~0, the disease pays a wall
calibrate-threshold-between-healthy-and-sickCalibrate every threshold penalty by evaluating its exact formula on replays of at least one healthy and one sick policy: place the threshold between their distributions, size the weight so the sick policy pays a decisive fraction of tracking while the healthy one pays ~0, and pre-compute neighboring thresholds for cheap adjustment.
Symptom
Real walk_v7 occasionally clipped its own legs (stance narrowed to 133 mm mean vs nominal 214.5); a foot-distance penalty was needed, but an uncalibrated threshold/weight risked either doing nothing or becoming a reverse barrier.
Context
The term (relu(d_min - lateral foot distance), measured in the base yaw frame because world-frame y is meaningless after turning) was calibrated before training by replaying three known policies through the exact reward formula at the acceptance operating point: healthy v5 (183 mm) pays 0.6% of tracking - effectively free; narrowed v7 (133 mm) pays 22% - effective widening pressure; collapsed v8 (93 mm) pays 55% - a wall. d_min 0.16 was placed deliberately between healthy and sick, with alternative thresholds (0.14/0.18) pre-computed in the tool output for later adjustment. The shape self-check was named as a standing question: "先问'零代价的选项是什么'" - the zero-cost region must be exactly the desired behavior.
Change
feet_lateral_distance added at d_min 0.16 / weight -10, with telemetry expectation pre-registered (should decay toward 0 as stance learns >160 mm; if bow-legged over-widening >214 appears, only then discuss an upper bound).
Outcome
v9_probe on hardware: no leg contact ("没碰腿(N2 兑现)"), stance min 145/126 mm green; the term's zero-cost design left healthy gait untaxed.
Mechanism
A relu threshold penalty defines a free region and a priced region; its correctness is entirely in where the boundary sits relative to the healthy and pathological distributions. Replaying known-good and known-bad policies through the exact formula measures both distributions in the term's own currency, making the threshold and weight a placement decision instead of a guess.
Applies when
- adding any relu/threshold-style wall penalty
- a safety margin (foot distance, joint limit, clearance) needs enforcement without taxing normal behavior
- choosing between candidate thresholds for a new term
“形状自检 (v6 横向组/v8-B 的教训 —— 先问"零代价的选项是什么"): 标称站距付 0, 健康步态付 ~0, 收窄才付费 … v5(健康) 183 mm … 0.6% ≈ 免费 | v7(收窄) 133 mm … 22% —— 有效推宽 | v8(塌陷) 93 mm … 55% —— 墙 … d_min=0.16 恰在 v5(183)与 v7(133)之间”
train/WALK_V9_SPEC.md § 2. N2 —— 脚距惩罚(已完成权重预标定) Freeze the deployment contract, stamp every export, and let an automated checker catch wiring bugs
contract-freeze-and-checkerFreeze and fingerprint the policy I/O contract; ship contract changes as new versioned profiles that leave old artifacts bit-identical; and extend the automated contract checker with every pipeline change, forcing the new path to execute in the check.
Symptom
Contract-level changes (observation layout, action pipeline) are where silent sim/real divergence is born; two real wiring bugs appeared the one time the action pipeline was extended.
Context
The 215-dim observation contract was frozen ("纪元 3，三机 digest" - an era number plus a digest agreed across three machines); proposals that would break it (e.g. a GRU memory) were rejected on contract grounds. Every exported ONNX is stamped and verified with a manifest (onnx_manifest --stamp / --verify), and deployment refuses mismatched combinations. When C4 added the lateral feed-forward, it went in as a NEW profile (omni_ff) leaving the existing omni profile's behavior bit-identical; the checker (check_contract) was extended to force the feed-forward path to actually execute (cmd_vy=0.13) and promptly caught two genuine bugs: (1) re-clamping with soft_joint_pos_limits after the feed-forward (0.23 rad deviation) instead of reusing the parent's clip; (2) indexing processed actions by asset.joint_names instead of the action term's own contract-ordered _joint_names, which landed the feed-forward on the wrong joints (l_hip_yaw / r_ankle_pitch).
Change
Contract discipline as implemented: frozen dims + digest; manifest stamping and refusal; contract changes only via new versioned profiles; checker updated in the same commit as any pipeline change, with inputs chosen so new code paths are exercised.
Outcome
Both wiring bugs caught before any training or deployment ("两个都是 check_contract 当场抓出来的 —— 这次它值回票价"); old deployments provably unaffected by the new profile.
Mechanism
The contract is the only interface the policy and robot share; freezing plus fingerprinting makes divergence detectable, and an executable checker turns "the contract holds" from a belief into a test - but only if its inputs actually drive the new code path.
Applies when
- modifying the action or observation pipeline of a deployed policy
- exporting policies for hardware
- proposals that would change observation dims or history structure
“契约校验抓到的两个真错误（记账，别再犯）：1. 前馈后误用 soft_joint_pos_limits（URDF 限位 ×0.9）重钳 → 0.23 rad 偏差 … 2. 用 asset.joint_names 索引 _processed_actions → 前馈落到 l_hip_yaw/r_ankle_pitch 上 … 两个都是 check_contract 当场抓出来的 —— 这次它值回票价。”
train/C_LADDER_RUN.md § 3j. 契约级改动 / 契约校验抓到的两个真错误 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 Privileged signals (true velocity, foot force, foot height) go to the critic only
observation-honesty-critic-onlyTreat the actor observation vector as a hardware contract: every element must exist on the real robot with realistic noise; privileged simulator truths belong in the critic only.
Symptom
Policies trained on ground-truth base linear velocity work in sim and fail on hardware, where only a drifting IMU and encoders exist - the policy has learned to depend on a signal that does not survive deployment.
Context
Many open-source locomotion stacks feed simulator ground-truth linear velocity to the actor. The reference team refused: the real robot has no ground-truth velocity. Asymmetric actor-critic keeps the training benefit of privileged information without deploying the dependency.
Change
Route ground-truth velocity, foot contact forces, and foot heights to the critic only; the actor observes exclusively signals that exist on hardware (IMU-derived quantities, encoders, commands, previous actions).
Outcome
Recorded as adopted doctrine in Lucen's experience log; the trained actor's input contract matches what the real robot can actually produce.
Mechanism
The critic is discarded at deployment, so it may consume any privileged state to reduce value-estimation variance; the actor's observation set is a deployment contract - anything in it that hardware cannot supply (or supplies with different noise/drift) becomes a train/deploy distribution shift the policy was never trained to handle.
Applies when
- designing actor/critic observation spaces
- reviewing a config where the actor sees base_lin_vel or contact forces
- sim policy is strong but real robot drifts, oscillates, or falls without obvious actuator cause
“很多开源代码库把真值线速度喂给策略，Asimov 团队没有，因为真机上没有真值速度，只有会漂的 IMU 和编码器；用完美速度训练出来的策略会依赖它，然后在硬件上失效。真值速度、足底力、足高统统只给 critic”
Experience.md § 观测空间的诚实性 (line 7) The real robot's right-leg kicking was over-trained-delay times loop gain - irreducible pipeline latency is plant, model it fully from day one
pipeline-latency-is-plant-not-drMeasure the end-to-end action pipeline delay and build it into the nominal plant and every acceptance gate from day one; treat power/scale deratings that "fix" oscillation as gain-reduction crutches flagging an unmodeled delay, and expect higher-feedback-gain policies to be MORE delay-fragile.
Symptom
On hardware, s1c/s1d at action scale 1.0 always kicked wildly with the right leg (s1c only ran as SOTA at power 0.8; s1d only at 0.7) - while sim showed nothing under default evaluation.
Context
Sim reproduced the incident item by item once the real pipeline delay was injected: s1d@1.0 with --delay 1 fell at 10.2 s, --delay 2 at 5.2 s; s1c@1.0 stressed (r_hip_roll saturation 5 -> 16%; "右脚" = the policy's chirality makes the right leg its high-gain leg); and the combos that worked on hardware (s1c@0.8+delay2, s1d@0.7+delay2) all survived in sim. Mechanism: the real pipeline is ~1-2 ticks (BusWorker next-cycle pickup + CAN round trip) but S1.1 trained only to 1 tick - "超训延迟 × 全环路增益 = 振荡;衰减 = 压环路增益换稳定" (delay beyond training x full loop gain = oscillation; the power derating had been buying stability by compressing loop gain). s1d was MORE fragile than s1c because its yaw 3-layer stack had learned higher feedback gain - higher gain, lower delay tolerance. Three changes: latency DR widened to cover reality; acceptance gates and smoke runs moved permanently to --delay 2 ("门必须在真机条件下预测 真机"); and the doctrine written twice-paid: "不可约的管线属性(延迟、 限速)不是'随机化选项',是 plant 本体,第一天就该全额建模" - S1's nominal-then-robust staging falsified by hardware for the second time. The later s1e hardware run at power 1.0 (no kicking, normal force) closed the loop: "0.8 = 旧代拐杖" - the derating had been a crutch for the under-modeled delay, not a real requirement.
Change
Latency modeled as plant from day one of any lineage (measured 1-2 ticks covered, bridge-layer rate limits likewise modeled by default); every gate and smoke evaluation issued under --delay 2.
Outcome
Kicking reproduced, explained, and eliminated in the s1e generation at full scale and full power; the deploy-side crutches (0.7/0.8) retired for the new lineage.
Mechanism
Feedback oscillation onset is a product of loop gain and phase lag; a policy trained below the real delay learns gains that sit past the real stability margin, and any output derating masks it by scaling gain down. Since pipeline delay is deterministic hardware property - not an uncertainty - it belongs in the nominal plant, and every evaluation must include it or the gate predicts a robot that does not exist.
Applies when
- hardware oscillation/kicking that sim only reproduces with added delay
- a policy only runs on hardware at reduced power/scale
- defining what belongs in the nominal plant vs the DR list
“真实链路延迟 ~1~2 拍 … S1.1 只训到 1 拍——超训延迟 × 全环路增益 = 振荡;衰减 = 压环路增益换稳定。s1d 比 s1c 更脆 = yaw 三层栈学出更高反馈增益,增益越高延迟容忍越低。… 教训入账:S1「先标称后鲁棒」第二次被真机证伪——不可约的管线属性(延迟、限速)不是"随机化选项",是 plant 本体,第一天就该全额建模。”
train/OMNI_V0_SPEC.md § 3. S1.4(真机右脚乱踢事故强制) Every power cycle starts with the same read-only pre-flight - read the buses, check the torque limits against 12/17/11, verify the IMU axes, check the ports after any new USB device - and any reassembly re-measures the joint zeros
power-cycle-preflightStart every powered session with a fixed, read-only pre-flight - bus responses, torque limits equal to the simulated ones, IMU axes, device identities - and re-measure joint zeros after any mechanical reassembly before running a policy.
Symptom
Hardware state drifts between sessions in ways no policy can see: a motor that stops answering after a power cycle, a torque limit that differs from the one simulated, an IMU axis flipped, two USB devices swapping identities, a joint zero moved by reassembly.
Context
The runbook's session order before any policy runs: read every motor on both CAN buses without enabling them (the first command after every power cycle); set_torque --check, all twelve motors must read 12/17/11 N*m, and any difference is written back; imu_reader --verify-axes, where the operator tilts the robot forward and to the right and every check must pass before continuing; check_ports after plugging in any new USB device (the IMU and a CAN adapter once collided on USB identity). After re-mounting motors: read the buses, then re-measure the calibration offsets (three repeats, written back) - "skipping it means running everything on the wrong zero". Hanging checklists repeat the torque-limit check (the deploy script also self-checks at start).
Change
A fixed, read-only pre-flight run in the same order every session.
Outcome
The runbook records one earlier hardware check in the same spirit: all 12 motors' implied kp fell within 18.4-22.0 for a commanded 20, inside the kp randomization range used in training.
Mechanism
A policy transfers only if the plant matches the one it was evaluated on; the pre-flight turns silent hardware drift into a failed check before the robot moves.
Applies when
- the first command after powering a robot on
- after swapping adapters, cables or motors
- a policy that worked last session suddenly behaves differently
“python tools/set_torque.py --check # 12 颗应全对 12/17/11, 有 diff 就 --write … 插任何新 USB 设备后都先跑一次 check_ports.py(IMU 和 CANable 的 USB 身份撞过车) … python tools/calib_stance.py --repeat 3 --write # 重标 offset —— 8/9/10 重新装, 机械零位变了”
RL系统/FOLLOW THIS copy 2.md § WALK / STAND 每次开始前 / 换CAN / 装回后必做两件 A reward on a quantity the actor cannot observe teaches "produce less of it", never "correct it" - closed-loop correction needs an outer loop
reward-observability-limitBefore adding a reward, check the actor can observe (or infer) the quantity: unobservable-error rewards buy only average suppression - route correction tasks to an outer loop whose commands stay in distribution, and do not break a frozen contract to add an observation a deploy-side loop can supply.
Symptom
Heading kept drifting despite world-frame yaw rewards, and a reviewer proposed heading-error rewards - raising the question of what yaw shaping can even teach this actor.
Context
The adopted architectural verdict: the actor's 45-dim base observation cannot see accumulated heading at all - projected_gravity is invariant to rotation about the gravity axis, and omega_z is a rate, not an angle. World-frame yaw-rate rewards are therefore privileged shaping that can only teach "少产生旋转" (generate less rotation), never "偏了以后拉回原线" (pull back to the line after drifting) - the policy cannot represent the error it would need to correct. The S1 gate (<=5 deg / 10 s) demands exactly the former, so the stack is right for its gate; active heading correction is assigned to the deployment outer loop (--heading P-loop converting heading error into in-distribution wz commands) plus small-wz training - and the 215-dim contract is explicitly NOT extended with a heading observation ("契约不加 heading 观测,冻结不动"). The reviewer's companion bias hypothesis was adjudicated with data: drift is bimodal - a basin mechanism decides whether you leave (seeds vary +/-16-46 deg vs -385 to -391 deg), and once out, rotation direction is constant (weight chirality; candidate root: the phase clock always swings left first).
Change
Yaw shaping kept as rate-tracking (three-layer stack); heading correction owned by the deploy outer loop; contract frozen; the "which behaviors need an outer loop" question settled by observability analysis rather than reward tuning.
Outcome
Stopped a contract change and a futile reward direction; drift work split correctly into rate-suppression (trainable) and error correction (outer loop), consistent with the earlier measured 10x drift reduction from the deploy-side loop.
Mechanism
A policy can only condition on its observation sigma-algebra; rewards on functions outside it shift the marginal action distribution (open-loop average effects) but cannot create feedback on the unobserved variable. Whether to add an observation, an outer loop, or accept average-shaping is decided by the task's gate: suppression gates need shaping, correction gates need the variable in some loop's view.
Applies when
- adding rewards on accumulated/世界-frame quantities (heading, position)
- deciding between a new observation, an outer loop, and shaping
- a drift symptom persists across reward-weight changes
“actor 的 45 维基座观测不到累计航向(projected_gravity 对绕重力轴旋转不变,ωz 是速率不是角度)——世界系 yaw 奖励是特权塑形,只能教「少产生旋转」,不能教「偏了以后拉回原线」。… 主动纠偏闭环 = S3 把小 wz 进分布 + deploy --heading 外环 … 215 契约不加 heading 观测,冻结不动。”
train/OMNI_V0_SPEC.md § 3. 评审④判决(2026-08-06,S1.3 开训前) 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 sim veto needs real confirmation too - the worst sim cell was scheduled as the most informative hardware run
sim-veto-needs-real-confirmationNever let sim alone both condemn a purpose-built configuration and escape audit: spend one cheap, safeguarded hardware run on the condemned cell, pre-registering what agreement and disagreement would each imply about the proxy.
Symptom
The fric-2400@kd1.0 combination was sim's worst cell across the board (survival 17/20 - the only miss, mu0.4 1/20, push 103/160, zero-cmd 2/20), yet it was the only product specifically trained for the kd1.0 deployment gain - discarding it on sim evidence alone would leave the sim's own validity untested exactly where it mattered.
Context
The team had been burned in the other direction before ("Isaac 指标三次 零预警" - training-side metrics gave zero warning three times), so the symmetric rule was written: sim's rejection also needs hardware confirmation ("sim 判被支配 ≠ 真机被支配 … sim 的否决也要真机确认"). The run was pre-registered with a dual reading: real matches sim -> the S2f ladder closes and the fork root is settled; real clearly better than sim -> the MuJoCo proxy has a systematic bias in the kd1.0/low-margin region, "那比选型本身重要得多" - and every S2f sim acceptance would need re-scoring.
Change
The condemned configuration was kept on the hardware roster (last, spotted, minimal exposure) explicitly as a proxy-validation probe, not as a deployment candidate.
Outcome
Session design captured either result as progress: selection confirmed, or a proxy bias discovered that would re-price the whole ladder's verdicts.
Mechanism
Every sim verdict is a joint statement about the policy AND the proxy; cells where a policy was purpose-trained for the exact condition sim condemns are where proxy error is most likely and most costly. Testing the veto converts a selection decision into a calibration measurement of the evaluator itself.
Applies when
- sim rejects the configuration that targets the actual deployment condition
- the eval proxy's calibration has never been checked in that regime
- deciding which hardware runs are worth their risk
“但它也是唯一为 kd1.0 部署档专门训的产物 —— sim 判被支配 ≠ 真机被支配, 「Isaac 指标三次零预警」的教训反过来同样成立: sim 的否决也要真机确认。… 若真机明显好于 sim → MuJoCo 代理在 kd1.0/低裕度区有系统性偏差, 那比选型本身重要得多。”
train/REAL_RUN_S2.md § 上机名单 note / 2. sim 侧预注册预期 ⑤ 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) 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)