actionshift 1.0.0

Benchmark and research harness for manipulation policies adapting to hidden compositional action-interface contracts, with the DualABI controller.

ActionShift

What happens to a trained robot policy when the meaning of its action numbers silently changes — and can it recover on its own, without ever being told?

ActionShift is a benchmark and research harness for manipulation policies whose action interface changes without exposing the active contract to the agent. It freezes disjoint generalization splits and measures adaptation on real ManiSkill tasks (PickCube, PushCube, PullCube, StackCube).

One frozen PPO policy, three action contracts, real ManiSkill renders. Left: the clean interface — the policy grasps the cube. Middle: a hidden contract (channels permuted, signs flipped, scales changed) — the same policy acts confidently and wrongly. Right: the belief adapter spends 6 bounded, safe probe steps, identifies the contract, and then succeeds — no retraining, never told which contract is active.

TL;DR install (Python 3.11): pip install -e '.[dev]' then actionshift-selftest — a simulator-free plug-and-verify run. Full steps, plus the ManiSkill extras, in Install and quickstart.

Part of the Action-Interface pair

ActionShift is one half of a two-project attack on the hidden action-interface contract problem — when the meaning of a robot's action numbers is undocumented or silently changed. Its sibling, ActionABI, is the offline forensic half: it recovers an action-interface contract from logged trajectories after the fact and abstains when the evidence is insufficient. ActionShift is the online adaptation half: a benchmark that measures whether a policy can recover the same hidden contract on its own, in the loop, from a handful of bounded probes. The two share one contract grammar (permutation · sign · scale · target · frame · lag · gripper), and the coupling is real code, not just a theme: ActionABI's C++ evidence scorer runs as a live backend inside ActionShift's belief loop via a pybind11 fusion module, with verified numerical parity (see reports/cpp_fusion.md). Forensic recovery ↔ online adaptation, one shared grammar, one shared scorer. → ActionShift · ActionABI

Contents

• What is this?
• Who is this for?
• How it works
• How it performed (honesty-first)
• Install and quickstart
• actionshift-selftest — plug-and-verify
• Claims (graded SOTA ledger)
• Claim audit (adversarial red-team)
• Benchmark card (week-one falsification gate)
• Reproducing
• Experiment index
• Report index
• Related work & positioning
• Claim boundary and limits
• License and citation

What is this?

A learned manipulation policy emits a vector of numbers every control step. Whoever wired the robot decided what those numbers mean: which command channel drives which joint, with what sign, what gain, whether the value is a delta or an absolute target, in which reference frame, with what actuation delay, and which way the gripper opens. That mapping — the action contract — is usually undocumented, and it silently varies across robot stacks, datasets, and firmware. When a policy is deployed against a contract different from the one it learned, it does not error out: it acts confidently and wrongly, and success collapses to near zero.

ActionShift makes that hidden contract the explicit object of study. A software ABI re-maps the numbers a policy emits into controller behavior — composing permutation, sign, scale, target convention, reference frame, latency, and gripper semantics — and the benchmark measures how well a policy adapts when it is never told which contract is active. It holds the task dynamics fixed and varies only the interface, so any drop in success is attributable to the contract and nothing else.

DualABI is the included experimental controller. It maintains a structured belief over the action contract, conditions a task policy on that belief, proposes bounded candidate actions, rejects unsafe candidates with a hard mask, and scores survivors by task value, safety risk, and expected reduction in task regret. Entropy-seeking, no-information, no-safety, and exact-belief variants are explicit ablations rather than hidden switches.

Who is this for?

• Robot-learning researchers studying adaptation, system identification, sim-to-real transfer, or robustness — ActionShift gives frozen, hash-addressed splits, privileged oracles, preregistered promotion gates, and a matched-privilege method ladder over real ManiSkill backbones, so an adaptation method can be measured against an honest ceiling and floor rather than a hand-picked baseline.
• Dataset and robot-stack maintainers who need to know whether two robots that share a policy actually share an action contract — the companion actionshift-selftest packages the core finding as a bounded startup check: is this robot wired the way the policy thinks?
• Anyone deploying policies across robot stacks, where a swapped connector, a flipped driver sign, a changed controller mode, or an added latency turns a competent policy into a confidently-wrong one.

The scientific companion project ActionABI attacks the same problem from the data side: it forensically recovers an undocumented action contract from logged trajectories alone, with calibrated abstention. Its C++ scoring core is load-bearing inside ActionShift's belief loop.

How it works

Contract grammar. A contract composes seven field families over the action vector:

Field	Meaning
permutation	which policy channel drives which controller channel
sign	per-channel direction flip
scale	per-channel gain (grid-valued in the declared grammar)
target	delta vs absolute target convention
frame	reference frame (base vs tool)
lag	execution delay in control steps
gripper	open/close convention and sign

The mechanism (policy-agnostic adaptation). Every method is an adapter that sits between a frozen task backbone and the hidden-contract environment: the backbone emits a canonical command, the adapter turns it into the raw action the hidden wrapper executes, and the adapter learns from the calibrated response. Because adapters consume canonical actions, the same adapter rescues a frozen PPO policy and a frozen Diffusion Policy unchanged. Belief-family adapters spend a short, bounded, safe probe phase — a handful of amplitude-capped pulses (the gripper is never actuated) folded into an exact Bayesian belief over the declared contract pool — then act on the maximum-a-posteriori contract.

Splits. Five frozen, hash-addressed splits (configs/split/): seen, unseen_value, unseen_composition (zero contract- and composition-signature overlap with training), long_lag, and task_transfer. Materialized manifests make the disjointness test-enforced and reproducible.

Tasks. Four competence-gated ManiSkill tasks — PickCube-v1, PushCube-v1, PullCube-v1, StackCube-v1. PegInsertionSide-v1 is honestly excluded on backbone competence (0/100 at the full official 75M-step budget; see reports/peg_retry.md).

Method families.

• Privileged oracle — knows the true contract; the instantaneous ceiling.
• Pool-privileged belief (exact belief, fixed/entropy/random probes, DualABI) — shares a declared finite contract pool; adapts within an episode.
• Grammar-knowledge belief (full-grammar factorized belief, hold-probe excitation, scale corrector) — knows only the declared grammar, no pool.
• Unprivileged learned identifiers (passive UP-OSI-style, recurrent episode-length, probe-augmented) — no pool, no grammar enumeration; learn to identify from responses.
• Delay-aware augmented-state control (PPO/SAC) — solves the long-lag split by planning through delay rather than identifying the contract.
• No adaptation — sends policy output through the hidden contract unchanged; the floor.

How it performed (honesty-first)

Exact numbers and intervals are in reports/adaptation_tournament.md (rounds 1–13); the plots below quote those reports directly, and the honesty caveats travel with each headline. No claim of broad novelty or method superiority is made — the supported niche is the hidden-ABI compositional benchmark and its matched-privilege adaptation ladder. See the claim boundary and the graded Claims ledger.

The adaptation ladder — privilege buys success. On PickCube/seen, the same frozen PPO backbone goes from a hard 0.000 floor (no adaptation, and the unprivileged passive learned identifier) up through a grammar-only factorized belief (0.458) to a pool-privileged belief (0.987) that reaches the privileged oracle ceiling (1.000). The gap between the learned identifiers and the belief family is the load-bearing negative result: active probing is not a substitute for hypothesis-space knowledge.

DualABI wins on probe efficiency, not raw success. It matches the entropy champion on success on every instantaneous cell while using ~52% fewer probe steps (mean 2.89 vs 6.00) and ~42% less probe displacement — a Pareto win that replicates across four tasks. It does not beat entropy on raw success anywhere (all paired 95% intervals include zero), and the week-one proxy negative (DualABI < fixed pulses) stands on record. (Rounds 1, 2)

The long-lag split is solvable by composition, not identification. A delay-aware augmented-state PPO backbone lifts Pick/long-lag from a frozen-oracle 0.027 to 0.528 [0.488, 0.568] (≈20×) and Push from 0.153 to 0.415 [0.376, 0.455] (≈2.7×) — identification alone cannot fix delay, delay-aware control alone cannot fix hidden semantics, the combination solves both. This is a new backbone, not the frozen tournament backbone; report both ratios, not just Pick. (Round 4)

Brittleness is a property of the interface, not the learning paradigm. A competent frozen Diffusion Policy collapses from ~0.58 to ~0.003 under the same hidden contracts that zero PPO — the same 0.000–0.007 floor — and the same policy-agnostic belief adapters restore both to their oracle ceilings. The failure is interface-semantic, not policy-class-specific. (Round 12)

Other load-bearing findings (all in reports/).

• Information-seeking helps, within a matched-privilege comparison. Entropy-guided probing beats fixed probing by +5.8 points [+3.7, +8.2] on Pick/seen (paired 95% bootstrap, 3 seeds, 600 paired episodes) — clearing the preregistered 5-point promotion gate with no safety/cost regression — and replicates on a second task. It does not generalize to all tasks: entropy is the worst belief-family method on StackCube/seen. (Rounds 1, 5)
• Negative results are load-bearing. Passive learned system-ID collapses to ~0.0 end-to-end under the weak real response model; recurrent accumulation lifts discrete fields but continuous map estimation plateaus at chance; probe-augmented learning caps permutation at 0.39. (Rounds 1, 2, 6)
• The absolute-mode wall is breached. Hold-probe excitation takes dead all-absolute cells from 0.005 to 0.722 [0.684, 0.756] (Push) and 0.000 to 0.790 [0.756, 0.821] (Pick), with exact permutation recovery and causal controls. (Round 11)
• External anchors are honest. The augmented-state recipe is delay-robust and baseline-tier on DCAC's own MuJoCo delay benchmark (PPO and SAC), but makes no absolute-return claim vs DC/AC. (Rounds 10, 13)

Install and quickstart

Python 3.11 and PyTorch 2.4+ are required. ManiSkill is optional for unit tests but required for the real simulator smokes.

python -m venv .venv
.venv/bin/pip install -e '.[dev,maniskill]'

# static gates
.venv/bin/ruff check src tests experiments
.venv/bin/mypy src
.venv/bin/pytest -q

# validate a frozen split and materialize the evaluation matrix (one example slice)
.venv/bin/actionshift-validate split configs/split/unseen_composition.yaml
.venv/bin/actionshift-evaluate matrix experiments/manifests/headline.jsonl

# plug-and-verify self-test: "is this robot wired the way the policy thinks?"
.venv/bin/actionshift-selftest --demo swapped-axes --json

# real ManiSkill integration smoke (requires the maniskill extra)
.venv/bin/actionshift-smoke --sim-backend cpu --output reports/maniskill_cpu_smoke.json

# regenerate the README media (real renders + report-derived plots)
CUDA_VISIBLE_DEVICES=0 .venv/bin/python media/make_media.py all   # hero + lag GIFs + selftest panel
.venv/bin/python media/make_plots.py                              # the report-number plots + diagram

Console entry points: actionshift-validate, actionshift-evaluate, actionshift-smoke, actionshift-sprint, actionshift-selftest.

actionshift-selftest — plug-and-verify

Is this robot wired the way the policy thinks?

A learned policy assumes a particular action contract: which command channel drives which joint, with what sign, scale, and timing. If the robot is wired differently — two axes swapped on a connector, a sign flipped in a driver, a controller lagging — the policy will act confidently and wrongly. actionshift-selftest is a bounded startup check that catches that mismatch before you hand control to the policy. It packages the pair's core finding (a short probe phase identifies a hidden action contract; see reports/adaptation_tournament.md) as a practical self-test with a fail-closed verdict and a meaningful exit code.

30-second quickstart

pip install -e .          # registers the console script

# Demo: prove the tool CATCHES a miswiring (no robot, no GPU needed)
actionshift-selftest --demo miswired
#  -> VERDICT: MISMATCH (exit 1): channels 0 and 1 SWAPPED; channel 3 sign FLIPPED

actionshift-selftest --demo identity        # -> PASS (exit 0)
actionshift-selftest --demo unmodeled       # -> INCONCLUSIVE (exit 2)  (abstains, never guesses)

# Real ManiSkill task (auto-runs response calibration if missing):
actionshift-selftest --real --task pick_cube --expected identity

Exit codes are scriptable: 0 = PASS, 1 = MISMATCH, 2 = INCONCLUSIVE.

What it does

1. Probe phase (bounded, safe). Sends --budget (default 6) bounded raw pulses (|action| <= --amplitude, default 0.5; the gripper is never actuated) and folds each observed response into an exact Bayesian belief over a declared pool of plausible wirings. Two probe schedules: --strategy entropy (default; picks the most informative pulse) or fixed (cycles the pose channels).
2. Identify. Reports the maximum-a-posteriori wiring with per-field confidence (the marginal posterior mass on each field's value).
3. Verdict against the wiring the policy --expecteds (default: identity):
   • PASS — every observable field is resolved with high confidence and matches expected.
   • MISMATCH — a field is confidently resolved and differs; the exact diff is printed (e.g. channels 0 and 1 SWAPPED; channel 3 sign FLIPPED).
   • INCONCLUSIVE — the belief did not concentrate, or the best hypothesis fits the responses poorly (the true wiring is likely outside the declared pool). The tool abstains rather than guesses.
4. Probe-safety statement — prints the amplitude bound and an estimated end-effector displacement incurred during probing.

What it checks

Within its declared pool of wirings, the tool resolves and verifies these observable fields:

Field	Example miswiring caught
permutation	two command channels swapped
sign	a channel's direction flipped
scale	a channel's gain wrong (under delta control)
target	delta vs. absolute command encoding
frame	base vs. tool frame (under a non-identity rotation)
lag	actuation delay of N control steps

What it CANNOT check (honest limits)

These are structural limits of a bounded pose probe, not tuning knobs. They come straight from the pair's measured results (reports/adaptation_tournament.md, the Claims section) and the tool is built to abstain, never bluff, on them:

• Gripper direction (gripper_inverted) — out of scope, always "not checked." The probe never actuates the gripper (a safety choice), so a pose probe carries no evidence about gripper polarity. Verifying it needs a gripper channel and a gripper probe (a v2 calibration extension).
• Exact scale under absolute-target control. Scale is non-identifiable under the weak pd_ee_delta_pose response with absolute-target encoding (the pair's single open challenge); scale confidence stays low and the verdict abstains.
• Lag correction. The tool identifies lag but does not correct for it — that requires a delay-aware backbone (reports/adaptation_delay_aware.md), not a probe.
• Tool frame under identity rotation. With an identity end-effector rotation (the default), base and tool frames are indistinguishable; the default pool is therefore base-frame only.
• Wirings outside the declared pool. A wiring it never modeled triggers the misspecification guard (INCONCLUSIVE) — it is not silently mapped onto the nearest pool member as a false verdict.
• Real-robot short-window limit. On the real sim, the weak controller response means a 6-step probe usually abstains (fail-closed) rather than concentrating; raise --budget for real hardware. Synthetic/demo mode uses the calibrated response model directly, so the minimal 6-step budget suffices.

Options

--task            ManiSkill task key (default: pick_cube)
--demo NAME       inject a named demo wiring as the hidden contract (synthetic):
                  identity, swapped-axes, sign-flip, miswired, scaled, reversed, lagged, unmodeled
--hidden-contract inject an arbitrary hidden contract (library name | JSON | path)
--expected        the wiring the policy expects (library name | JSON | path; default: identity)
--strategy        fixed | entropy   (default: entropy)
--budget          probe steps (default: 6)
--amplitude       probe amplitude bound (default: 0.5)
--num-envs        parallel probe environments (default: 8)
--confidence-floor  min per-field posterior to trust a field (default: 0.9)
--misspec-ratio     max fit residual before abstaining (default: 4.0)
--real            probe a real ManiSkill environment (auto-calibrates if missing)
--json            emit machine-readable JSON

Claims (graded SOTA ledger)

Sim-only, no hardware. Every claim follows the project's "we are not aware of X" convention — no "first" claims. Grading is deliberately harsh: a claim a hostile reviewer could destroy with one counter-example from the project's own reports is downgraded to STRETCH or cut. Sources: the reports/*.md files, the Related work section, and a websearch pass on the ManiSkill3 official PPO baseline configs.

Claim 1. DualABI is a probe-efficiency Pareto win over the tournament's best prober — STRONG

Claim: On the matched-privilege belief-family tournament, DualABI (task-regret-aware probe selection with early stopping) matches the top prober's success rate while spending roughly half the probe budget and displacement, replicating across every task tested.

Evidence: Pick/seen: DualABI 0.983 vs entropy_probes 0.987 (Δ −0.003 [−0.017,+0.010] ns) at 2.78 vs 6.00 probe steps; Push/seen 1.000 vs 1.000 at 2.60 vs 6.00 steps; over 4 instantaneous cells (Pick/Push × seen/unseen), mean probe steps 2.89 vs 6.00 (−52%), mean displacement −42%, mean success tied (0.990 vs 0.987) — reports/adaptation_dualabi.md. Replicates on PullCube (2.5–2.9 vs 6.0 steps) — reports/third_task.md — and StackCube (2.9/3.5 vs 6.0 steps) — reports/fourth_task.md. 4 of 4 competent tasks.

External comparator: No comparable published "N probes out of a budget → identification accuracy" curve exists after checking ASID (qualitative "one rollout suffices" only) and Dynamics-as-Prompts (accuracy gains, no steps-budget curve). Verified empty niche, not a beat: state as "no external number exists to compare against."

Caveats: Matched-privilege only (every belief-family method shares the same declared 9-contract pool, budget, amplitude, backbone, seed) — a within-family efficiency result, not an unprivileged-adaptation claim. Does not beat entropy on raw success anywhere (all paired 95% intervals include zero). The threshold was calibrated on a 48-episode pilot then frozen (robust over τ ∈ [0.4, 1.5]). Long-lag was evaluated later (Study B, reports/lag_completions.md) and DualABI collapses under lag on the frozen backbone like the rest of the belief family (0.02/0.11) — do not extend the Pareto claim to lag. Sim-only, pd_ee_delta_pose, identity-rotation-wrapper unless stated otherwise.

Claim 2. Entropy-guided active probing beats fixed probing on identification quality — MODERATE

Claim: Under matched privilege, entropy-guided probe selection clears the preregistered 5-percentage-point promotion margin over fixed scripted probing on some but not all tasks, with no safety-cost regression where it clears.

Evidence: Pick/seen +5.8 [+3.7, +8.2] (3-seed, 600 paired episodes) — the preregistered gate-clear, reports/adaptation_tournament.md round 1. Replicates significantly on PullCube/unseen-composition +5.5 [+3.3, +7.8] — round 5 / reports/third_task.md. Direction only (below the 5-point margin, not significant) on Push/seen (+1.8 [+0.8,+3.0]).

Caveats — the reason this is downgraded from STRONG: the ordering does not replicate on StackCube: entropy is the lowest-scoring belief-family method on seen (0.910, below fixed/passive/ DualABI at 0.945) and does not clear the promotion gate on unseen (+2.5 ns) — reports/fourth_task.md. On PullCube the companion "> random" tail also inverts (random_probes highest, 0.997) — reports/third_task.md. Honest statement: "clears the promotion gate on 2 of 4 tasks (Pick/seen, Pull/unseen), replicates in direction on a 3rd (Push/seen), and is the worst belief-family method on the 4th (Stack)" — a genuine, disclosed, task-dependent result, not a general law.

Claim 3. The long-lag split is solvable with delay-aware training — STRONG (internal claim)

Claim: A delay-aware augmented-state PPO backbone (observation augmented with the last 4 canonical actions, trained under randomized per-episode lag {0,1,2,4}) recovers substantial task success on the long-lag split where the frozen reactive backbone — oracle included — collapses to near zero.

Evidence (3-seed, 600 episodes, Wilson 95% intervals, every interval strictly above its frozen-backbone reference): Pick oracle-encode 0.528 [0.488,0.568] vs frozen oracle 0.027 (≈20×); Push oracle-encode 0.415 [0.376,0.455] vs frozen oracle 0.153 (≈2.7×); Pick exact-belief 0.360 vs frozen 0.022; Push exact-belief 0.387 vs frozen 0.117. Seen-split competence: Pick 0.715, Push 0.990, both clear the 0.5 floor — reports/adaptation_delay_aware.md.

Caveats: report both ratios (~20× Pick, ~2.7× Push) — leading with only Pick is a cherry-pick. This is a new backbone, not the frozen Gate 0/1 tournament backbone — not a like-for-like method contest against the frozen-backbone tournament rows. Labeled "delay-aware augmented-state PPO (local)" — the classical Katsikopoulos/Walsh state-augmentation reduction — not DCAC or D-TRPO. Lag 4 remains hardest (0.27–0.40). On StackCube even the oracle collapses to 0.000 under lag (reports/fourth_task.md) — the long-lag solution is demonstrated on Pick and Push only.

Claim 4. Not aware of prior published randomized action-delay online RL on manipulation — MODERATE (empty niche, heavily hedged)

Claim: We are not aware of prior published work studying randomized per-episode action-delay-robust online RL specifically on ManiSkill-style manipulation tasks.

Evidence for the search: 9+ distinct query phrasings. Three genuine near-misses must be cited, not omitted: (1) arXiv:2509.20869 — Meta-World manipulation under random observation delay (not action), benchmarks DCAC as a baseline and reports it degrading outside its trained delay range; (2) arXiv:2506.00131 — D4RL Adroit dexterous-hand tasks under delay, but offline RL, not online PPO; (3) arXiv:2605.15480 — Franka Panda under stochastic communication delay, a teleoperation framing, not randomized action-lag PPO.

Caveats: This is an "EMPTY-NICHE (hedged, not clean)" verdict, not "CLAIMABLE-NOW" — a flat "nothing exists" statement is not defensible. Use the exact hedged sentence: "We are not aware of prior published results on randomized per-episode action-delay-robust online RL specifically on ManiSkill-style manipulation tasks; the closest related work studies observation delay on Meta-World... or offline-RL delay on Adroit manipulation..., neither of which matches our action-lag/online-PPO/ManiSkill3 setting." Do not call these "the first strong delayed-manipulation numbers" without immediately citing all three near-misses in the same paragraph.

Claim 5. No prior benchmark isolates compositional action-interface shift — STRONG

Claim: We are not aware of a prior manipulation benchmark that holds task dynamics fixed and varies a compositional, discrete action-interface contract (permutation, sign, scale, target convention, frame, lag, gripper convention) as the object of study, with privileged oracles and preregistered promotion gates.

Evidence / external comparator (4 benchmarks independently checked and ruled out): RoboHiMan/HiMan-Bench (arXiv:2510.13149, compositionality over chained skills/subgoals, not controller/frame), ATOM-Bench (arXiv:2606.16826, atomic-skill composition), LIBERO-Plus (arXiv:2510.13626, 7 axes — object layout/camera/pose/language/lighting/background/sensor-noise, all visual/scene), and COLOSSEUM (arXiv:2402.08191, 14 axes, all visual/physical). Reflective VLA (arXiv:2606.25215) is the closest near-miss and must be cited and distinguished: a single continuous additive calibration-shift bias axis, not a compositional discrete grammar, and not structured as a benchmark with splits/oracles/promotion gates.

Caveats: Absence-of-evidence framing only ("we are not aware of," never "first"). Cite Reflective VLA explicitly as the nearest neighbor in any related-work paragraph — omitting it is the single biggest catchable gap.

Claim 6. The frame/coordinate-convention axis is genuinely identifiable and non-degenerate — MODERATE

Claim: ActionShift v2 wires a live (non-identity) end-effector rotation into the frame axis, converting it from an observationally-free axis (v1: no_adapt on a tool contract scored 1.000, identical to base) into a real oracle-vs-no-adapt gap (v2: oracle 1.000 vs no_adapt 0.000), and no other checked benchmark studies a controller-identity/coordinate-frame axis at all (same 4-benchmark check as Claim 5).

Evidence: reports/rotation_v2.md — parity table: oracle 1.000/no_adapt 0.000 under real rotation on both Pick and Push; the pool belief identifies the tool-frame hypothesis end-to-end (0.900–0.997 across 3-seed pooled cells).

Caveats: This is a rigor/correctness fix, not a new headline result — it makes an existing axis of Claim 5's grammar non-vacuous. A disclosed limitation travels with it: the full-grammar factorized (unprivileged, grammar-only) belief cannot represent a tool-frame hypothesis under real rotation (rotation couples channels, breaking the per-channel factorization) — frame identification under v2 currently requires either the 9-contract pool privilege or a richer-than-factorized evidence model. Do not claim the frame axis is solved in general; claim only that it is real and identifiable under privilege.

Claim 7. The unprivileged-identification challenge is quantified, not just failed — STRONG (honest negative)

Claim: No unprivileged learned method (passive, episode-length-recurrent, or probe-augmented) succeeds at hidden-contract identification, and the failure is localized to a specific, dimensionally-motivated cause: bounded probe budgets that let a 9-hypothesis belief score near-perfectly are insufficient in sample rank for a learned method to estimate the full continuous permutation/sign/scale map.

Evidence: Passive UP-OSI-style: 0.0 end-to-end, permutation 0.29, sign at chance (reports/adaptation_tournament.md round 1). Episode-length recurrent: still 0.000/0.010 end-to-end; lifts discrete fields (lag 0.75→0.92, target 0.59→0.69) but permutation plateaus ~0.35, sign at chance (reports/adaptation_recurrent.md). Probe-augmented learned identification: still 0.000–0.015 end-to-end; permutation caps at 0.39 (below even the random-excitation ablation's 0.52), sign never leaves chance (reports/adaptation_probe_osi.md). Mechanism: 6 basis pulses span ≤6 regressor directions against a 12-dimensional (lagged) per-channel regression problem — underdetermined by design, not by insufficient tuning.

Caveats: Report as a quantified negative. Single-task (mostly PickCube/PushCube), single backbone per method; do not generalize the exact numbers (0.39, 0.52, 0.35) beyond the reported tasks/methods.

Claim 8. ManiSkill official-baseline budget efficiency — STRETCH — do not lead with this

Claim (heavily qualified): Our PPO backbones reach ceiling (success_once 1.00) on PickCube/PushCube/PullCube and near-ceiling (0.98) on StackCube at step budgets that are a fraction of ManiSkill's own official baselines.sh budgets for the same tasks.

Evidence (internal): Pick 1.00 @ 10M steps, Push 1.00 @ 2M, Pull 1.00 @ 2M, Stack 0.98 @ 25M (reports/gate1.md-family, reports/third_task.md, reports/fourth_task.md).

External comparator — budget context: the true official examples/baselines/ppo/baselines.sh state-mode budgets are PickCube-v1 50M, PushCube-v1 50M, PullCube-v1 50M, StackCube-v1 50M (all num_envs=4096), and PegInsertionSide-v1 75M (num_envs=2048), fetched directly from the ManiSkill repo. The exact success numbers at those budgets could not be retrieved: the W&B report (api.wandb.ai/links/stonet2000/k6lz966q) is JS-rendered and returned no numeric content to repeated WebFetch. The ManiSkill3 paper (arXiv:2410.00425) states qualitatively only that "PickCube... reaches near 100% success rate after about 1 minute of training" and publishes no results table, directing readers to the W&B dashboard. PullCube-v1 is not in ManiSkill's curated "standard benchmark" task list, so no official number exists to compare its 1.000/0.998 competence against.

Why this is STRETCH, not CLAIMABLE-NOW:

1. No exact official number was obtained — "matches/exceeds" cannot be verified, only "trains at a smaller budget fraction," a materially weaker statement.
2. Control-mode mismatch. The official ppo.py default is --control_mode=pd_joint_delta_pos. ActionShift requires pd_ee_delta_pose for its shared controller. Joint-space and Cartesian-EE-space PPO have different sample efficiency; a budget-fraction comparison across control modes is not apples-to-apples.
3. num_envs mismatch. baselines.sh uses num_envs=4096; internal Gate-0/1 runs use num_envs=1024 — different parallelism changes both wall-clock and sample-efficiency framing.
4. StackCube budget-provenance error (corrected): reports/fourth_task.md originally framed its 25M-step run as "the official StackCube-v1 configuration verbatim from examples.sh." The actual official baselines.sh StackCube-v1 budget is 50M steps at num_envs=4096, not 25M at num_envs=1024 — the same error class as the earlier Peg 250M→75M mistake (examples.sh carries an explicit "not for official baseline results" disclaimer). The report now carries the correction.

Recommendation: do not publish a "matches/exceeds ManiSkill's official baseline" claim. Defensible, narrow statement: "our backbones reach ceiling success at step budgets smaller than ManiSkill's official baselines.sh schedule for the same tasks, though this is confounded by a different (harder, Cartesian-EE-space) control mode and different parallelism, and we could not independently verify the official success numbers at those budgets."

DO NOT CLAIM (ActionShift)

• Do not claim ActionShift beats or reproduces DCAC. No DCAC comparison beats absolute return; DCAC's own domain is Gym-MuJoCo locomotion, not ManiSkill manipulation — a domain mismatch that weakens relevance regardless of outcome.
• Do not claim a Vintix reproduction or comparison result. The transformer-ICL row is adjudicated unfaithful and excluded with evidence (reports/transformer_icl_adjudication.md). Any future run must be labeled "Vintix-derived in-context adapter (local, adapted)," never "Vintix."
• Do not claim any hardware or real-robot result. ActionShift is sim-only (ManiSkill GPU sim).
• Do not claim ActionShift "matches or exceeds" ManiSkill's official PPO baseline numbers. Exact numbers were not obtainable; a control-mode mismatch and a num_envs mismatch further confound any budget-fraction comparison. See Claim 8 (STRETCH).
• Do not repeat the "StackCube ran at the official 25M-step budget" framing without correction. The true official baselines.sh budget is 50M steps at num_envs=4096.
• Do not claim Peg is unsolvable by PPO in general. Only that it did not clear the 0.20 floor at or beyond its corrected official 75M-step budget, under this config, in a segmented run (reports/peg_retry.md).
• Do not claim the "entropy beats fixed probing" ordering, the "fixed-probe pipeline-flush beats the oracle under lag" finding, or the "active probing beats passive belief" tail generalize across tasks. All three are task-specific: entropy is the worst method on StackCube/seen; pipeline-flush is Push-specific and inverts on PullCube (fixed becomes the worst, reports/lag_completions.md Study C); the >random tail inverts on PullCube/unseen.
• Do not claim DualABI beats entropy on raw success. It ties (all paired 95% intervals include zero); the claim is cost-efficiency only.
• Do not claim the gripper wall or scale wall are "solved." Gripper is "substantially closed" (0.00→0.54 on the killer sub-cell) with a residual gap from scale leaking in; scale under absolute-target control is "unmoved" even with a synthetically-proven-correct drift corrector, because on real absolute contracts discrete permutation/target identification collapses before scale becomes reachable (reports/adaptation_scale_corrector.md).
• Do not claim the frame axis is solved without privilege. The unprivileged grammar-knowledge-only factorized belief structurally cannot represent a tool-frame hypothesis under real rotation.
• Do not claim "action-interface shift" is a formally/theoretically distinct RL problem class from dynamics-parameter shift. The only supported statement is empirical: a UP-OSI-style passive identifier transfers to 0.0 success on this benchmark's discrete/compositional hidden variable — an observed transfer failure, not an a priori theoretical claim.
• No "first" claims anywhere — always "we are not aware of X."

External anchors resolved (post-battery)

• DCAC external run — RESOLVED: augmented-state PPO is delay-robust on DCAC's delay-5 setup (40–69% undelayed retention; naive is near-random) but NOT competitive on absolute return vs DC/AC or their augmented SAC at 1M (on-policy/off-policy gap; documented). CLAIM the mechanism-robustness sentence only; DO NOT claim absolute-return competitiveness. DCAC publishes curves, not tables; same-hardware DCAC run blocked by mujoco_py build. Source: reports/delayed_rl_external.md.
• SAC external shot — RESOLVED: augmented-state SAC reaches DCAC's augmented-SAC/RTAC baseline tier on their delay-5 setup (matches HalfCheetah/Ant, ~half Walker2d) and reproduces the naive-collapse finding; it does NOT reach DC/AC itself. GRADE: MODERATE external anchor ("baseline-tier competitive on the established delayed-RL benchmark"), NOT a SOTA. Source: reports/delayed_rl_sac.md.
• Vintix ICL adjudication — RESOLVED as structured exclusion (three-ground unfaithfulness, pinned commit): the transformer-ICL row stays excluded with evidence; no ICL claim either way. Source: reports/transformer_icl_adjudication.md.

Pending (recommended, not yet executed)

• Direct DCAC comparison on delayed Gym-MuJoCo (github.com/rmst/rlrd, constant delay-5) would replace the internal-only long-lag claim with an external number — but DCAC's locomotion domain ≠ ManiSkill manipulation, so a win there does not transfer to "beats DCAC on manipulation."
• Vintix-derived in-context adapter as an unprivileged baseline (Vintix/Vintix-II have real code + manipulation-suite exposure) — likely another honest negative joining the ~0.0 unprivileged tier; must be labeled "Vintix-derived (local, adapted)."
• Manual W&B dashboard read — Claim 8 cannot be strengthened past STRETCH until someone opens api.wandb.ai/links/stonet2000/k6lz966q in a real browser and transcribes exact success numbers.

Claim audit (adversarial red-team)

This ledger separates implemented capability from experimental evidence. "Implemented" means code and tests exist; it does not mean the scientific hypothesis succeeded.

Candidate claim	Evidence now	Red-team verdict
Contracts can compose permutation, sign, scale, target, frame, lag, and gripper semantics.	Algebra, inverse, reset, lag, and wrapper tests.	Supported within the declared grammar; not arbitrary interfaces.
Frozen splits measure compositional generalization.	Materialized hashed manifests; unseen-composition has zero item and signature overlap.	Supported as a benchmark property; performance is not yet measured.
Oracle encoding is invariant to a known contract.	1,000-step integrator test and nonidentity real ManiSkill CPU/GPU smokes on three tasks.	Supported for tested contracts and simulator path.
Belief adaptation is possible.	Exact filter convergence and learned factorized-belief tests; controlled gate posterior near one for probes.	Supported in the controlled proxy, not yet across trained ManiSkill policies.
DualABI beats passive or fixed exploration.	Exact regret-aware 0.519, fixed pulses 0.669, random probes 0.531 in the frozen proxy.	Rejected by current evidence. Treat DualABI as an experimental controller.
Task-regret information is better than entropy information.	Both selectors and a task-irrelevant uncertainty counterexample are implemented.	Missing trained head-to-head evidence.
The hard mask makes deployment safe.	Finite/action/workspace/motion/gripper/collision checks fail closed in tests.	Unsupported as a deployment-safety claim; learned collision predictions can be wrong.
Results generalize across tasks, long lags, and unseen compositions.	Three adapters and a 750-job matrix exist.	Missing completed trained matrix.
ActionShift is uniquely novel.	It combines a compositional hidden action contract benchmark with task-regret-directed adaptation.	Broad novelty is rejected: UP-OSI/RMA cover adaptation, task-relevant exploration exists, and task-compatible active calibration is a close conceptual competitor. Only the hidden-ABI benchmark/application niche remains plausible. Avoid "first" claims.

Mandatory promotion gates

1. Complete every frozen five-seed trained job or publish a preregistered, clearly labeled subset.
2. Compare oracle, no adaptation, domain randomization, recurrent, OSI, RMA, random probes, fixed probes, DualABI, and entropy-DualABI under matched policy budgets.
3. Run no-information, no-safety, exact-belief, and learned-belief ablations.
4. Report failures and censored recovery times, not only successful episodes.
5. Keep simulator integration, task competence, method superiority, hardware safety, and novelty as separate claims.
6. Include terminal controllability/executability as a direct ablation; posterior accuracy alone is not an adequate outcome.

The dated competitor matrix and primary-source links are in the Related work section.

Benchmark card (week-one falsification gate)

Decision: continue. This is a controlled linearized PickCube control proxy. It falsifies benchmark mechanics quickly but is not evidence of ManiSkill policy performance. It is produced by scripts/smoke_matrix.sh (see Reproducing); the machine-readable form is reports/week_one_gate.json.

Method	Success	Final distance	Unintended displacement	Violations	True posterior
oracle	1.000	0.000	0.000	0.000	0.953
no_adaptation	0.081	3.432	3.128	15.206	0.899
recurrent_domain_randomization	0.062	2.681	2.287	12.950	0.904
random_probes	0.531	0.106	0.488	0.000	1.000
fixed_pulses	0.669	0.082	0.429	0.000	1.000
exact_regret_aware	0.519	0.112	0.516	0.000	0.997

The gate continues benchmark engineering because the exact filter converges, adaptation beats no adaptation, and probe strategies expose a nontrivial task/safety tradeoff. No method superiority claim is made at this stage — in particular it rejects a DualABI-superiority claim, because fixed pulses beat exact regret-aware selection.

Reproducing

Run commands from this directory with Python 3.11. The committed results were generated on 2026-07-18 with the package environment produced by pip install -e '.[dev,maniskill]' on that date.

Environment and static verification

python -m venv .venv
.venv/bin/pip install -e '.[dev,maniskill]'
.venv/bin/ruff check src tests
.venv/bin/mypy src
.venv/bin/pytest -q

Tests that import ManiSkill skip when the optional dependency is absent. A release verification must install it and observe three real-task smoke cases, not three skips.

Frozen splits and matrix

for split in seen unseen_value unseen_composition long_lag task_transfer; do
  .venv/bin/actionshift-validate split "configs/split/${split}.yaml"
done
.venv/bin/actionshift-evaluate matrix experiments/manifests/headline.jsonl
wc -l experiments/manifests/headline.jsonl

The matrix must contain 750 records: three tasks, five splits, ten methods, and five independent seeds. unseen_composition must report zero contract overlap and zero composition-signature overlap. Matrix generation schedules experiments; it does not imply that all 750 trained runs have happened.

Controlled falsification gate

ACTIONSHIFT_PYTHON=.venv/bin/python scripts/smoke_matrix.sh

This produces reports/week_one_gate.json and the Benchmark card. It is a controlled linearized PickCube proxy for falsifying adaptation mechanics quickly. It is not a ManiSkill result. The current result continues benchmark engineering but rejects a DualABI-superiority claim because fixed pulses beat exact regret-aware selection.

Real ManiSkill integration

.venv/bin/actionshift-smoke --sim-backend cpu --steps 3 --output reports/maniskill_cpu_smoke.json
.venv/bin/actionshift-smoke --sim-backend gpu --steps 3 --output reports/maniskill_gpu_smoke.json

Both reports must set passed to true, contain PickCube-v1, PushCube-v1, and PegInsertionSide-v1, and bound maximum oracle decode error by 1e-6. These runs use a nonidentity permutation/sign/scale/gripper contract. They verify integration only: short oracle-encoded actions are not trained task policies and need not complete the task.

README media

The hero / lag GIFs are real ManiSkill renders driven through the real adapters; the plots quote the committed reports. Both are regenerable:

CUDA_VISIBLE_DEVICES=0 .venv/bin/python media/make_media.py all   # hero_triptych.gif, lag_sidebyside.gif, selftest_demos.png
.venv/bin/python media/make_plots.py                              # plot_*.png + diagram_architecture.png

media/make_media.py loads the frozen Gate-1 PPO backbone and the trained delay-aware backbone, renders single-environment episodes offscreen (SAPIEN render_mode="rgb_array"), and composes the labeled panels; media/make_plots.py renders the report-number figures from media/style.py's shared palette.

Trained evaluations

Every trained run must preserve its matrix job ID, split-manifest hash, method/task config hash, checkpoint SHA-256, Git commit, package versions, hardware, and raw episode JSONL. Summaries must be created from raw episodes with:

.venv/bin/actionshift-evaluate summarize episodes.jsonl summary.json

Do not promote a method claim until the frozen five-seed matrix is complete and confidence intervals, safety violations, recovery time, unintended displacement, action cost, and posterior calibration are reported together. Dataset and simulator assets retain their original licenses; this repository does not redistribute them.

Experiment index

experiments/ holds the evidence-producing drivers for the reports in reports/. Every script is part of the reproduction path for a committed report — none is dead scratch. Runners write hash-addressed episode JSONL and summaries under artifacts/ (gitignored, local); analyzers aggregate those into the tables quoted in the reports. Slices generally require the maniskill extra and frozen Gate 0 PPO backbones (reports/gate0.md); the delayed_external/ battery requires MuJoCo instead.

Adaptation tournament (belief-family methods)

Script	Produces	Cited by
run_adaptation_slice.py	One adaptation-method evaluation slice against the frozen Gate 1 setup.	adaptation_tournament.md, adaptation_dualabi.md, third_task.md, fourth_task.md, adaptation_grasp_channel.md, rotation_v2.md, lag_completions.md
analyze_adaptation.py	Aggregates adaptation slices into the preregistered tournament table (Wilson cells, paired bootstrap).	adaptation_tournament.md, adaptation_dualabi.md, third_task.md, fourth_task.md

Full-grammar belief and excitation

Script	Produces	Cited by
run_factorized_slice.py	Full-grammar factorized-belief evaluation slice (grammar knowledge, no pool).	adaptation_factorized.md, adaptation_scale_corrector.md, adaptation_grasp_channel.md, absolute_excitation.md, cpp_fusion.md
run_absolute_excitation.sh	Hold-probe absolute-excitation campaign that breaches the discrete-identification wall.	absolute_excitation.md

Delay-aware backbone (long-lag split)

Script	Produces	Cited by
train_delay_aware.py	Trains a delay-aware augmented-state PPO backbone under randomized action lag.	adaptation_delay_aware.md
run_delay_slice.py	Evaluates a delay-aware backbone on one split (oracle or exact-belief).	adaptation_delay_aware.md, lag_completions.md
analyze_delay_slices.py	Aggregates delay-aware slice summaries into a Wilson-95% table.	adaptation_delay_aware.md, lag_completions.md

Unprivileged learned identifiers (honest negatives)

Script	Produces	Cited by
train_osi_real.py	Real-ManiSkill matched-budget UP-OSI-style training run.	adaptation_stage1.md
train_recurrent_real.py	Trains the recurrent episode-length adapter (matched budget).	adaptation_recurrent.md
run_recurrent_slice.py	Evaluates the trained recurrent adapter end-to-end.	adaptation_recurrent.md
train_probe_osi_real.py	Trains the probe-augmented learned identifier (matched budget).	adaptation_probe_osi.md
run_probe_osi_slice.py	Evaluates the probe-augmented learned identifier.	adaptation_probe_osi.md

Rotation v2 (frame axis)

Script	Produces	Cited by
run_rotation_v2_campaign.sh	Drives the v2 real-rotation frame-axis campaign.	rotation_v2.md
run_rotation_v2_parity.py	v2 real-rotation oracle-parity and frame=tool ceiling cells.	rotation_v2.md

Imitation backbone (paradigm generality)

Script	Produces	Cited by
train_dp_backbone.py	Resumable Diffusion Policy trainer on the clean pd_ee_delta_pose interface.	imitation_brittleness.md
run_imitation_brittleness.py	Imitation-backbone brittleness + adapter-rescue evaluation.	imitation_brittleness.md
analyze_imitation.py	Aggregates imitation slices into the DP-vs-PPO comparison tables.	imitation_brittleness.md

C++ fusion and the ActionABI bridge

Script	Produces	Cited by
bench_cpp_fusion.py	Honest torch-vs-C++ benchmark of the belief loop's evidence-scoring step.	cpp_fusion.md
export_labeled_traces.py	Exports labeled real-sim ManiSkill traces (ground-truth contracts) for supervised ActionABI evaluation.	ActionABI repo, reports/labeled_sim_traces.md

External delayed-RL anchors (MuJoCo, DCAC setup)

Script	Produces	Cited by
delayed_external/delay_wrapper.py	Faithful DCAC-style constant-delay wrapper for Gymnasium MuJoCo envs.	delayed_rl_external.md, delayed_rl_sac.md
delayed_external/ppo_delay_mujoco.py	Delay-aware augmented-state PPO on the DCAC MuJoCo delay benchmark.	delayed_rl_external.md
delayed_external/sac_delay_mujoco.py	Delay-aware augmented-state SAC on the DCAC MuJoCo delay benchmark.	delayed_rl_sac.md
delayed_external/run_sac_battery.py	Driver for the SAC delay battery (multi-env, multi-seed, multi-GPU).	delayed_rl_sac.md
delayed_external/aggregate_sac.py	Aggregates the SAC battery into a comparison table + retention analysis.	delayed_rl_sac.md

Supporting data. manifests/headline.jsonl — the frozen 750-record evaluation matrix (three tasks × five splits × ten methods × five seeds); regenerated by actionshift-evaluate matrix (see Reproducing). The Gate 0 / Gate 1 sprint tooling that produced reports/gate0.md, reports/gate1.md, and the week-one falsification gate lives in scripts/ (smoke_matrix.sh, plan_gate1_slices.py, evaluate_gate1_slice.py, analyze_gate1_slice.py, evaluate_ppo_parity.py, bootstrap_sprint.py).

Report index (reports/)

Report	One-liner
adaptation_tournament.md	Master tournament, rounds 1–13 — the load-bearing narrative and all headline numbers.
gate0.md	Official backbone competence and wrapper validity; task gating decisions.
gate1.md	Real hidden-contract PPO ceiling slice (oracle vs no-adapt).
adaptation_stage1.md	First end-to-end exact-belief adapter run on the real benchmark.
adaptation_dualabi.md	DualABI's probe-efficiency Pareto win over the entropy champion.
adaptation_factorized.md	Full-grammar factorized belief: grammar knowledge without the pool.
adaptation_grasp_channel.md	Grasp-channel evidence closes the gripper wall, tests the scale wall.
absolute_excitation.md	Hold-probe excitation breaches the discrete-identification (absolute-mode) wall.
adaptation_scale_corrector.md	Drift-based scale corrector — honest negative that re-localizes the last wall.
adaptation_recurrent.md	Recurrent episode-length adapter — honest negative sharpening the information limit.
adaptation_probe_osi.md	Probe-augmented learned identification — the probe budget cannot estimate the map.
adaptation_delay_aware.md	Delay-aware augmented-state PPO — first strong method on the long-lag split.
lag_completions.md	Lag-completion self-corrections (pipeline-flush is task-specific).
third_task.md	Gating in PullCube-v1; cross-task replication of load-bearing claims.
fourth_task.md	Gating in StackCube-v1; third Pareto replication; disclosed ordering anomaly.
peg_retry.md	PegInsertionSide-v1 at the full 75M budget — exclusion upheld honestly.
rotation_v2.md	De-degenerating the frame axis; factorized grammar cannot represent tool-frame rotation.
cpp_fusion.md	ActionABI's C++ scoring core as a live in-loop identification backend; honest regime map.
imitation_brittleness.md	Diffusion Policy is exactly as brittle and exactly as rescuable as PPO.
delayed_rl_external.md	Augmented-state PPO on DCAC's MuJoCo delay benchmark — mechanism validates, no return claim.
delayed_rl_sac.md	Augmented-state SAC on the same setup — baseline-tier, off-policy anchor.
transformer_icl_adjudication.md	Transformer-ICL (Vintix) adjudicated unfaithful for ActionShift, with evidence.
selftest_tool.md	The actionshift-selftest plug-and-verify startup check.

Related work & positioning

Absence-of-evidence framing only ("we are not aware of X," never "first"). Both a 2026-07-18 internal red-team and a 2026-07-21 literature pass reach these findings; the competitor families and their primary-source links are enumerated below.

No prior benchmark isolates compositional action-interface shift. Hidden-parameter adaptation benchmarks (UP-OSI/RMA-lineage) target continuous physical dynamics (mass, friction, payload). Compositional-generalization manipulation benchmarks (RoboHiMan arXiv:2510.13149; ATOM-Bench arXiv:2606.16826) target skill/task composition. Robustness benchmarks (LIBERO-Plus arXiv:2510.13626; COLOSSEUM arXiv:2402.08191, 14 visual/physical axes, no controller/frame axis) target visual/scene/sensor perturbation. None varies a low-level controller-convention/coordinate-frame axis. Nearest near-miss: Reflective VLA (arXiv:2606.25215) — a single continuous additive calibration-shift bias axis (±10 mm translation, ≤3° rotation), not a compositional discrete grammar, not structured with splits/oracles/ promotion gates; must be cited and distinguished in any related-work paragraph.

Closest algorithmic families (ActionShift does NOT own these ideas).

• Hidden-dynamics adaptation: UP-OSI (arXiv:1702.02453), RMA (arXiv:2107.04034) + manipulator RMA (CVPR 2024), VariBAD (arXiv:1910.08348, the BAMDP-via-meta-learned-belief ancestor). ActionShift's own tournament data shows a UP-OSI-style passive identifier collapses to 0.0 on this discrete/ compositional hidden variable — the strongest empirical evidence the two problem classes are not interchangeable (state as observation, not theory).
• In-context RL / transformer-ICL: Algorithm Distillation (arXiv:2210.14215, no official code, toy domains), Decision-Pretrained Transformer (arXiv:2310.08566), Vintix / Vintix-II (arXiv:2501.19400 / arXiv:2604.05112; real code + Meta-World/MuJoCo/Bi-DexHands, but standard fixed action spaces). A run counts as this baseline only if faithful (no-renaming-approximations rule).
• Active information-seeking: MaxInfoRL (ICLR 2025), Xie et al. informational-reward POMDP exploration (ICML 2024), Task-Compatible Active Calibration (DOI 10.64898/2026.06.11.731593 — identifiability + task-compatibility insufficient without terminal controllability; the reason DualABI's critic scores terminal controllability, not just posterior accuracy; verify citation manually — the bioRxiv DOI returned HTTP 403 to automated fetch). Provably-Safe Online System Identification (arXiv:2504.21486) is the safety-formalism neighbor for DualABI's bounded-probe + hard-mask framing.
• Cross-embodiment / shared-action-space (motivation, not competitor): UniAct (arXiv:2501.10105), GR00T N1 (arXiv:2503.14734), Open X-Embodiment (arXiv:2310.08864). These train/learn a declared adapter per embodiment; ActionShift asks what happens when the interface is not declared.

Delayed-MDP literature (long-lag split). Formal grounding: Katsikopoulos & Engelbrecht (2003, constant-delay MDP + state-augmentation reduction — the reduction ActionShift's delay-aware backbone uses); Walsh/Nouri/Li/Littman (2008/2009, Model-Based Simulation predict-through-the-lag). Deep-RL baselines: DCAC (Bouteiller et al., arXiv:2010.02966, "Reinforcement Learning with Random Delays," Gym-MuJoCo, constant delay-5; code github.com/rmst/rlrd — the only turnkey one), D-TRPO / DIDA (Liotet et al., IJCNN 2021 / ICML 2022 arXiv:2205.05569, no public code — cite-only), and "RL from Delayed Observations via World Models" (arXiv:2403.12309, code github.com/indylab/DelayedDreamer). No randomized-action-lag online-PPO ManiSkill result exists; three near-misses (obs-delay Meta-World arXiv:2509.20869 — also benchmarks DCAC and reports it degrading outside its trained delay range; offline-RL Adroit arXiv:2506.00131; teleop residual-RL Franka arXiv:2605.15480) must be cited/distinguished.

Unprivileged-baseline recon. No existing runnable baseline directly targets "recover a hidden compositional action-interface contract from short interaction." Vintix/Vintix-II is the most defensible to adapt (real code, real manipulation exposure), but any run must be labeled "Vintix-derived (local, adapted)." No published paper reports a comparable "N probe steps → identification accuracy" curve (ASID qualitative-only; Dynamics-as-Prompts no steps-budget curve) — a genuine reporting gap; state it rather than search further.

C++ CPU-fusion anchor. The ~13× CPU-over-32-thread-torch win in the small-batch regime (8 envs; the 1024-env cell is ~11×) is consistent with the documented small-batch CPU/GPU crossover pattern — NAG TR1/17 reports 1.5×–40× depending on transfer inclusion and batch size; arXiv:2311.07602 independently confirms >2× on modern CPU architectures. Positioning citation only, never "beats X's number" (workloads are not identical). See reports/cpp_fusion.md and ActionABI's Python bindings.

Venue framing. NeurIPS Datasets & Benchmarks track or CoRL for the benchmark contribution (frozen splits, privileged oracles, promotion-gate methodology, real ManiSkill backbones); RSS/ICRA if the paper leans on DualABI once it has trained end-to-end runs. The current headline (entropy > fixed probing, matched-privilege) is a benchmark-methodology result, not an algorithm paper's centerpiece.

Claim boundary and limits

The implemented benchmark mechanics, exact and learned belief updates, baselines, PPO/SAC primitives, real ManiSkill/DP adapters, safety mask, metrics, provenance, and the 750-job matrix are covered by tests, and the reports carry confidence intervals, causal controls, and disclosed anomalies. But: the safety mask is a software constraint check, not a hardware safety certificate; matched-privilege comparisons are claims about the value of information-seeking, not about unprivileged adaptation; no broad-novelty or method-superiority-over-SOTA claim is made. The graded, harshly-audited ledger is the Claims section; the per-claim red-team is the Claim audit section.

License and citation

ActionShift code is MIT licensed (LICENSE). Cite via CITATION.cff. ManiSkill, SAPIEN, MuJoCo, robot assets, and task assets are separate works with their own licenses and attribution requirements; this repository does not redistribute them. Dataset and simulator assets retain their original licenses.