stray-light/zerograv-manipulation-trajectories-state-v1

Zero-Gravity Manipulation Trajectories (State-Only, v1) Dataset Card

Fourth collection round on the DROID rig, superseding stray-light/zerograv-manipulation-trajectories-state-v0 — same general recipe (state-only, ~1000 episodes/task, QA-filtered), but with a recorded-action bug fixed, hold-segment trimming applied, corrected QA filtering per task, and one new task (PickSingleYCB). Full per-task hyperparameters below, sourced directly from each training run's own logged hyperparameters/text_summary (not reconstructed from memory).

Tasks, checkpoints, and dataset stats

Task	Episodes	Frames	Checkpoint	Peak eval success_once
PushCube	1000	220,307	droid-pushcube-armbound02-full50M-h300/best_ckpt.pt	0.75
LiftPeg	1000	291,455	droid-liftpeg-armbound02-full50M-h425/best_ckpt.pt	0.688
PickCube	1000	603,005	droid-repro-ourbranch-armbound02-full50M/best_ckpt.pt	1.00 (noisy 16-env eval)
PickSingleYCB	1000	526,846	droid-picksingleycb-armbound02-full80M-h900-ne2048/best_ckpt.pt	~0.5-0.875 (still-training run; see note below)

Frames is the sum of each task's 1000 individual trimmed episode lengths (see "Hold-segment trimming" below) — not a fixed count per episode.

Per-task training hyperparameters (PPO, ManiSkill baseline)

All four use control_mode=hybrid_impedance_delta, arm_action_bound=0.2, gamma=0.95, gae_lambda=0.9, clip_coef=0.2, lr=3e-4, obs_mode=state — the ManiSkill PPO baseline otherwise unmodified. Per-task horizon and entropy:

param	PushCube	LiftPeg	PickCube	PickSingleYCB
env_id	PushCube-DROID-ZeroGrav-OnContact-v1	LiftPeg-DROID-ZeroGrav-OnGrasp-WallFix-v1	PickCube-DROID-ZeroGrav-v1	PickSingleYCB-DROID-ZeroGrav-OnGrasp-v1
max_episode_steps	300	425	900	900
ent_coef	0.0	0.0	0.0	0.002
num_envs (train)	1024	1024	1024	2048
total_timesteps	50M	50M	50M	80M (checkpoint from an in-progress run, not final)
seed	1	1	1	1

Only PickCube's recipe was hand-tuned beyond the ManiSkill baseline (per the collaborator): max_episode_steps=900 (a arm_action_bound-bounded policy needs ~17s to complete the task) and gamma=0.95 instead of the baseline's 0.8 (0.8 gives an effective horizon of ~5 steps — far too short to carry reward across a 260+-step episode once actions are bounded). The other three tasks reuse that same gamma/horizon-scaling logic per-task.

PickSingleYCB's ent_coef=0.002 and total_timesteps=80M (vs. 0.0/50M for the other three) simply reflect that it's the only PickSingleYCB DROID run so far — no ent_coef A/B sweep was run for it like the other three, and its checkpoint is from a run still training toward 80M total steps at collection time (not a finished/final checkpoint).

Collection QA filtering (why episode counts differ from raw success rate)

Every kept episode passes ep_ever_succeeded (stock ManiSkill success flag true at some point) plus a per-task combination of additional checks, because the stock success condition alone can be satisfied by a transient, physically-unrepresentative moment (object slides through the target at speed, or briefly touches a grasp/placement condition without holding it):

Task	Filters applied	Why
PushCube	--require-success-at-end	Release-and-settle task; literal end-of-episode success check worked fine here (~66% of raw successes survived)
LiftPeg	--min-stable-hold-frames 15 (+ default settle-velocity check)	Release-and-settle task; --require-success-at-end proved too strict (~12% survival — many genuine successes drift by the literal last raw frame) — replaced with requiring the final success run last ≥15 frames wherever it falls
PickCube	--settle-vel-threshold 0 --require-grasp-at-success --min-stable-hold-frames 15	Grasp-and-hold task: the default settled-velocity check is the WRONG invariant for a held (still-moving-with-the-arm) object — disabled in favor of grasp-maintained + the same stable-hold-length check
PickSingleYCB	same as PickCube	Same grasp-and-hold success-condition family

--min-stable-hold-frames N requires the episode's final contiguous success run to last at least N frames wherever it occurs in the episode, rather than literally on the last raw-collected frame — verified empirically (via a direct probe) to still guarantee every kept episode survives the 15-frame hold-trim below ending on success=True, without requiring the much stricter (and here, much lower-yield) literal-final-frame condition.

Hold-segment trimming

Every episode is collected to its full horizon (--full-horizon), then trimmed post-hoc to (start of the final contiguous success run) + 15 frames (~1s at 15Hz) — enough real frames behind the final action chunk for a 15-step chunked policy (e.g. pi0.5) to see genuine stop-and-hold behavior, without shipping the full multi-hundred-frame post-success tail. Trimming is computed from the FINAL success run specifically (not the first-ever success), since a --full-horizon episode can flicker (an early transient success that later drifts away isn't the one that holds) — this also composes correctly with --min-stable-hold-frames above.

Differences from zerograv-manipulation-trajectories-state-v0

• Recorded-action bug fixed: v0's actions were the policy's raw, unclipped output (RecordEpisode.step() logs the action before the env's own arm_action_bound clamp runs internally) even though the physically-simulated motion was correctly clamped. This round clamps the action before stepping, so recorded actions now match physics exactly (verified: max |arm action| == arm_action_bound across every episode).
• Hold-segment trimming added (see above) — v0 shipped the full post-success tail uncut.
• Corrected, task-appropriate QA filtering (see above) — v0 used --require-success-at-end uniformly for all tasks, which this round found to be the wrong/too-strict check for LiftPeg and the grasp-and-hold tasks.
• Raw HDF5 + provenance shipped: trajectory.h5/trajectory.json (full per-episode env_states, every simulation step — not just the trimmed frames that ship in the parquet) plus provenance.json and env_code/ (a snapshot of the exact env/training code at collection time) are included alongside the LeRobot data//meta/ directories, for independent reconstruction/cross-checking.
• One new task: PickSingleYCB (grasp-and-place on a randomized YCB object), not present in v0.

Key Notes

The checkpoints themselves are not published here — they live in the training run's own runs/ directory and Weights & Biases project (Post_Recovery_Experiments), not on the Hub.

observation.state layout is unchanged from v0/earlier exports for the same task (verified directly: PushCube 43-dim, LiftPeg 40-dim, PickCube 50-dim, all identical to the prior published exports) — this pipeline never transforms the state vector itself, only which frames are kept.