armteam/hapticwam-teleop-raw

HapticWAM — teleoperated episodes (raw)

Renamed from armteam/phantom-episodes on 2026-09-19, when the project's working name PHANTOM became HapticWAM (Haptic World-Action Model). The old id still redirects. The Python package and CLI keep the name phantom, so task keys, checkpoint names and config keys are unchanged.

Tactile manipulation episodes for HapticWAM (Haptic World-Action Model), the tactile world-action model: UR3 + Robotiq 2F-85 + 2x Daimon DM-Tac W2L fingertip sensors + RealSense scene camera, teleoperated via the Echo exoskeleton leader.

Layout

• tasks/<task>/ep_<task>_<epoch>_<idx>/ — canonical flattened view: one directory per episode, grouped by task. Use this + manifests/.
• manifests/<task>.jsonl, manifests/all.jsonl — one JSON object per episode: success, split (train/val), tactile_contact, duration, peak force, contact area, source session, quality notes, hub path.
• manifests/quality_full.csv — full per-episode quality audit.
• archive/, collect/ — raw as-recorded session layout (provenance; superset that also contains pre-cleanup debug takes). Directory names are the raw session labels as typed at the rig (carton, *_fail_undergrasp); meta.json is authoritative for task/labels.
• archive/20260822_* — batch batch_20260822 (34 sessions / 325 eps), NOT yet in tasks/ or manifests/. Appended at provision time by tools/intake_recovery.py (normalize → place → manifest; all rows go to train, the v4 val set stays frozen). Result: 1115 eps = 1037 train / 78 val.
• text_embeddings.pt and norm_stats.json for tasks/ live in armteam/phantom-checkpoints/dataset_v3_packed/ (never recomputed; the cache covers all 8 task keys incl. *_fail).

Episode format

Each episode directory holds one zarr group per stream (data + ts arrays, timestamps in master-clock seconds) plus meta.json. Streams:

Stream	Contents	Rate
camera_scene_color	JPEG images	~15 Hz
tactile_left, tactile_right	wrench (6D, N / 1e−2 Nm), area (mm²), fields_ds (72×96×8, f16), keyframes (144×192×8, f16), infer_img (288×384, u8)	~6.5 Hz
arm	q, qd, tcp_pose, tcp_speed, ft	~125–140 Hz
gripper	pos, obj-detect	~100 Hz
actions	Delta end-effector commands	~10 Hz
actions_abs	Absolute joint targets + gripper command	Event-based

Tasks

task	episodes	role	median dur	median peak |F|	median contact
Carton	180	successes	17.4 s	12.9 N	17.9 mm²
Carton_fail	20	failure demos	17.7 s	26.3 N	36.1 mm²
waffles	180	successes	18.3 s	8.4 N	3.0 mm²
waffles_fail	20	failure demos	20.4 s	17.8 N	25.5 mm²
egg	180	successes	27.6 s	8.8 N	18.0 mm²
egg_fail	20	failure demos	18.4 s	33.5 N	49.1 mm²
whiteboard	180	successes
whiteboard_fail	10	failure demos

(tasks/ view = 790 eps: 712 train / 78 val; val = last 2 sessions per success task.)

batch_20260822 (archive/20260822_*, not yet in tasks/)

task	eps	labels
Carton, egg, waffles, whiteboard	70 each	success=true, tags [full, batch_20260822] — ordinary demos, appended as train
Carton_fail, egg_fail, waffles_fail	15 each	success=false, failure_demo=true, tags [full, deliberate_failure, undergrasp, batch_20260822] — under-grasp: gripper closed on little/nothing, then the task was continued as if holding ("phantom carry"); actions are never imitated (action_weight=0), tactile/contact/event heads still train on them

Notes

• success=true everywhere in the success tasks. *_fail tasks are DELIBERATE failure demonstrations: v4 *_fail (over-squeeze / induced slip, higher forces/contact) carry success=true ("the episode captured the intended failure"); batch_20260822 *_fail (under-grasp) carry success=false. Training treats both identically — is_failure_demo() (phantom/data/schema.py) fires on success=false OR the deliberate_failure tag OR a task ending in _fail, and zeroes the action-imitation loss for that episode.
• Tags: full = complete teleop take (recorder default); batch_<YYYYMMDD> = intake batch (provenance only, no training effect); deliberate_failure / undergrasp = failure-demo kind.
• tactile_contact=false marks episodes where the grasp landed outside the sensor pads (valid vision/proprio demos, no tactile signal).
• Split rule: per success task, the last 2 sessions (chronological) are val, the rest train; failure demos are train-only. Re-split freely via the manifests — they are the source of truth, not the folder layout.
• Peak forces briefly exceed the 30 N pad ceiling in a handful of episodes (dynamic spikes, mostly failure demos) — flagged in quality_notes.