armteam/hapticwam-teleop-dataset

HapticWAM teleoperation dataset

1,115 teleoperated manipulation episodes with fingertip tactile sensing, packed as per-task .tar.zst shards. This is the teleoperation corpus the HapticWAM teacher is trained on.

Hardware: UR3 + Robotiq 2F-85 + 2x Daimon DM-Tac W2L fingertip sensors + RealSense scene camera, teleoperated through an Echo exoskeleton leader.

Related repos: policy rollouts live in armteam/hapticwam-rollouts; the raw as-recorded session tree is armteam/hapticwam-teleop-raw; models are under armteam/hapticwam-teacher, -student, -baselines, -ablations.

Contents

File	Size	Episodes
Carton.tar.zst	14.53 GB	180
Carton_fail.tar.zst	1.50 GB	20
egg.tar.zst	21.59 GB	180
egg_fail.tar.zst	1.70 GB	20
waffles.tar.zst	14.92 GB	180
waffles_fail.tar.zst	1.80 GB	20
whiteboard.tar.zst	15.33 GB	180
whiteboard_fail.tar.zst	0.65 GB	10
batch_20260822.tar.zst	30.65 GB	325
manifests.tar	1.2 MB	index over the 790 task episodes
manifests_v6.tar	1.2 MB	index over all 1,115 episodes
norm_stats.json	42 KB	action/state normalisation statistics
total	102.67 GB	1,115

MANIFEST_OF_RECORD.md says which manifest to use. Read it before training.

Unpacking

The eight task shards unpack directly into the canonical flattened view:

hf download armteam/hapticwam-teleop-dataset --repo-type dataset --local-dir .
mkdir -p data/episodes/tasks
for f in Carton egg waffles whiteboard Carton_fail egg_fail waffles_fail whiteboard_fail; do
  tar -I zstd -xf "$f.tar.zst" -C data/episodes/tasks
done
tar -xf manifests_v6.tar -C data/episodes      # -> manifests/all.jsonl (1,115 rows)

batch_20260822.tar.zst unpacks to the raw session layout (20260822_<time>_<label>/ep_*), 34 sessions / 325 episodes, not the tasks/<task>/ layout. It is normalised and appended to the task view at provision time; the v6 manifest already carries the resulting rows and paths.

The shards were made as one archive per task because the loose per-episode layout runs to roughly 865,000 files, well past the hub's file-count guidance, and enumerating it before training costs hours. A shard is a plain tar compressed with zstd; nothing else is needed to read it.

Episode format

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

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

Manifest rows carry success, split (train/val), tactile_contact, duration_s, peak_force_N, max_contact_mm2, failure_demo, session, operator, task, text and path. manifests/quality_full.csv holds the full per-episode quality audit with per-stream sample counts, rates and gaps.

Tasks

task	episodes (v4 view)	+batch_20260822	role	median dur	median peak |F|	median contact
Carton	180	+70	successes	17.4 s	12.9 N	17.9 mm²
Carton_fail	20	+15	failure demos	17.7 s	26.3 N	36.1 mm²
waffles	180	+70	successes	18.3 s	8.4 N	3.0 mm²
waffles_fail	20	+15	failure demos	20.4 s	17.8 N	25.5 mm²
egg	180	+70	successes	27.6 s	8.8 N	18.0 mm²
egg_fail	20	+15	failure demos	18.4 s	33.5 N	49.1 mm²
whiteboard	180	+70	successes
whiteboard_fail	10	0	failure demos
total	790	+325				1,115

The 790-episode view splits 712 train / 78 val. With batch_20260822 appended the corpus is 1,115 episodes = 1,037 train / 78 val; the val set stays frozen across both views.

batch_20260822

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]

The batch failure demos are under-grasps: the gripper closed on little or nothing and the task was then continued as if the object were held ("phantom carry"). Their actions are never imitated (action_weight=0); the tactile, contact and event heads still train on them.

Notes

• success=true throughout the success tasks. The *_fail tasks are deliberate failure demonstrations. The v4 *_fail episodes (over-squeeze / induced slip, higher forces and contact areas) carry success=true, meaning "the episode captured the intended failure"; the batch_20260822 under-grasp episodes carry success=false. Training treats both the same way: a failure demo is anything with success=false, a deliberate_failure tag, or a task name ending in _fail, and its action-imitation loss is zeroed.
• 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. These are valid vision and proprioception demos with no tactile signal.
• Split rule: per success task, the last two sessions chronologically are val and the rest train; failure demos are train-only. Re-split freely from 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). These are flagged in quality_notes.
• One recorder configuration (config_hash b2504b6d5ff0c29d) covers the whole corpus, and every episode here is policy: teleop, dagger_round: -1 — pure human teleoperation.
• index.jsonl lists every file in this repo with its size and LFS sha256, and the path it was copied from.

Licence

Data: CC-BY-4.0.