Datasets:

benudavis
/

cluttered_grasp

license: apache-2.0
task_categories:
  - robotics
tags:
  - LeRobot
  - ur10
  - robotiq
  - tactile
  - depth
  - cluttered-grasping
  - imitation-learning
configs:
  - config_name: default
    data_files: data/*/*.parquet

Cluttered Grasp (UR10 + Robotiq 2F-140 + tactile + depth)

Real-robot teleop demonstrations of cluttered grasping. Episodes are gated on gripper_tcp height in base_link (record while low, pause while high), time-synced to the RealSense RGB master @ 30 Hz, then converted to LeRobot format.


Robot	UR10 + Robotiq 2F-140 (`ur10_robotiq_2f140`)
FPS	30 (RGB master timeline)
Episodes	103
Frames	23943
Codebase	LeRobot dataset v3.0
Task	`cluttered grasping`

Sensors & resolutions

Stream	Capture (ROS)	Dataset feature	Shape (H×W×C)	Aspect
Center RGB	RealSense color `1280×720@30` JPEG	`observation.images.center_cam`	720×1280×3	16:9
Depth	Depth module `848×480@30`, published as `aligned_depth_to_color` → resampled into the color grid	`observation.images.center_cam_depth`	720×1280×1 (uint16 mm, video12)	16:9 (aligned)
Tactile L	Fingertip cam JPEG (~90 Hz capture, hold-last to RGB)	`observation.images.tactile_L`	240×320×3	4:3
Tactile R	same	`observation.images.tactile_R`	240×320×3	4:3

Master clock: compressed RGB frames — one dataset row per RGB stamp (~30 Hz).
Sync: nearest-neighbor within ~50 ms, then hold-last for depth/tactile/proprio.
Depth note: the D4xx depth module runs at 480p (848×480). Hardware align-to-color writes depth in RGB resolution, so the stored/LeRobot depth video is 1280×720 (same pixels as center_cam), unit millimetres.
Depth export modes used for this release: 12-bit gray12le HEVC (video12), log-quantized over 0.2–1.0 m.

Dataset structure

cluttered_grasping/
├── meta/
│   ├── info.json          # schema, fps, totals, video codec info
│   ├── stats.json         # per-feature normalization stats
│   ├── tasks.parquet      # task prompts
│   └── episodes/          # per-episode metadata
├── data/
│   └── chunk-*/file-*.parquet   # state, action, indices
└── videos/
    ├── observation.images.center_cam/chunk-*/file-*.mp4        # AV1, 1280×720
    ├── observation.images.center_cam_depth/chunk-*/file-*.mp4  # HEVC gray12le depth
    ├── observation.images.tactile_L/chunk-*/file-*.mp4         # AV1, 320×240
    └── observation.images.tactile_R/chunk-*/file-*.mp4

Features (per frame)

Key	Type	Shape	Description
`observation.state`	float32	(14,)	`joint_0..5`, `gripper`, `eef_x/y/z`, `eef_qx/qy/qz/qw` (xyzw)
`action`	float32	(7,)	Absolute next `joint_0..5` + `gripper` (last frame of each episode dropped)
`grasp_state`	int64	(1,)	Exclusive grasp phase at observation `t`: `0` no_contact, `1` contact, `2` closure, `3` lift, `4` success
`next.success`	float32	(1,)	`1.0` on the frame before a transition into `success` (`labels[t]≠4` and `labels[t+1]==4`); else `0`
`next.done`	float32	(1,)	Same pulse as `next.success` (task/terminal success co-located with that transition)
`observation.images.center_cam`	video (AV1)	720×1280×3	Scene RealSense RGB
`observation.images.center_cam_depth`	video (HEVC gray12le)	720×1280×1	Aligned depth, mm; `is_depth_map`
`observation.images.tactile_L`	video (AV1)	240×320×3	Left fingertip tactile (`/tactile1`)
`observation.images.tactile_R`	video (AV1)	240×320×3	Right fingertip tactile (`/tactile2`)
`timestamp`	float32	(1,)	Time within episode (s)
`frame_index` / `episode_index` / `index` / `task_index`	int64	(1,)	LeRobot indices

observation.state EE pose is gripper_tcp in base_link. Wrist force/torque is not exported to LeRobot (still present in upstream zarr if needed).

Label example: if phase history is …, contact, contact, success, success, …, then only the last contact frame has next.success = next.done = 1.0.

Episode definition

Start when TCP (z < 0.32) m
End when TCP (z > 0.33) m
No recording between episodes (reset / high)

Load

from lerobot.datasets.lerobot_dataset import LeRobotDataset

ds = LeRobotDataset("benudavis/cluttered_grasp")
print(ds)
frame = ds[0]
# RGB / tactile: frame["observation.images.center_cam"], ...
# Depth (mm-quantized video12): frame["observation.images.center_cam_depth"]
# Phases / returns:
#   frame["grasp_state"]      # int64 phase id
#   frame["next.success"]     # 1.0 before successful grasp onset
#   frame["next.done"]        # same as next.success

Collection / conversion notes

ROS bags → one zarr episode per bag (bag2dataset under ~/venvs/clutter, zarr≥3).
Offline rebuild: scripts/zarr_to_lerobot.py --fps 30 --depth-mode video12 over ~/EDG_Experiment/clutter/**/*.zarr.
Phase labels: scripts/label_grasp_states.py (Streamlit) writes data/obs/policy/grasp_state and, on Save / Save all, derived next_success / next_done on each episode zarr.
Converter recomputes next.success / next.done from grasp_state and trims leading NaN policy frames (delayed joint/TF after record start).
Auto-label may set contact/success from tactile blob area; only terminal success runs (touching episode end) stay success.
Action mode default: absolute_next (see scripts/lerobot_convert/actions.py).
Upload: scripts/upload_lerobot_hf.py (copies this card to the dataset root as README.md).