---
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](https://github.com/huggingface/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

```text
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

```python
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`).