---
license: apache-2.0
task_categories:
  - robotics
tags:
  - LeRobot
  - dexbench
  - dexterous-manipulation
  - single-hand
pretty_name: DexBench Single-Hand
size_categories:
  - 100K<n<1M
---

# DexBench — Single-Hand

Single-hand dexterous manipulation demonstrations from **DexBench**, replayed through Isaac Lab and packaged as a LeRobot v2.1 dataset. Each frame ships with third-person + wrist RGB video (256×256 @ 30 fps), proprioception, and the corresponding joint-space action.

## Summary

|                                |                                                  |
| ------------------------------ | ------------------------------------------------ |
| Episodes                       | **735**                                          |
| Frames                         | **287,019**                                      |
| Tasks                          | **15**                                           |
| FPS                            | **30**                                           |
| Camera resolution              | 256 × 256                                        |
| Robot                          | Floating-base Shadow Dexterous Hand (right)      |
| LeRobot codebase version       | v2.1                                             |
| Per-episode visual randomization | **disabled** (fixed HDRI background + table texture across all episodes) |

## Feature schema

| Feature                            | dtype           | shape         |
| ---------------------------------- | --------------- | ------------- |
| `observation.state`                | float32         | (28,)         |
| `observation.images.third_person`  | video (h264)    | (256, 256, 3) |
| `observation.images.wrist`         | video (h264)    | (256, 256, 3) |
| `action`                           | float32         | (28,)         |

## Tasks

| `task_index` | name                                       |
| ------------ | ------------------------------------------ |
| 0            | Dexbench-OpenFaucet-v0                     |
| 1            | Dexbench-FunctionalDrillApply-v0           |
| 2            | Dexbench-FunctionalHammerStrike-v0         |
| 3            | Dexbench-FunctionalPourCan-v0              |
| 4            | Dexbench-FunctionalPourMug-v0              |
| 5            | Dexbench-PivotLargeCuboidAgainstWall-v0    |
| 6            | Dexbench-TakeBookOffShelf-v0               |
| 7            | Dexbench-GraspBleach-v0                    |
| 8            | Dexbench-GraspCup-v0                       |
| 9            | Dexbench-GraspKettle-v0                    |
| 10           | Dexbench-GraspPan-v0                       |
| 11           | Dexbench-PickThinObjectFromContainer-v0    |
| 12           | Dexbench-GearMesh-v0                       |
| 13           | Dexbench-InsertPeg-v0                      |
| 14           | Dexbench-PlugCharger-v0                    |

## Usage

```python
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset

ds = LeRobotDataset("dexbench/single-lerobot")
print(ds.num_episodes, ds.num_frames)

frame = ds[0]
# frame["observation.state"], frame["action"]: torch.Tensor(28,)
# frame["observation.images.third_person"], frame["observation.images.wrist"]: torch.Tensor(3, 256, 256)
# frame["task"]: str (one of the 15 task ids above)
```

## Companion dataset

- **[dexbench/bimanual-lerobot](https://huggingface.co/datasets/dexbench/bimanual-lerobot)** — bimanual subset (746 ep, 56-dim action, additional left/right wrist cameras).

## Source

Replayed from teleop trajectory pickles in the gated [`dexbench/DexBench_dataset`](https://huggingface.co/datasets/dexbench/DexBench_dataset) repo (`teleop_dataset/{rigid, articulation, dexterous, grasping}/...`) using DexBench's `scripts/create_demo_files_sequential.py` (one Isaac Sim env per task, episodes replayed serially within the env, per-episode HDRI / table-texture randomizers disabled for visually stable demos). Then converted with `scripts/convert_to_lerobot.py` (h264 video at CRF 18).

## License

Apache-2.0. Defers to upstream DexBench terms for the underlying assets and teleop data.