---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- robotics
- so101
- code-as-policies
- auto-collected
- cap
- 10fps
- single-arm
- dual-camera
- skill-annotations
- close-pot-lid
configs:
- config_name: default
  data_files: data/*/*.parquet
---
# SO101 CAP Close Pot Lid

This dataset contains 100 LeRobot v3.0 demonstration episodes for an SO101 follower robot. The task is: Pick up the pot lid by its handle and place it on the pot. The dataset was collected at 10 Hz and includes paired top-view and wrist-view RGB videos, robot state/action trajectories, and CAP skill annotations.

<a class="flex" href="https://huggingface.co/spaces/lerobot/visualize_dataset?path=CoRL2026-CSI/SO101-cap_close_pot_10fps">
<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl.svg"/>
<img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl-dark.svg"/>
</a>

## Dataset Details

| Field | Value |
|---|---|
| Repository | `CoRL2026-CSI/SO101-cap_close_pot_10fps` |
| LeRobot codebase version | `v3.0` |
| Robot type | `so101_follower` |
| FPS | 10 |
| Episodes | 100 |
| Frames | 37669 |
| Tasks | 2 |
| Split | `0:100` train |
| Objects | pot and pot lid |
| Parquet files | 2 |
| Video files | 4 |

## Task And Annotations

**Task:** Pick up the pot lid by its handle and place it on the pot.

**Task indices:** 0, 1

**Observed subtask annotations:**

- pick pot lid and place on pot

**Representative skill types:**

- move_initial
- move_and_open
- move
- gripper_close
- move_linear
- gripper_open
- move_and_close
- move_free

**Representative skill-language annotations:**

- Move to initial position
- Approach pot lid handle and open gripper
- Pick up the pot lid by its handle
- grasp pot lid
- Lift the pot lid
- Move pot lid above the pot
- Place lid on pot and seat it with a drag motion
- push lid +2cm on pot
- pull lid -0cm on pot
- release lid on pot
- Retreat from pot and close gripper
- Return to initial state

## Observation And Action Space

| Feature | Shape | Names / Notes |
|---|---:|---|
| `observation.state` | `6` | shoulder_pan.pos, shoulder_lift.pos, elbow_flex.pos, wrist_flex.pos, wrist_roll.pos, gripper.pos |
| `action` | `6` | shoulder_pan.pos, shoulder_lift.pos, elbow_flex.pos, wrist_flex.pos, wrist_roll.pos, gripper.pos |
| `observation.images.top` | `480x640x3` | RGB video, 10 fps |
| `observation.images.left_wrist` | `480x640x3` | RGB video, 10 fps |

Raw camera keys are `observation.images.top` and `observation.images.left_wrist`. The local SmolVLA training scripts map them to `observation.images.camera2` and `observation.images.camera1`, respectively.

## Files

```text
meta/info.json
meta/tasks.parquet
meta/episodes/chunk-*/file-*.parquet
data/chunk-*/file-*.parquet
videos/{observation.images.top,observation.images.left_wrist}/chunk-*/file-*.mp4
```

The dataset uses the LeRobot v3.0 format. Episode metadata and frame-level trajectories are stored in parquet files; image observations are stored as H.264 MP4 videos referenced by the frame records.

## Annotation Columns

| Column | Shape |
|---|---:|
| `skill.natural_language` | `1` |
| `skill.verification_question` | `1` |
| `skill.type` | `1` |
| `skill.progress` | `1` |
| `skill.goal_position.joint` | `6` |
| `skill.goal_position.robot_xyzrpy` | `6` |
| `skill.goal_position.gripper` | `1` |
| `subtask.natural_language` | `1` |
| `subtask.object_name` | `1` |
| `subtask.target_position` | `3` |

## Loading

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

dataset = LeRobotDataset("CoRL2026-CSI/SO101-cap_close_pot_10fps")
sample = dataset[0]
```

## Intended Uses

This dataset is intended for robot imitation learning, action-chunking policy training, skill-conditioned behavior analysis, and reproducible LeRobot/SmolVLA experiments on the specified tabletop task.

## Limitations

The dataset is task-specific and collected in a fixed workspace. It does not include an official validation or test split, nor does it include benchmark success-rate results. Downstream users should verify camera calibration, action normalization, and task-language assumptions before transferring policies to a different robot, workspace, or object set.