---
pretty_name: ForceAwareACT MuJoCo Peg-in-Hole Teleoperation Dataset
tags:
  - robotics
  - mujoco
  - hdf5
  - imitation-learning
  - peg-in-hole
  - contact-rich-manipulation
  - force-torque
  - teleoperation
  - timeseries
size_categories:
  - n<1K
---

# ForceAwareACT MuJoCo Peg-in-Hole Teleoperation Dataset

## Dataset Summary

This dataset is a raw force-aware MuJoCo peg-in-hole teleoperation archive with synchronized robot state, RGB images, force/torque wrench streams, command-action labels, event records, and episode metadata. It contains 100 episode directories and 200 raw episode files. The raw episode archive inspected for this card is 13,344,579,333 bytes (12.43 GiB), excluding this card and generated audit reports.

Each episode directory is named with the pattern `YYYYMMDD_HHMMSS_teleop_NNN` and contains:

```text
<episode_directory>/
  episode.hdf5
  metadata.json
```

All 100 inspected episodes share one HDF5 schema, and every episode contains both required files.

## Dataset Description

The dataset was recorded from a MuJoCo wall peg-in-hole teleoperation setup. The HDF5 schema version stored in every file is `compact_mujoco_hdf5_v1`. The archive stores raw observations and metadata directly in HDF5. It is not a pre-converted Hugging Face `datasets` table, and the standard Dataset Viewer should not be assumed to render the HDF5 image tensors automatically.

The current ForceAwareACT training loader can consume this archive directly with `h5py`, derive fixed-length action/force chunks, align image and force streams to state timestamps, and compute normalization statistics.

## Repository Structure

```text
forceawareact-peg-hole-mujoco/
├── README.md
└── raw/
    ├── 20260701_131034_teleop_006/
    │   ├── episode.hdf5
    │   └── metadata.json
    └── ...
```

The Hugging Face repository stores the Dataset Card as the root `README.md` and
the immutable episode archive under `raw/`. Local audit reports were used to
prepare this card but are not required for loading the dataset.

## Task Description

The task is a MuJoCo wall peg-in-hole task using a 7-DoF right-arm model with a cylindrical peg tool. The robot is teleoperated toward a wall fixture with a round hole. Episodes in this archive all have final HDF5 status `auto_stop_task_success`, and their events include `task_success_site_reached`, `terminal_hold_start`, and `auto_stop_task_success`.

Important caveat: the HDF5 attribute `task_success` is `unknown` in all inspected files. For this dataset, success-stop status is represented by the file/group `status` attributes and event records, not by the `task_success` attribute.

## Data Collection

The matching recorder implementation is a compact MuJoCo HDF5 recorder. It records:

- state stream at nominal 30 Hz;
- image stream at nominal 30 Hz;
- force/torque stream at nominal 500 Hz;
- command-action labels aligned with state samples;
- episode events;
- initial and final robot/task metadata.

The recorder uses MuJoCo `data.time` as the alignment clock. The sidecar `metadata.json` stores only summary fields; detailed arrays and most metadata live in `episode.hdf5`.

The collection configuration inspected alongside this archive disables hole randomization (`enable_hole_randomization: false` and `randomize_hole_on_record_start: false`). No hole-randomization fields are present in `metadata.json`.

## Sensor Modalities

| Modality | Stored paths | Rate summary |
|---|---|---|
| Joint state | `observations/joint_pos`, `observations/joint_vel`, `observations/joint_torque` | approximately 30 Hz |
| End-effector pose | `observations/ee_pose` | approximately 30 Hz |
| RGB cameras | `observations/images/ee_cam`, `observations/images/base_top_cam` | approximately 30 Hz |
| Force/torque wrench | `observations/ft_wrench`, `observations/ft_wrench_raw`, `observations/ft_wrench_gravity` | approximately 500 Hz |
| Command actions | `actions/joint_pos_command`, `action` | approximately 30 Hz |
| Events | `events/names`, `events/t_episode`, `events/t_sim`, `events/t_wall` | event records, not periodic samples |

## HDF5 Schema

Every inspected `episode.hdf5` file contains these dataset paths:

- `action`
- `actions/joint_pos_command`
- `episode_metadata/actuator_names`
- `episode_metadata/camera_names`
- `episode_metadata/final_ee_pose`
- `episode_metadata/final_ft_wrench`
- `episode_metadata/final_ft_wrench_gravity`
- `episode_metadata/final_ft_wrench_raw`
- `episode_metadata/final_hole_center_pos`
- `episode_metadata/final_joint_pos`
- `episode_metadata/final_joint_pos_command`
- `episode_metadata/final_joint_torque`
- `episode_metadata/final_joint_vel`
- `episode_metadata/final_peg_tip_pos`
- `episode_metadata/ft_gravity_tool_body_names`
- `episode_metadata/ft_gravity_tool_com_sensor_initial`
- `episode_metadata/ft_gravity_tool_com_world_initial`
- `episode_metadata/ft_gravity_world`
- `episode_metadata/initial_ee_pose`
- `episode_metadata/initial_ft_wrench`
- `episode_metadata/initial_ft_wrench_gravity`
- `episode_metadata/initial_ft_wrench_raw`
- `episode_metadata/initial_hole_center_pos`
- `episode_metadata/initial_joint_pos`
- `episode_metadata/initial_joint_pos_command`
- `episode_metadata/initial_joint_torque`
- `episode_metadata/initial_joint_vel`
- `episode_metadata/initial_peg_tip_pos`
- `episode_metadata/initial_task_error_xyz`
- `episode_metadata/joint_names`
- `events/names`
- `events/t_episode`
- `events/t_sim`
- `events/t_wall`
- `observations/ee_pose`
- `observations/ft_wrench`
- `observations/ft_wrench_gravity`
- `observations/ft_wrench_raw`
- `observations/images/base_top_cam`
- `observations/images/camera_names`
- `observations/images/ee_cam`
- `observations/joint_pos`
- `observations/joint_torque`
- `observations/joint_vel`
- `timestamps/force`
- `timestamps/force_episode`
- `timestamps/image`
- `timestamps/image_episode`
- `timestamps/state`
- `timestamps/state_episode`

### Field Summary

| HDF5 path | Episodes | Shape summary | dtype | Compression | Chunks |
|---|---:|---|---|---|---|
| `action` | 100/100 | `varies by episode; first-dim min 213, max 448, mean 309.77, median 306.50` | `float64` | none | `(256, 7)` |
| `actions/joint_pos_command` | 100/100 | `varies by episode; first-dim min 213, max 448, mean 309.77, median 306.50` | `float64` | none | `(256, 7)` |
| `episode_metadata/actuator_names` | 100/100 | `(7,) x100` | `object` | none | `none` |
| `episode_metadata/camera_names` | 100/100 | `(2,) x100` | `object` | none | `none` |
| `episode_metadata/final_ee_pose` | 100/100 | `(7,) x100` | `float64` | none | `none` |
| `episode_metadata/final_ft_wrench` | 100/100 | `(6,) x100` | `float64` | none | `none` |
| `episode_metadata/final_ft_wrench_gravity` | 100/100 | `(6,) x100` | `float64` | none | `none` |
| `episode_metadata/final_ft_wrench_raw` | 100/100 | `(6,) x100` | `float64` | none | `none` |
| `episode_metadata/final_hole_center_pos` | 100/100 | `(3,) x100` | `float64` | none | `none` |
| `episode_metadata/final_joint_pos` | 100/100 | `(7,) x100` | `float64` | none | `none` |
| `episode_metadata/final_joint_pos_command` | 100/100 | `(7,) x100` | `float64` | none | `none` |
| `episode_metadata/final_joint_torque` | 100/100 | `(7,) x100` | `float64` | none | `none` |
| `episode_metadata/final_joint_vel` | 100/100 | `(7,) x100` | `float64` | none | `none` |
| `episode_metadata/final_peg_tip_pos` | 100/100 | `(3,) x100` | `float64` | none | `none` |
| `episode_metadata/ft_gravity_tool_body_names` | 100/100 | `(1,) x100` | `object` | none | `none` |
| `episode_metadata/ft_gravity_tool_com_sensor_initial` | 100/100 | `(3,) x100` | `float64` | none | `none` |
| `episode_metadata/ft_gravity_tool_com_world_initial` | 100/100 | `(3,) x100` | `float64` | none | `none` |
| `episode_metadata/ft_gravity_world` | 100/100 | `(3,) x100` | `float64` | none | `none` |
| `episode_metadata/initial_ee_pose` | 100/100 | `(7,) x100` | `float64` | none | `none` |
| `episode_metadata/initial_ft_wrench` | 100/100 | `(6,) x100` | `float64` | none | `none` |
| `episode_metadata/initial_ft_wrench_gravity` | 100/100 | `(6,) x100` | `float64` | none | `none` |
| `episode_metadata/initial_ft_wrench_raw` | 100/100 | `(6,) x100` | `float64` | none | `none` |
| `episode_metadata/initial_hole_center_pos` | 100/100 | `(3,) x100` | `float64` | none | `none` |
| `episode_metadata/initial_joint_pos` | 100/100 | `(7,) x100` | `float64` | none | `none` |
| `episode_metadata/initial_joint_pos_command` | 100/100 | `(7,) x100` | `float64` | none | `none` |
| `episode_metadata/initial_joint_torque` | 100/100 | `(7,) x100` | `float64` | none | `none` |
| `episode_metadata/initial_joint_vel` | 100/100 | `(7,) x100` | `float64` | none | `none` |
| `episode_metadata/initial_peg_tip_pos` | 100/100 | `(3,) x100` | `float64` | none | `none` |
| `episode_metadata/initial_task_error_xyz` | 100/100 | `(3,) x100` | `float64` | none | `none` |
| `episode_metadata/joint_names` | 100/100 | `(7,) x100` | `object` | none | `none` |
| `events/names` | 100/100 | `(5,) x100` | `object` | none | `none` |
| `events/t_episode` | 100/100 | `(5,) x100` | `float64` | none | `none` |
| `events/t_sim` | 100/100 | `(5,) x100` | `float64` | none | `none` |
| `events/t_wall` | 100/100 | `(5,) x100` | `float64` | none | `none` |
| `observations/ee_pose` | 100/100 | `varies by episode; first-dim min 213, max 448, mean 309.77, median 306.50` | `float64` | none | `(256, 7)` |
| `observations/ft_wrench` | 100/100 | `varies by episode; first-dim min 3548, max 7455, mean 5155.24, median 5106.00` | `float64` | none | `(2048, 6)` |
| `observations/ft_wrench_gravity` | 100/100 | `varies by episode; first-dim min 3548, max 7455, mean 5155.24, median 5106.00` | `float64` | none | `(2048, 6)` |
| `observations/ft_wrench_raw` | 100/100 | `varies by episode; first-dim min 3548, max 7455, mean 5155.24, median 5106.00` | `float64` | none | `(2048, 6)` |
| `observations/images/base_top_cam` | 100/100 | `varies by episode; first-dim min 213, max 448, mean 309.77, median 306.50` | `uint8` | lzf | `(1, 480, 640, 3)` |
| `observations/images/camera_names` | 100/100 | `(2,) x100` | `object` | none | `none` |
| `observations/images/ee_cam` | 100/100 | `varies by episode; first-dim min 213, max 448, mean 309.77, median 306.50` | `uint8` | lzf | `(1, 480, 640, 3)` |
| `observations/joint_pos` | 100/100 | `varies by episode; first-dim min 213, max 448, mean 309.77, median 306.50` | `float64` | none | `(256, 7)` |
| `observations/joint_torque` | 100/100 | `varies by episode; first-dim min 213, max 448, mean 309.77, median 306.50` | `float64` | none | `(256, 7)` |
| `observations/joint_vel` | 100/100 | `varies by episode; first-dim min 213, max 448, mean 309.77, median 306.50` | `float64` | none | `(256, 7)` |
| `timestamps/force` | 100/100 | `varies by episode; first-dim min 3548, max 7455, mean 5155.24, median 5106.00` | `float64` | none | `(2048,)` |
| `timestamps/force_episode` | 100/100 | `varies by episode; first-dim min 3548, max 7455, mean 5155.24, median 5106.00` | `float64` | none | `(2048,)` |
| `timestamps/image` | 100/100 | `varies by episode; first-dim min 213, max 448, mean 309.77, median 306.50` | `float64` | none | `(1,)` |
| `timestamps/image_episode` | 100/100 | `varies by episode; first-dim min 213, max 448, mean 309.77, median 306.50` | `float64` | none | `(1,)` |
| `timestamps/state` | 100/100 | `varies by episode; first-dim min 213, max 448, mean 309.77, median 306.50` | `float64` | none | `(256,)` |
| `timestamps/state_episode` | 100/100 | `varies by episode; first-dim min 213, max 448, mean 309.77, median 306.50` | `float64` | none | `(256,)` |

All numeric state/action/force datasets are `float64`. Camera tensors are `uint8` RGB, LZF-compressed, and chunked as one full frame per chunk.

## metadata.json Schema

Every sidecar `metadata.json` has these keys:

| Key | Episodes | Type | Notes |
|---|---:|---|---|
| `camera_names` | 100/100 | list | `['ee_cam', 'base_top_cam']` |
| `duration_sim` | 100/100 | float | simulation duration in seconds |
| `episode_label` | 100/100 | string | e.g. `teleop_006` |
| `hdf5_path` | 100/100 | string | original recorder-machine path; not portable |
| `image_storage` | 100/100 | string | `inside_hdf5_uint8_rgb` |
| `n_force` | 100/100 | integer | force sample count |
| `n_image` | 100/100 | integer | image sample count |
| `n_state` | 100/100 | integer | state sample count |
| `schema_version` | 100/100 | string | `compact_mujoco_hdf5_v1` |
| `status` | 100/100 | string | `auto_stop_task_success` |

`metadata.json` does not contain task geometry, hole-randomization fields, full timestamp arrays, force streams, images, or initial/final robot arrays. Those are stored in `episode.hdf5`.

## Temporal Sampling

Measured from the timestamp arrays across all 100 episodes:

| Stream | Samples per episode | Duration seconds | Measured rate Hz | Timestamp check |
|---|---|---|---|---|
| State, `timestamps/state_episode` | min 213, max 448, mean 309.77, median 306.50 | min 7.066, max 14.900, mean 10.292, median 10.183 | min 29.996, max 30.004, mean 30.001, median 30.001 | finite and strictly increasing |
| Image, `timestamps/image_episode` | min 213, max 448, mean 309.77, median 306.50 | min 7.066, max 14.900, mean 10.292, median 10.183 | min 29.996, max 30.004, mean 30.001, median 30.001 | finite and strictly increasing |
| Force, `timestamps/force_episode` | min 3548, max 7455, mean 5155.24, median 5106.00 | min 7.093, max 14.908, mean 10.308, median 10.209 | min 499.938, max 500.070, mean 500.027, median 500.043 | finite and strictly increasing |
| Events, `events/t_episode` | 5 records in every episode | min 7.094, max 14.910, mean 10.309, median 10.210 | not periodic | finite and nondecreasing; duplicate event times occur |

State, image, and force streams begin at the same episode time in every episode. Their end times differ by at most 0.032 seconds across streams.

## Action Definition

The raw HDF5 archive contains explicit command-action labels in every episode:

- `actions/joint_pos_command`: `[N_state, 7]`, actual MuJoCo position-actuator command from `data.ctrl[actuator_ids]`.
- `action`: `[N_state, 7]`, ACT-compatible alias of `actions/joint_pos_command`.

The ForceAwareACT loader also supports training with `action_mode="joint_pos"`, where labels are derived from future observed joint positions:

```text
action_chunk = observations/joint_pos[i + 1 : i + K + 1]
```

For command modes such as `action` and `joint_pos_command`, chunks are aligned to the current decision index:

```text
command_chunk = command[i : i + K]
```

Delta action modes subtract the current joint position from the selected command chunk. The raw dataset does not define a fixed chunk length; `K` is a loader/training argument. Samples near the end of an episode that cannot provide a full future chunk are excluded rather than padded.

## Force/Torque Representation

The wrench component order is:

```text
[Fx, Fy, Fz, Tx, Ty, Tz]
```

Every episode stores:

- `observations/ft_wrench_raw`: raw MuJoCo force/torque sensor reading;
- `observations/ft_wrench_gravity`: predicted gravity wrench in the sensor frame;
- `observations/ft_wrench`: compensated wrench used by default for learning.

For this dataset, HDF5 metadata records:

```text
observations/ft_wrench = observations/ft_wrench_raw - observations/ft_wrench_gravity
```

The compensation mode is `gravity`; episode-start tare is disabled. Recorder code expresses the gravity wrench in the FT sensor site frame using the configured tool body and gravity vector. A formal public frame diagram and sign-convention statement are still required before public release.

During ForceAwareACT training, causal force windows are derived from `observations/ft_wrench` by sampling only force timestamps less than or equal to the current state timestamp. Future force chunks for force-prediction losses are aligned to future state timestamps by nearest force timestamp.

## Camera Streams

The archive contains two camera streams in every episode:

| Camera | HDF5 path | Shape | dtype | Compression | Verified role |
|---|---|---|---|---|---|
| `ee_cam` | `observations/images/ee_cam` | `[N_image, 480, 640, 3]` | `uint8` RGB | LZF | end-effector camera attached near the flange/peg connection |
| `base_top_cam` | `observations/images/base_top_cam` | `[N_image, 480, 640, 3]` | `uint8` RGB | LZF | fixed top/base camera looking at the wall task region |

Sampled HDF5 frames show rendered MuJoCo RGB images without text HUD overlays. The force-feedback HUD is drawn only on live OpenCV display frames by the controller code and is not recorded into these HDF5 image tensors.

Camera calibration matrices and intrinsics are not stored in the HDF5 files.

## Episode Metadata

HDF5 `episode_metadata` contains:

- names of joints, actuators, cameras, and force-gravity tool bodies;
- initial/final joint position, velocity, torque, and end-effector pose;
- initial/final force wrench, raw wrench, and gravity wrench;
- initial/final peg-tip positions;
- force compensation attributes;
- sample counts and recording status.

### Legacy hole-goal metadata caveat

The active task-success target in the current MuJoCo model is
`hole_goal_site`. However, the recorder metadata embedded in this historical
100-episode archive refers to the legacy site name `hole_center_site`. That
legacy name did not resolve when the archived metadata was written.

Consequently, `episode_metadata/initial_hole_center_pos`,
`episode_metadata/final_hole_center_pos`, and
`episode_metadata/initial_task_error_xyz` contain non-finite values in all
100 episodes. These fields must not be used as valid hole-position or
task-error labels. Recorded task completion should instead be interpreted from
the `status` attributes and event records, including
`task_success_site_reached` and `auto_stop_task_success`.

## Loading Example

Use `h5py` and load only the slices you need, especially for image tensors.

```python
from pathlib import Path
import h5py

root = Path("raw")
episode = sorted(root.glob("*/episode.hdf5"))[0]

with h5py.File(episode, "r") as f:
    qpos = f["observations/joint_pos"][:]          # small numeric array
    force_window_source = f["observations/ft_wrench"]
    first_force_sample = force_window_source[0]

    # Avoid loading the full image tensor unless you really need it.
    ee_first_frame = f["observations/images/ee_cam"][0]

    print(f.attrs["schema_version"])
    print(qpos.shape, first_force_sample.shape, ee_first_frame.shape)
```

For ForceAwareACT training, prefer using the repository loader rather than hand-aligning streams.

## Download Instructions

Install the Hugging Face CLI, authenticate if the dataset is private, and download the raw archive:

```bash
python -m pip install -U huggingface_hub
hf auth login

hf download \
  shuteng0608/forceawareact-peg-hole-mujoco \
  --repo-type dataset \
  --local-dir forceawareact-peg-hole-mujoco
```

After download, run the loading example from the repository root, where the
episode archive is located under `raw/`.

## Intended Uses

- Research on imitation learning for contact-rich manipulation in simulation.
- Force-aware policy learning with synchronized vision, proprioception, and force/torque observations.
- Offline evaluation of action prediction and future force prediction models.
- Reproducibility studies for the ForceAwareACT data pipeline.

## Out-of-Scope Uses

- Direct real-world robot deployment or safety validation.
- Claims about real sensor noise, real calibration, or physical hardware safety.
- Training methods that require frame-level human intent/correction annotations unless those annotations are added separately.
- Treating this raw HDF5 archive as a Dataset Viewer-native image dataset without conversion.

## Known Limitations

- The dataset is simulation-only MuJoCo data.
- Public license/access terms are not defined in the repository at audit time.
- Camera intrinsics/calibration are not stored in HDF5.
- The formal force/torque frame and sign convention need a public diagram or specification.
- `metadata.json` contains original recorder-machine `hdf5_path` strings and should not be used as portable paths.
- The `task_success` attribute on `episode_metadata` is `unknown`; success-stop information is in `status` and event records.
- The historical recorder metadata refers to the legacy site name `hole_center_site`, while the active task-success target is `hole_goal_site`. Consequently, the archived hole-center position and task-error fields are non-finite and must not be used as valid labels.
- Human corrective behavior during contact is not explicitly annotated.

## Dataset Versioning

This card describes the inspected raw archive with HDF5 schema version `compact_mujoco_hdf5_v1` and 100 episode directories. Future releases should update this card when episodes are added, schema changes, or license/access terms are clarified.

## Integrity Verification

Expected raw archive properties for this version:

- Episode directories: 100
- Raw episode files: 200
- Raw archive size: 13,344,579,333 bytes (12.43 GiB)
- Per-episode required files: `episode.hdf5`, `metadata.json`
- HDF5 schema variants: 1
- HDF5 read errors in audit: 0
- Metadata JSON read errors in audit: 0

A minimal verification command after download:

```bash
python - <<'PYCODE'
from pathlib import Path
root = Path('raw')
episodes = sorted(p for p in root.iterdir() if p.is_dir())
print('episodes', len(episodes))
print('missing required files', [p.name for p in episodes if not (p/'episode.hdf5').is_file() or not (p/'metadata.json').is_file()])
PYCODE
```

## License and Access

No repository license file was found during this audit. Do not assume a public license until the dataset owner publishes one. Access restrictions, redistribution terms, and citation requirements should be defined before public release.

## Citation

No paper citation is defined by the repository at audit time. If you use this dataset, cite the dataset repository and any future associated paper or project page provided by the authors.

## Documentation Still Required Before Public Release

- Public license and access policy.
- Formal wrench coordinate-frame and sign-convention documentation.
- Camera calibration/intrinsics policy, or an explicit statement that calibration is unavailable.
- Definition of a stable public version tag or release revision.
- Clarification of whether public consumers should use raw HDF5 directly or a converted `datasets` package.