---
license: mit
task_categories:
  - robotics
tags:
  - lerobot
  - imitation-learning
  - force-torque
  - maniskill
configs: []
---

# Haptic-ACT: datasets and models

Datasets and trained models of [Haptic-ACT](https://github.com/KelinLi-ReCAST/Haptic-ACT): the MuJoCo experiments and real-robot rollouts of the paper, and the version with wrist force/torque in ManiSkill.

## `haptic_act/`, `act_baseline/`: Haptic-ACT and ACT in MuJoCo

The data and models of the simulation experiments of the paper (sections 1-7 of the GitHub README): an xArm7 with the Inspire hand picks up an object and places it in a basket. The paths match the GitHub repository, so the download can go straight into a clone.

| Path | Content |
|---|---|
| `haptic_act/data/pick_and_place/raw_data/episode_<i>.hdf5` | 50 demonstrations, 400 steps each: `observations/qpos`, `observations/qvel`, `action` (400 x 13: 7 arm and 6 hand joints), `observations/contact_force` (400 x 5 fingertip forces, N), `observations/images/frontview` (400 x 480 x 640 x 3) |
| `haptic_act/ckpt/` | Haptic-ACT: `policy_best.ckpt`, `dataset_stats.pkl`, `result_policy_best.txt` and the training curves |
| `act_baseline/ckpt/` | ACT, trained on the same demonstrations: the same files |

```bash
git clone https://github.com/KelinLi-ReCAST/Haptic-ACT.git && cd Haptic-ACT
hf download KelinLiIC/Haptic-ACT --repo-type dataset --local-dir . --include "haptic_act/*" --include "act_baseline/*"
```

ACT reads the demonstrations from `haptic_act/data/`: set `DATA_DIR` in `act_baseline/constants.py` to that folder (section 4 of the GitHub README).

## `ft_maniskill/`: Haptic-ACT with wrist force/torque in ManiSkill

Demonstrations of five tasks with a Franka Panda with wrist camera in ManiSkill (section 8 of the GitHub README), recorded in the environments of the `haptic-act` branch of [KelinLi-ReCAST/ManiSkill](https://github.com/KelinLi-ReCAST/ManiSkill/tree/haptic-act). Each `ft_maniskill/<task>/lerobot_ft/` is a LeRobot dataset (format v3.0, 20 fps), and `ft_maniskill/<task>/models/` holds the models trained on it.

| Task | Folder | ManiSkill environment | Demonstrations | Episodes | Frames |
|---|---|---|---|---|---|
| PegInsertionSide | `peg` | `PegInsertionSideFT-v1` | jam and recovery | 1000 | 196,523 |
| PickCube | `pickcube` | `PickCubeVisGoal-v1` | motion planner | 1000 | 78,246 |
| StackCube | `stackcube` | `StackCube-v1` | ManiSkill's demonstrations, replayed | 1000 | 107,420 |
| PushCube | `pushcube` | `PushCube-v1` | ManiSkill's demonstrations, replayed | 990 | 68,287 |
| PlugChargerTopDown | `plug_topdown` | `PlugChargerTopDown-v1` | jam and recovery | 1000 | 173,314 |

| Column | Shape | Content |
|---|---|---|
| `observation.images.base_camera`, `observation.images.wrist_camera` | 480 x 480 x 3 | RGB video |
| `observation.state`, `observation.qvel` | 9 | joint positions / velocities (7 arm joints, 2 fingers) |
| `action` | 8 | absolute targets of the 7 arm joints and the gripper (`pd_joint_pos`) |
| `observation.torque` | 7 | arm-joint torques (N m) |
| `observation.wrench` | 6 | TCP wrench [fx fy fz tx ty tz] (N, N m, TCP frame), least squares of tau = J^T F |
| `observation.environment_state` | 30 | the last 5 wrenches, oldest first: the F/T input of Haptic-ACT |
| `observation.torque_hist` | 35 | the last 5 joint-torque readings |
| `observation.torque_do` | 7 | joint torques with 30 % of the frames zeroed |
| `observation.peg_box_force` (peg) / `observation.contact_force` | 1 | contact force between the object and the box / goal (N), privileged, not a policy input |
| `phase` | 1 | demonstration phase (peg: reach, grasp, align, jam, backoff, realign, insert) |
| `replan` | 1 | 1 during the deliberate jam of the peg demonstrations (contact force > 2 N), else 0 |
| `next.success` | 1 | task success after the frame's action |

The baselines (ACT, Diffusion Policy, BC) train on a copy without the force columns, made by `ft_maniskill/dataset_processing/make_baseline_dataset.py` of the GitHub repository.

### Models


Each `ft_maniskill/<task>/models/<method>/` is a LeRobot 0.6.1 `pretrained_model` directory (config, weights, pre/post-processors, training config).

| Method | Folder | Policy type |
|---|---|---|
| Haptic-ACT (F/T) | `haptic-act` | `acthaptic` ([plugin](https://github.com/KelinLi-ReCAST/Haptic-ACT/tree/main/ft_maniskill/policy/lerobot_policy_acthaptic)) |
| ACT | `act` | `act` |
| Diffusion Policy | `dp` | `diffusion` |
| BC (ACT without chunking or CVAE) | `bc` | `act` |

### Download and evaluation

```bash
hf download KelinLiIC/Haptic-ACT --repo-type dataset --local-dir data_ft --include "ft_maniskill/*"
# only the datasets: --include "ft_maniskill/*/lerobot_ft/*"; only the models: --include "ft_maniskill/*/models/*"
python ft_maniskill/eval/eval_ft.py --task peg --policy-path data_ft/ft_maniskill/peg/models/haptic-act \
    --n-action-steps 50 --learned-trigger 0.5 --out outputs/eval/peg/haptic-act
```

Installation, training and evaluation of every method: section 8 of the GitHub README.

## `real_robot/`: real-robot rollouts of Haptic-ACT and ACT

Rollouts of the trained policies on the real robot (section 6 of the GitHub README): Haptic-ACT in `real_robot/real_hact/` and ACT in `real_robot/real_act/`, 50 episodes each. Each `episode_<i>_compressed.hdf5` has 400 steps:

| Key | Content |
|---|---|
| `observations/qpos`, `observations/qvel`, `observations/effort` | 400 x 13: 7 arm joints and 6 hand joints |
| `action` | 400 x 13 joint targets |
| `observations/images/end_effector`, `observations/images/frontview` | JPEG-encoded 480 x 640 frames, one per step, zero-padded to a fixed length |
| `compress_len` | 2 x 400 encoded lengths; row 0 is `end_effector`, row 1 is `frontview` |

`real_robot/data_extract.py` of the GitHub repository reads these files (`load_hdf5`).

```bash
hf download KelinLiIC/Haptic-ACT --repo-type dataset --local-dir . --include "real_robot/*"
```

### Real-robot experiments (LeRobot format)

Real-robot experiments on a uFactory xArm7 with the xArm gripper, in LeRobot dataset format (`codebase_version` v2.1, 30 fps):

| Folder | Task | Episodes | Frames |
|---|---|---|---|
| `real_robot/batterydemo-lerobot/` | battery | 2 | 1,080 |
| `real_robot/plugchargerdemo-lerobot/` | plug charger | 20 | 12,000 (600 per episode) |

| Feature | Shape | Content |
|---|---|---|
| `observation.state`, `observation.qvel`, `observation.effort`, `action` | 8 | 7 arm joints + 1 gripper (`meta/modality.json`) |
| `observation.force_torque` | 6 | wrist force/torque sensor: forces in N, torques in N·m |
| `observation.images.0_femtobolt`, `observation.images.1_gemini330` | 480 x 640 x 3 | two cameras, AV1 video |

```bash
hf download KelinLiIC/Haptic-ACT --repo-type dataset --local-dir . --include "real_robot/*-lerobot/*"
```

## Citation

```bibtex
@inproceedings{li2025hapticact,
  title     = {Haptic-ACT: Bridging Human Intuition with Compliant Robotic Manipulation via Immersive VR},
  author    = {Li, Kelin and Wagh, Shubham M and Sharma, Nitish and Bhadani, Saksham and Chen, Wei and Liu, Chang and Kormushev, Petar},
  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year      = {2025},
  doi       = {10.1109/IROS60139.2025.11247471},
  note      = {arXiv:2409.11925}
}
```