Haptic-ACT: Bridging Human Intuition with Compliant Robotic Manipulation via Immersive VR
Paper • 2409.11925 • Published
video video |
|---|
Datasets of Haptic-ACT.
ft_maniskill/: Haptic-ACT with wrist force/torque in ManiSkill
Demonstrations of four 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. Each ft_maniskill/<task>/lerobot_ft_replan/ is a LeRobot dataset (format v3.0, 20 fps).
| Task | Folder | ManiSkill environment | Demonstrations | Episodes | Frames |
|---|---|---|---|---|---|
| PegInsertionSide | peg |
PegInsertionSideFT-v1 |
scripted 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 |
| 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: the label of the re-planning head |
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.
hf download KelinLiIC/Haptic-ACT --repo-type dataset --local-dir data_ft --include "ft_maniskill/*"
@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}
}