HJ666/dexmulti_grasp_grasp

DexMulti: Grasp + Grasp

_{One teleoperated demonstration: the two RGB cameras (left); the robot, the segmented object clouds with hand contact, and the end-effector path coloured by skill (right).}

Teleoperated demonstrations of the Grasp + Grasp task (paper: main experiments): 36 demonstrations, the complete training set used for the paper's experiments.

Paper: Concurrent Prehensile and Nonprehensile Manipulation: A Practical Approach to Multi-Stage Dexterous Tasks (IROS 2026, arXiv:2603.11655). Code: https://github.com/Msornerrrr/dexmulti. Full documentation (conventions, loading without LeRobot, npz conversion): https://github.com/Msornerrrr/dexmulti/blob/main/docs/dataset.md.

Task: Sequentially grasp two objects without releasing the first.

robot	xArm7 arm + LEAP Hand (right), teleoperated with DexCap
cameras	2x Intel RealSense L515 (RGB 640x480)
episodes / frames	36 / 5230 (length 109–197, mean 145.3)
rate	10 Hz
format	LeRobot v3.0, videos H.264 (yuv420p)

Features

feature	shape	description
observation.images.cam1, observation.images.cam2	480x640x3 video	RGB of the two cameras
observation.state	(29,) float32	see below
action	(22,) float32	see below
observation.pointcloud.object	(512, 3) float32	segmented point cloud of the first (grasped) object, robot base frame [m]
observation.pointcloud.drawer	(512, 3) float32	segmented point cloud of the second object (key name kept from the code base)
observation.pointcloud.hand	(512, 3) float32	points on the LEAP Hand surface from forward kinematics
observation.contact.object, observation.contact.drawer	(512, 1) float32	per-point hand contact score in [0, 1] of the matching cloud
observation.skill	int64	0: not in a skill (approach / transition), 1: skill 1 (first object), 2: skill 2 (the second object)
task	string	language instruction (one per dataset)

observation.state:

index	source key	meaning
0–5	eef_pose	measured end-effector pose: x, y, z [m], roll, pitch, yaw [rad] (scipy Euler xyz)
6–12	arm_state	xArm7 joint positions [rad]
13–28	hand_state	LEAP Hand joint positions [rad] (LEAP ROS order: 4 per finger, thumb last)

action (what the robot was commanded at this step, 10 Hz):

index	source key	meaning
0–5	arm_cmd	commanded end-effector pose: x, y, z [m], roll, pitch, yaw [rad]
6–21	hand_cmd	LEAP Hand joint commands [rad]

Element names are in meta/info.json.

Frames and conventions

• All poses and point clouds are in the xArm7 base frame (link_base), metres. Orientations are scipy Euler "xyz" angles in radians; roll is near ±π, so its plot wraps.
• Point clouds come from the two depth cameras (fused, segmented, 512 points per object) and are the inputs used by DexMulti. Depth images and masks are not included.
• observation.skill is derived from the skill keyframes (task1_start..task1_end = 1, task2_start..task2_end = 2, inclusive). Where the two recorded ranges overlap, skill 1 takes precedence; frames of task2_start..task2_end outside skill 1 are 2 even when task2_start < task1_start. The ranges overlap in 3 episode(s) (dexmulti/keyframe_overlaps.json, with the resulting label runs; set: null = the keyframes behind observation.skill), e.g. session_20251226_002029 with keyframes [38, 64, 29, 130] is labelled 0 on [0, 28], 2 on [29, 37], 1 on [38, 64], 2 on [65, 130]. The keyframe files are unchanged.
• timestamp is frame_index / 10; the original wall-clock times are in dexmulti/episodes.json.

DexMulti extras (dexmulti/)

Not part of the LeRobot schema; used by the DexMulti code base:

• episodes.json: per episode_index the original recording name, length, source set and wall-clock times.
• keyframes/<episode>.json: skill keyframes as used to build the library.
• library/dual_task_policy_chamfer.npz: the skill library built from these demonstrations, exactly as run on the robot (pickle-free format, load with dexmulti.library.load_library).
• keyframe_overlaps.json: recordings whose two keyframe ranges overlap, with the resulting observation.skill runs.
• cameras.json: intrinsics (fx, fy, cx, cy) and extrinsics T_base_cam (camera optical frame to robot base) of both cameras; constant for the whole dataset.

Load as DexMulti episodes (no lerobot or torch needed):

from dexmulti.data.lerobot import LeRobotEpisodes
ds = LeRobotEpisodes("<path or repo id>")
ep = ds.episode(0)          # dict: keys of dexmulti/data/episode.py + skill
kf = ds.keyframes(0)        # task1_start, task1_end, task2_start, task2_end

or with LeRobot: LeRobotDataset("HJ666/dexmulti_grasp_grasp").

Citation

@inproceedings{jiang2026concurrent,
  title     = {Concurrent Prehensile and Nonprehensile Manipulation: A Practical Approach to Multi-Stage Dexterous Tasks},
  author    = {Jiang, Hao and Wu, Yue and Wang, Yue and Sukhatme, Gaurav S. and Seita, Daniel},
  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year      = {2026}
}

License

CC-BY-4.0. Please cite the paper when using the data.