Datasets:
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- dexsuite
- scripted-expert
- dart
- force
- spray_graffiti
- sharpa
- floating
configs:
- config_name: default
data_files: data/*/*.parquet
This dataset was created using LeRobot.
Dataset Description
Scripted-expert DexSuite data for spray_graffiti with the floating wrist and the Sharpa hand: 200 successful episodes (91,120 frames) from DexSuite's scripted task controller with grasp-recovery perturbations (DART) and randomized object and start-pose initialization, including per-finger contact forces. These are the Sharpa Spray Graffiti demonstrations of the DexSuite benchmark (Track 2). The hand picks up the aerosol can, aims the nozzle at the sheet of paper, presses the spray button with the index finger and paints inside the red circle stencil; the task succeeds once at least 60% of the stencil interior is painted with at most 35% of the paint outside it. All episodes are successful, recorded at 20 Hz with a static front camera and a wrist camera (224x224 RGB video), proprioception and ground-truth object state, under DexSuite's shared observation/action interface (actions are OSC pose offsets from the episode-start end-effector pose plus gripper/hand joint targets). As in every DexSuite set, frame t stores the observation before action t; the success flag on the last frame refers to the transition it triggers, so the terminal state itself is not stored.
The controller grasps the can with a wrap grasp along one of ten approach routes, carries it over the paper, aligns the nozzle with the measured paint hit and the index fingertip with the button, and paints overlapping passes inside the stencil. It keeps painting after the first success (to about 90% coverage) and then releases the button, so success and terminated are true on every frame from the first successful transition on (the last 102 frames of an episode at the median). DART: during recording the environment executed the controller's command plus a smooth Ornstein-Uhlenbeck perturbation of the wrist pose (1.5 cm and 4 degrees, 0.6 s time constant, scaled by 1.0 / 0.5 / 0.25 in the free / approach / in-hand phases and switched off while the nozzle and the index pad are aligned), and the recorded action is the controller's clean command, its correction from the perturbed state (Laskey et al., 2017). The command the environment actually executed at step t is stored in obs.state.other.action of frame t+1, so replaying action open loop does not reproduce the recorded episodes. Each episode randomizes the can and paper positions (±2 cm in x, ±3 cm in y), the can yaw (±15°), the start wrist pose (±5 cm and ±12° per axis) and the relaxed finger pose; 200 of 236 attempts were kept, the other 36 failed the controller's own checks (mostly a grip not secured during the approach) and were discarded.
The robot is an arm-less floating wrist (a virtual 6-DoF gantry) carrying the right Sharpa hand (22 DoF); the 29-D action is 7 osc_pose_abs_quat values (position offset and orientation relative to the episode-start wrist pose) followed by 22 hand joint position targets in radians. The hand is sharpa_fardexbot, the right Sharpa hand carrying the wrist-camera mount of the real handheld rig (mount plate, quick changer and D405 body, which collide with the scene and show in the front-camera frames); DexSuite ships it under that name. Besides the standard DexSuite keys, every frame carries per-finger contact forces (obs.state.other.finger_force_world, finger_force_norm in N, finger_force_display, finger_force_saturated, finger_contact; thumb, index, middle, ring, pinky) and the controller's phase (obs.state.other.controller_phase: 0 free, 1 approach, 2 in hand) and stage (obs.state.other.controller_stage, named in meta/controller.json). meta/env_config.json rebuilds the environment and meta/controller_episodes/episode_*.json keeps each episode's scene seed, style seed and controller parameters. Depth was rendered during collection but is not included.
- Homepage: https://huggingface.co/dexsuite
- Paper: https://arxiv.org/abs/2610.37799
- License: apache-2.0
Loading
from lerobot.datasets.lerobot_dataset import LeRobotDataset
dataset = LeRobotDataset("dexsuite/spray_graffiti_floating_sharpa_scripted")
frame = dataset[0] # action, obs.state.*, obs.cameras.front.rgb and obs.cameras.wrist.rgb (3x224x224 float in [0, 1])
Training
DexSuite's robomimic entry point converts the set to HDF5 and trains on it:
python -m scripts.imitation_learning.robomimic.train \
--dataset spray_graffiti_floating_sharpa_scripted \
--config bc \
--sensors state \
--architecture mlp
--sensors cameras --cameras front trains on the front camera and robot proprio instead, and --config act, act_img or diffusion select the other policy recipes (see scripts/imitation_learning/robomimic/README.md). --dataset dexsuite/spray_graffiti_floating_sharpa_scripted works as well.
Dataset Structure
{
"codebase_version": "v3.0",
"robot_type": null,
"total_episodes": 200,
"total_frames": 91120,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 20,
"splits": {
"train": "0:200"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4",
"features": {
"obs.state.gripper.qpos": {
"dtype": "float32",
"shape": [
22
]
},
"obs.state.gripper.qtorque": {
"dtype": "float32",
"shape": [
22
]
},
"obs.state.gripper.qvel": {
"dtype": "float32",
"shape": [
22
]
},
"obs.state.gripper.tcp_pos": {
"dtype": "float32",
"shape": [
3
]
},
"obs.state.gripper.tcp_quat": {
"dtype": "float32",
"shape": [
4
]
},
"reward": {
"dtype": "float32",
"shape": [
1
]
},
"truncated": {
"dtype": "bool",
"shape": [
1
]
},
"terminated": {
"dtype": "bool",
"shape": [
1
]
},
"success": {
"dtype": "bool",
"shape": [
1
]
},
"failure": {
"dtype": "bool",
"shape": [
1
]
},
"action": {
"dtype": "float32",
"shape": [
29
]
},
"obs.state.manipulator.eef_pos": {
"dtype": "float32",
"shape": [
1,
3
]
},
"obs.state.manipulator.eef_quat": {
"dtype": "float32",
"shape": [
1,
4
]
},
"obs.state.manipulator.qpos": {
"dtype": "float32",
"shape": [
6
]
},
"obs.state.manipulator.qtorque": {
"dtype": "float32",
"shape": [
6
]
},
"obs.state.manipulator.qvel": {
"dtype": "float32",
"shape": [
6
]
},
"obs.state.other.action": {
"dtype": "float32",
"shape": [
29
]
},
"obs.state.other.can_pos": {
"dtype": "float32",
"shape": [
3
]
},
"obs.state.other.can_quat": {
"dtype": "float32",
"shape": [
4
]
},
"obs.state.other.can_vel": {
"dtype": "float32",
"shape": [
3
]
},
"obs.state.other.head_q": {
"dtype": "float32",
"shape": [
1
]
},
"obs.state.other.last_action": {
"dtype": "float32",
"shape": [
29
]
},
"obs.state.other.nozzle_dir": {
"dtype": "float32",
"shape": [
3
]
},
"obs.state.other.nozzle_pos": {
"dtype": "float32",
"shape": [
3
]
},
"obs.state.other.paint_coverage": {
"dtype": "float32",
"shape": [
1
]
},
"obs.state.other.paint_debug_spheres": {
"dtype": "float32",
"shape": [
1
]
},
"obs.state.other.paint_outside_frac": {
"dtype": "float32",
"shape": [
1
]
},
"obs.state.other.paper_pos": {
"dtype": "float32",
"shape": [
3
]
},
"obs.state.other.paper_quat": {
"dtype": "float32",
"shape": [
4
]
},
"obs.state.other.press_frac": {
"dtype": "float32",
"shape": [
1
]
},
"obs.state.other.spray_active": {
"dtype": "float32",
"shape": [
1
]
},
"obs.state.other.spray_hit_xy": {
"dtype": "float32",
"shape": [
2
]
},
"obs.state.other.stencil_center_xy": {
"dtype": "float32",
"shape": [
2
]
},
"obs.state.other.stencil_coverage": {
"dtype": "float32",
"shape": [
1
]
},
"obs.state.other.stencil_id": {
"dtype": "float32",
"shape": [
1
]
},
"obs.state.other.stencil_size": {
"dtype": "float32",
"shape": [
1
]
},
"obs.cameras.front.rgb": {
"dtype": "video",
"shape": [
224,
224,
3
],
"info": {
"video.height": 224,
"video.width": 224,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 20,
"video.channels": 3,
"has_audio": false
}
},
"obs.cameras.wrist.rgb": {
"dtype": "video",
"shape": [
224,
224,
3
],
"info": {
"video.height": 224,
"video.width": 224,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 20,
"video.channels": 3,
"has_audio": false
}
},
"obs.state.other.finger_force_world": {
"dtype": "float32",
"shape": [
5,
3
]
},
"obs.state.other.finger_force_norm": {
"dtype": "float32",
"shape": [
5
]
},
"obs.state.other.finger_force_display": {
"dtype": "float32",
"shape": [
5
]
},
"obs.state.other.finger_force_saturated": {
"dtype": "bool",
"shape": [
5
]
},
"obs.state.other.finger_contact": {
"dtype": "bool",
"shape": [
5
]
},
"obs.state.other.controller_phase": {
"dtype": "int64",
"shape": [
1
]
},
"obs.state.other.controller_stage": {
"dtype": "int64",
"shape": [
1
]
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"episode_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
},
"task": "spray_graffiti",
"options": null
}
Citation
BibTeX:
[More Information Needed]