Episodes Preview Unitree G1 Visualizer
235 episodes · 30 fps · 8 cameras · 640×480 h264
Xense Robotics

Representative frames from TacVerse's bimanual demonstrations.

TacVerse collection and processing pipeline

Collected with XTac-UMI-G1 grippers, released as LeRobot datasets.

This dataset was created using LeRobot.

Access

This dataset is public. Download it and load it with LeRobot:

hf download TacVerse/xtac-umi-g1-insert-divider --repo-type dataset --local-dir ./xtac-umi-g1-insert-divider
from lerobot.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset("TacVerse/xtac-umi-g1-insert-divider", root="./xtac-umi-g1-insert-divider")

Privacy

Faces of people appearing in the camera streams (wrist and head cameras) have been anonymized: they were detected automatically and pixelated before release. The tactile streams contain no people and are unchanged. Automatic detection can miss a face; if you find one that is not masked, please contact us via the TacVerse landing page and we will fix it.

Dataset Structure

meta/info.json:

{
    "codebase_version": "v3.0",
    "robot_type": "xtac_umi_g1",
    "collection_stack": "xense-taccap-lerobot",
    "total_episodes": 235,
    "total_frames": 550820,
    "total_tasks": 1,
    "chunks_size": 1000,
    "data_files_size_in_mb": 100,
    "video_files_size_in_mb": 200,
    "fps": 30,
    "splits": {
        "train": "0:235"
    },
    "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": {
        "action": {
            "dtype": "float32",
            "names": [
                "left_tcp.x",
                "left_tcp.y",
                "left_tcp.z",
                "left_tcp.r1",
                "left_tcp.r2",
                "left_tcp.r3",
                "left_tcp.r4",
                "left_tcp.r5",
                "left_tcp.r6",
                "left_gripper.pos",
                "right_tcp.x",
                "right_tcp.y",
                "right_tcp.z",
                "right_tcp.r1",
                "right_tcp.r2",
                "right_tcp.r3",
                "right_tcp.r4",
                "right_tcp.r5",
                "right_tcp.r6",
                "right_gripper.pos",
                "head.x",
                "head.y",
                "head.z",
                "head.r1",
                "head.r2",
                "head.r3",
                "head.r4",
                "head.r5",
                "head.r6"
            ],
            "shape": [
                29
            ]
        },
        "observation.state": {
            "dtype": "float32",
            "names": [
                "left_tcp.x",
                "left_tcp.y",
                "left_tcp.z",
                "left_tcp.r1",
                "left_tcp.r2",
                "left_tcp.r3",
                "left_tcp.r4",
                "left_tcp.r5",
                "left_tcp.r6",
                "left_gripper.pos",
                "right_tcp.x",
                "right_tcp.y",
                "right_tcp.z",
                "right_tcp.r1",
                "right_tcp.r2",
                "right_tcp.r3",
                "right_tcp.r4",
                "right_tcp.r5",
                "right_tcp.r6",
                "right_gripper.pos",
                "head.x",
                "head.y",
                "head.z",
                "head.r1",
                "head.r2",
                "head.r3",
                "head.r4",
                "head.r5",
                "head.r6"
            ],
            "shape": [
                29
            ]
        },
        "observation.images.left_tactile_left": {
            "dtype": "video",
            "shape": [
                400,
                700,
                3
            ],
            "names": [
                "height",
                "width",
                "channels"
            ],
            "info": {
                "video.height": 400,
                "video.width": 700,
                "video.codec": "h264",
                "video.pix_fmt": "yuv420p",
                "video.is_depth_map": false,
                "video.fps": 30,
                "video.channels": 3,
                "has_audio": false
            }
        },
        "observation.images.left_tactile_right": {
            "dtype": "video",
            "shape": [
                400,
                700,
                3
            ],
            "names": [
                "height",
                "width",
                "channels"
            ],
            "info": {
                "video.height": 400,
                "video.width": 700,
                "video.codec": "h264",
                "video.pix_fmt": "yuv420p",
                "video.is_depth_map": false,
                "video.fps": 30,
                "video.channels": 3,
                "has_audio": false
            }
        },
        "observation.images.left_wrist": {
            "dtype": "video",
            "shape": [
                480,
                640,
                3
            ],
            "names": [
                "height",
                "width",
                "channels"
            ],
            "info": {
                "video.height": 480,
                "video.width": 640,
                "video.codec": "h264",
                "video.pix_fmt": "yuv420p",
                "video.is_depth_map": false,
                "video.fps": 30,
                "video.channels": 3,
                "has_audio": false
            }
        },
        "observation.images.right_tactile_left": {
            "dtype": "video",
            "shape": [
                400,
                700,
                3
            ],
            "names": [
                "height",
                "width",
                "channels"
            ],
            "info": {
                "video.height": 400,
                "video.width": 700,
                "video.codec": "h264",
                "video.pix_fmt": "yuv420p",
                "video.is_depth_map": false,
                "video.fps": 30,
                "video.channels": 3,
                "has_audio": false
            }
        },
        "observation.images.right_tactile_right": {
            "dtype": "video",
            "shape": [
                400,
                700,
                3
            ],
            "names": [
                "height",
                "width",
                "channels"
            ],
            "info": {
                "video.height": 400,
                "video.width": 700,
                "video.codec": "h264",
                "video.pix_fmt": "yuv420p",
                "video.is_depth_map": false,
                "video.fps": 30,
                "video.channels": 3,
                "has_audio": false
            }
        },
        "observation.images.right_wrist": {
            "dtype": "video",
            "shape": [
                480,
                640,
                3
            ],
            "names": [
                "height",
                "width",
                "channels"
            ],
            "info": {
                "video.height": 480,
                "video.width": 640,
                "video.codec": "h264",
                "video.pix_fmt": "yuv420p",
                "video.is_depth_map": false,
                "video.fps": 30,
                "video.channels": 3,
                "has_audio": false
            }
        },
        "observation.images.head_left": {
            "dtype": "video",
            "shape": [
                480,
                640,
                3
            ],
            "names": [
                "height",
                "width",
                "channels"
            ],
            "info": {
                "video.height": 480,
                "video.width": 640,
                "video.codec": "h264",
                "video.pix_fmt": "yuv420p",
                "video.is_depth_map": false,
                "video.fps": 30,
                "video.channels": 3,
                "has_audio": false
            }
        },
        "observation.images.head_right": {
            "dtype": "video",
            "shape": [
                480,
                640,
                3
            ],
            "names": [
                "height",
                "width",
                "channels"
            ],
            "info": {
                "video.height": 480,
                "video.width": 640,
                "video.codec": "h264",
                "video.pix_fmt": "yuv420p",
                "video.is_depth_map": false,
                "video.fps": 30,
                "video.channels": 3,
                "has_audio": false
            }
        },
        "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
        },
        "observation.tracker_confidence": {
            "dtype": "float32",
            "names": [
                "left_tracker.confidence",
                "right_tracker.confidence"
            ],
            "shape": [
                2
            ]
        }
    },
    "tacflow_stage": "postprocess.anonymize",
    "tcp_frame_convention": "mirrored-v1"
}

Sensor key map

Sensor key map — each image stream keyed to its physical mount

Tactile keys map to fingertip sensors; wrist keys to gripper cameras; head keys to the headset's stereo eyes.

Citation

BibTeX:

@misc{tacverse2026,
  title        = {TacVerse: A Large-Scale Bimanual Visuo-Tactile Manipulation Dataset},
  author       = {Xense Robotics},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/TacVerse/TacVerse}},
  note         = {Collected with the XTac-UMI-G1 handheld gripper and Xense Neutron visuotactile sensors}
}
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