--- annotations_creators: - expert-generated language: - en license: mit size_categories: - 10K - **Paper:** — *FreeTacMan: Robot-free Visuo-Tactile Data Collection System for Contact-rich Manipulation* (ICRA 2026) ## Uses ### Direct Use This dataset is intended for research on visuo-tactile robot learning, including: - Imitation learning of contact-rich manipulation policies (e.g. Action Chunking Transformer-style models) that fuse wrist-camera RGB with optical tactile sensor observations and 6-DoF end-effector trajectories. - Tactile representation / contrastive pretraining of a tactile encoder aligned with a visual encoder, as described in the source paper. - Benchmarking tactile-vs-vision-only policies on contact-rich tasks (e.g. slip detection, force-sensitive grasping, precise insertion). ### Out-of-Scope Use [More Information Needed]. The source paper notes the tactile sensor is an optical (camera-based) gel-deformation sensor rather than a calibrated force/torque sensor, so the dataset should not be treated as ground-truth contact force measurements. The paper's own experiments were validated on a PIPER 6-DoF arm; direct transfer of learned policies to other embodiments has not been established by the source paper. ## Dataset Structure **Dataset name:** `FreeTacMan` **Media type:** `group` **Default group slice:** `tactile_sensor_1` ### Summary | Property | Value | | --- | --- | | Groups (trajectories) | 6,228 | | Video samples (total) | 18,084 | | Tasks | 45 | | Group slices | `tactile_sensor_1`, `tactile_sensor_2`, `fisheye_camera` | ### Groups and slices Each group is one demonstration trajectory. The linked slices are the synchronized streams recorded during that demonstration: | Slice | Media type | Samples | Description | | --- | --- | --- | --- | | `tactile_sensor_1` (default) | video | 6,228 | First optical tactile sensor stream | | `tactile_sensor_2` | video | 6,228 | Second optical tactile sensor stream | | `fisheye_camera` | video | 5,628 | Wrist-mounted fisheye RGB camera stream | `fisheye_camera` is missing from ~600 groups because a small subset of tasks were recorded with only the two tactile-sensor streams and no wrist camera. Switch slices in the FiftyOne App to compare the tactile imprints against the RGB view for the same demonstration. Videos are transcoded to H.264 / `yuv420p` for in-App playback; the source videos are MPEG-4 Part 2. ### Sample-level fields | Field | Type | Description | | --- | --- | --- | | `trajectory_id` | string | Unique id, e.g. `Hold_3` (`{task}_{demo_idx}`) | | `task` | `fo.Classification` | Task name, e.g. `PourWater` | | `demo_idx` | int | Demonstration index within the task | | `camera` | string | Original source camera id for this sample (`camera1`/`camera2`/`camera3`; `camera1` maps to `tactile_sensor_1`, `camera2` to `tactile_sensor_2`, `camera3` to `fisheye_camera`) | | `num_timesteps` | int | Number of trajectory timesteps for this demo | ### Frame-level fields The 6-DoF end-effector trajectory is replicated on every stream in the group, so proprioception can be read off any slice frame-by-frame. Per the source paper, these values come from an OptiTrack motion-capture system tracking the in-situ gripper interface at 300 Hz, downsampled and synchronized to the 30 Hz camera frame rate: | Field | Type | Description | | --- | --- | --- | | `timestamp` | float | Unix timestamp of the frame | | `tcp_pos_x` / `tcp_pos_y` / `tcp_pos_z` | float | TCP (tool center point) position | | `tcp_euler_x` / `tcp_euler_y` / `tcp_euler_z` | float | TCP orientation (Euler angles) | | `quat_w` / `quat_x` / `quat_y` / `quat_z` | float | TCP orientation (quaternion) | | `gripper_distance` | float | Gripper opening distance | ### Tasks This dataset spans 45 contact-rich manipulation tasks, including `ArrangeFruit`, `CakePiping`, `CutBanana`, `PourWater`, `ScrewInTheBulb`, `SqueezeToothpaste`, `ThreadNeedle`, `UsbPlug`, `WipeBoard`, `Write`, and more. ```python from fiftyone import ViewField as F # All demonstrations of a single task pour = dataset.match(F("task.label") == "PourWater") # Just the tactile view for every demonstration tactile = dataset.select_group_slices("tactile_sensor_1") ``` ## Dataset Creation ### Curation Rationale Existing demonstration-collection setups for contact-rich manipulation are either expensive/complex real-robot teleoperation rigs (motion-capture, VR/AR, primary-replica arms) or handheld interfaces whose multi-link trigger-based grippers introduce mechanical backlash that blurs tactile cues. FreeTacMan was built to eliminate that backlash: a wearable, in-situ visuo-tactile sensor sits directly at the operator's fingertip, giving zero-mechanical-attenuation tactile feedback while remaining robot-free and cross-embodiment. The dataset was curated by collecting many contact-rich manipulation demonstrations with this system to support tactile-conditioned imitation learning research. ### Source Data #### Data Collection and Processing Each demonstration frame pairs a wrist-mounted fisheye-camera RGB image (180° field of view, 640×480 @ 30 FPS) with two camera-based optical tactile sensor images (640×480 @ 30 FPS, one per fingertip), plus the 6-DoF end -effector pose and gripper width. End-effector pose is derived from an OptiTrack motion-capture system tracking five retroreflective markers on the in-situ gripper interface at 300 Hz (mean tracking error 0.118 mm), with marker coordinates transformed into the robot base frame and downsampled to synchronize with the 30 Hz camera streams. For this FiftyOne release, the source per-task directories of `{task}_{demo_idx}_camera{1,2,3}.mp4` videos and `{task}_{demo_idx}_traj.csv` trajectory files were parsed into a grouped video dataset: each trajectory CSV's 12 columns were mapped to per-frame fields, source MPEG-4 Part 2 videos were transcoded to H.264/`yuv420p` MP4 for in-App playback, and each demonstration's camera streams were joined into a `fo.Group()` with slices renamed to `tactile_sensor_1`, `tactile_sensor_2`, and `fisheye_camera`. #### Who are the source data producers? Demonstrations were collected by human operators wearing/holding the FreeTacMan wearable interface. The source paper's user study describes 12 volunteer operators collecting demonstrations across 8 tasks to evaluate the data-collection system itself; [More Information Needed] on the exact number and identity of operators who produced the full 45-task, 6,228-trajectory dataset released publicly, as this is a larger-scale release beyond the paper's user study. ### Annotations #### Annotation process There is no manual human annotation. The `task` label is assigned from the source directory/file naming convention (one folder per task). All frame -level trajectory fields (`tcp_pos_*`, `tcp_euler_*`, `quat_*`, `gripper_distance`, `timestamp`) are captured automatically by the OptiTrack motion-capture system described above, not hand-labeled. #### Who are the annotators? Not applicable — trajectory fields are sensor/motion-capture output rather than human annotations. [More Information Needed] on task-label assignment beyond the directory-naming convention. #### Personal and Sensitive Information [More Information Needed]. The source paper does not discuss personal or sensitive information; recordings are of a wrist-mounted camera and fingertip-mounted tactile sensors during tabletop manipulation tasks. ## Citation If you use the source FreeTacMan dataset, cite: **BibTeX:** ```bibtex @article{wu2025freetacman, title = {FreeTacMan: Robot-free Visuo-Tactile Data Collection System for Contact-rich Manipulation}, author = {Wu, Longyan and Yu, Checheng and Ren, Jieji and Chen, Li and Jiang, Yufei and Huang, Ran and Gu, Guoying and Li, Hongyang}, journal = {IEEE International Conference on Robotics and Automation (ICRA)}, year = {2026} } ``` **APA:** Wu, L., Yu, C., Ren, J., Chen, L., Jiang, Y., Huang, R., Gu, G., & Li, H. (2026). FreeTacMan: Robot-free visuo-tactile data collection system for contact-rich manipulation. *IEEE International Conference on Robotics and Automation (ICRA)*. ## More Information - Project page: - Code: - Hardware guide: - Video: - Source dataset contact: Longyan Wu (im.longyanwu@gmail.com) ## Dataset Card Authors [Harpreet Sahota](https://huggingface.co/harpreetsahota) (FiftyOne conversion and card)