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arXiv · 2506.01941

FreeTacMan: Robot-free Visuo-Tactile Data Collection System for Contact-rich Manipulation

Longyan Wu·Checheng Yu·Jieji Ren·Li Chen·Yufei Jiang·Ran Huang·Guoying Gu·Hongyang Li
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Abstract

Enabling robots with contact-rich manipulation remains a pivotal challenge in robot learning, which is substantially hindered by the data collection gap, including its inefficiency and limited sensor setup. While prior work has explored handheld paradigms, their rod-based mechanical structures remain rigid and unintuitive, providing limited tactile feedback and posing challenges for operators. Motivated by the dexterity and force feedback of human motion, we propose FreeTacMan, a human-centric and robot-free data collection system for accurate and efficient robot manipulation. Concretely, we design a wearable gripper with visuo-tactile sensors for data collection, which can be worn by human fingers for intuitive control. A high-precision optical tracking system is introduced to capture end-effector poses while synchronizing visual and tactile feedback simultaneously. We leverage FreeTacMan to collect a large-scale multimodal dataset, comprising over 3000k paired visuo-tactile images with end-effector poses, 10k demonstration trajectories across 50 diverse contact-rich manipulation tasks. FreeTacMan achieves multiple improvements in data collection performance over prior works and enables effective policy learning from self-collected datasets. By open-sourcing the hardware and the dataset, we aim to facilitate reproducibility and support research in visuo-tactile manipulation.

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Longyan Wu, Checheng Yu, Jieji Ren, Li Chen, Yufei Jiang, Ran Huang, Guoying Gu, Hongyang Li. 2025-06-02. FreeTacMan: Robot-free Visuo-Tactile Data Collection System for Contact-rich Manipulation. https://arxiv.org/abs/2506.01941

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