TLA: Tactile-Language-Action Model for Contact-Rich Manipulation

• URL: http://arxiv.org/abs/2503.08548v1
• Date: Tue, 11 Mar 2025 15:36:28 GMT
• Title: TLA: Tactile-Language-Action Model for Contact-Rich Manipulation
• Authors: Peng Hao, Chaofan Zhang, Dingzhe Li, Xiaoge Cao, Xiaoshuai Hao, Shaowei Cui, Shuo Wang,
• Abstract summary: We introduce the Tactile-Language-Action model, which processes sequential tactile feedback via cross-modal language grounding.<n>We construct a comprehensive dataset that contains 24k pairs of tactile action instruction data, customized for fingertip peg-in-hole assembly.<n>Results show that TLA significantly outperforms traditional imitation learning methods in terms of effective action generation and action accuracy.
• Score: 9.97307182748107
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Significant progress has been made in vision-language models. However, language-conditioned robotic manipulation for contact-rich tasks remains underexplored, particularly in terms of tactile sensing. To address this gap, we introduce the Tactile-Language-Action (TLA) model, which effectively processes sequential tactile feedback via cross-modal language grounding to enable robust policy generation in contact-intensive scenarios. In addition, we construct a comprehensive dataset that contains 24k pairs of tactile action instruction data, customized for fingertip peg-in-hole assembly, providing essential resources for TLA training and evaluation. Our results show that TLA significantly outperforms traditional imitation learning methods (e.g., diffusion policy) in terms of effective action generation and action accuracy, while demonstrating strong generalization capabilities by achieving over 85\% success rate on previously unseen assembly clearances and peg shapes. We publicly release all data and code in the hope of advancing research in language-conditioned tactile manipulation skill learning. Project website: https://sites.google.com/view/tactile-language-action/

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