VLA-Touch: Enhancing Vision-Language-Action Models with Dual-Level Tactile Feedback, Jianxin Bi★, Kevin Yuchen Ma, Ce Hao★, Mike Shou Zheng, and Harold Soh★, arXiv preprint
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Abstract: Tactile feedback is generally recognized to be crucial for effective interaction with the physical world. However, state-of-the-art Vision-Language-Action (VLA) models lack the ability to interpret and use tactile signals, limiting their effectiveness in contact-rich tasks. Incorporating tactile feedback into these systems is challenging due to the absence of large multi-modal datasets. We present VLA-Touch, an approach that enhances generalist robot policies with tactile sensing without fine-tuning the base VLA. Our method introduces two key innovations: (1) a pipeline that leverages a pretrained tactile-language model that provides semantic tactile feedback for high-level task planning, and (2) a diffusion-based controller that refines VLA-generated actions with tactile signals for contact-rich manipulation. Through real-world experiments, we demonstrate that our dual-level integration of tactile feedback improves task planning efficiency while enhancing execution precision.
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Jianxin Bi★, Kevin Yuchen Ma, Ce Hao★, Mike Shou Zheng, and Harold Soh★, “VLA-Touch: Enhancing Vision-Language-Action Models with Dual-Level Tactile Feedback”, arXiv preprint
@misc{bi2025vlatouchenhancingvisionlanguageactionmodels, title = {VLA-Touch: Enhancing Vision-Language-Action Models with Dual-Level Tactile Feedback}, author = {Bi, Jianxin and Ma, Kevin Yuchen and Hao, Ce and Shou, Mike Zheng and Soh, Harold}, year = {2025}, eprint = {2507.17294}, archiveprefix = {arXiv}, primaryclass = {cs.RO}, url = {https://arxiv.org/abs/2507.17294}, }
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