Touch begins where vision ends: Generalizable policies for contact-rich manipulation

• URL: http://arxiv.org/abs/2506.13762v1
• Date: Mon, 16 Jun 2025 17:59:48 GMT
• Title: Touch begins where vision ends: Generalizable policies for contact-rich manipulation
• Authors: Zifan Zhao, Siddhant Haldar, Jinda Cui, Lerrel Pinto, Raunaq Bhirangi,
• Abstract summary: We introduce VisuoTactile Local (ViTaL) policy learning, a framework that solves fine-grained manipulation tasks.<n>ViTaL achieves around 90% success on contact-rich tasks in unseen environments.
• Score: 18.195865256382334
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data-driven approaches struggle with precise manipulation; imitation learning requires many hard-to-obtain demonstrations, while reinforcement learning yields brittle, non-generalizable policies. We introduce VisuoTactile Local (ViTaL) policy learning, a framework that solves fine-grained manipulation tasks by decomposing them into two phases: a reaching phase, where a vision-language model (VLM) enables scene-level reasoning to localize the object of interest, and a local interaction phase, where a reusable, scene-agnostic ViTaL policy performs contact-rich manipulation using egocentric vision and tactile sensing. This approach is motivated by the observation that while scene context varies, the low-level interaction remains consistent across task instances. By training local policies once in a canonical setting, they can generalize via a localize-then-execute strategy. ViTaL achieves around 90% success on contact-rich tasks in unseen environments and is robust to distractors. ViTaL's effectiveness stems from three key insights: (1) foundation models for segmentation enable training robust visual encoders via behavior cloning; (2) these encoders improve the generalizability of policies learned using residual RL; and (3) tactile sensing significantly boosts performance in contact-rich tasks. Ablation studies validate each of these insights, and we demonstrate that ViTaL integrates well with high-level VLMs, enabling robust, reusable low-level skills. Results and videos are available at https://vitalprecise.github.io.

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