Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing

• URL: http://arxiv.org/abs/2312.01853v2
• Date: Sun, 10 Dec 2023 09:17:17 GMT
• Title: Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing
• Authors: Ying Yuan, Haichuan Che, Yuzhe Qin, Binghao Huang, Zhao-Heng Yin, Kang-Won Lee, Yi Wu, Soo-Chul Lim, Xiaolong Wang
• Abstract summary: We introduce a system that leverages visual and tactile sensory inputs to enable dexterous in-hand manipulation. Robot Synesthesia is a novel point cloud-based tactile representation inspired by human tactile-visual synesthesia.
• Score: 16.570647733532173
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Executing contact-rich manipulation tasks necessitates the fusion of tactile and visual feedback. However, the distinct nature of these modalities poses significant challenges. In this paper, we introduce a system that leverages visual and tactile sensory inputs to enable dexterous in-hand manipulation. Specifically, we propose Robot Synesthesia, a novel point cloud-based tactile representation inspired by human tactile-visual synesthesia. This approach allows for the simultaneous and seamless integration of both sensory inputs, offering richer spatial information and facilitating better reasoning about robot actions. The method, trained in a simulated environment and then deployed to a real robot, is applicable to various in-hand object rotation tasks. Comprehensive ablations are performed on how the integration of vision and touch can improve reinforcement learning and Sim2Real performance. Our project page is available at https://yingyuan0414.github.io/visuotactile/ .

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