Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing
  • Yuan, Ying
  • Che, Haichuan
  • Qin, Yuzhe
  • Huang, Binghao
  • Yin, Zhao-Heng
  • ... Lim, Soo-Chul
  • 외 3명
Citations

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18
Citations

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23

초록

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 cloudbased 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. 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/. © 2024 IEEE.

키워드

Adversarial Machine LearningMedical RoboticsMicrorobotsNanoroboticsNanorobotsRobot LearningRobot VisionSensory FeedbackVisual ServoingCloud-basedHand ManipulationManipulation TaskRobot ActionsSeamless IntegrationSensory InputSimultaneous IntegrationSpatial InformationsTactile FeedbackVisual FeedbackReinforcement Learning
제목
Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing
저자
Yuan, YingChe, HaichuanQin, YuzheHuang, BinghaoYin, Zhao-HengLee, Kang-WonWu, YiLim, Soo-ChulWang, Xiaolong
DOI
10.1109/ICRA57147.2024.10610532
발행일
2024-08
유형
Proceedings Paper
저널명
Proceedings - IEEE International Conference on Robotics and Automation
페이지
6558 ~ 6565