Whisker-Inspired Tactile Sensing: A Sim2Real Approach for Precise Underwater Contact Tracking
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917808031399936 |
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| author | Li, Hao Xing, Chengyi Khan, Saad Zhong, Miaoya Cutkosky, Mark R. |
| author_facet | Li, Hao Xing, Chengyi Khan, Saad Zhong, Miaoya Cutkosky, Mark R. |
| contents | Aquatic mammals, such as pinnipeds, utilize their whiskers to detect and discriminate objects and analyze water movements, inspiring the development of robotic whiskers for sensing contacts, surfaces, and water flows. We present the design and application of underwater whisker sensors based on Fiber Bragg Grating (FBG) technology. These passive whiskers are mounted along the robot$'$s exterior to sense its surroundings through light, non-intrusive contacts. For contact tracking, we employ a sim-to-real learning framework, which involves extensive data collection in simulation followed by a sim-to-real calibration process to transfer the model trained in simulation to the real world. Experiments with whiskers immersed in water indicate that our approach can track contact points with an accuracy of $<2$ mm, without requiring precise robot proprioception. We demonstrate that the approach also generalizes to unseen objects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14005 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Whisker-Inspired Tactile Sensing: A Sim2Real Approach for Precise Underwater Contact Tracking Li, Hao Xing, Chengyi Khan, Saad Zhong, Miaoya Cutkosky, Mark R. Robotics Artificial Intelligence Aquatic mammals, such as pinnipeds, utilize their whiskers to detect and discriminate objects and analyze water movements, inspiring the development of robotic whiskers for sensing contacts, surfaces, and water flows. We present the design and application of underwater whisker sensors based on Fiber Bragg Grating (FBG) technology. These passive whiskers are mounted along the robot$'$s exterior to sense its surroundings through light, non-intrusive contacts. For contact tracking, we employ a sim-to-real learning framework, which involves extensive data collection in simulation followed by a sim-to-real calibration process to transfer the model trained in simulation to the real world. Experiments with whiskers immersed in water indicate that our approach can track contact points with an accuracy of $<2$ mm, without requiring precise robot proprioception. We demonstrate that the approach also generalizes to unseen objects. |
| title | Whisker-Inspired Tactile Sensing: A Sim2Real Approach for Precise Underwater Contact Tracking |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2410.14005 |