AnySkin: Plug-and-play Skin Sensing for Robotic Touch
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929518026948608 |
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| author | Bhirangi, Raunaq Pattabiraman, Venkatesh Erciyes, Enes Cao, Yifeng Hellebrekers, Tess Pinto, Lerrel |
| author_facet | Bhirangi, Raunaq Pattabiraman, Venkatesh Erciyes, Enes Cao, Yifeng Hellebrekers, Tess Pinto, Lerrel |
| contents | While tactile sensing is widely accepted as an important and useful sensing modality, its use pales in comparison to other sensory modalities like vision and proprioception. AnySkin addresses the critical challenges that impede the use of tactile sensing -- versatility, replaceability, and data reusability. Building on the simplistic design of ReSkin, and decoupling the sensing electronics from the sensing interface, AnySkin simplifies integration making it as straightforward as putting on a phone case and connecting a charger. Furthermore, AnySkin is the first uncalibrated tactile-sensor with cross-instance generalizability of learned manipulation policies. To summarize, this work makes three key contributions: first, we introduce a streamlined fabrication process and a design tool for creating an adhesive-free, durable and easily replaceable magnetic tactile sensor; second, we characterize slip detection and policy learning with the AnySkin sensor; and third, we demonstrate zero-shot generalization of models trained on one instance of AnySkin to new instances, and compare it with popular existing tactile solutions like DIGIT and ReSkin. Videos of experiments, fabrication details and design files can be found on https://any-skin.github.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08276 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AnySkin: Plug-and-play Skin Sensing for Robotic Touch Bhirangi, Raunaq Pattabiraman, Venkatesh Erciyes, Enes Cao, Yifeng Hellebrekers, Tess Pinto, Lerrel Robotics Artificial Intelligence While tactile sensing is widely accepted as an important and useful sensing modality, its use pales in comparison to other sensory modalities like vision and proprioception. AnySkin addresses the critical challenges that impede the use of tactile sensing -- versatility, replaceability, and data reusability. Building on the simplistic design of ReSkin, and decoupling the sensing electronics from the sensing interface, AnySkin simplifies integration making it as straightforward as putting on a phone case and connecting a charger. Furthermore, AnySkin is the first uncalibrated tactile-sensor with cross-instance generalizability of learned manipulation policies. To summarize, this work makes three key contributions: first, we introduce a streamlined fabrication process and a design tool for creating an adhesive-free, durable and easily replaceable magnetic tactile sensor; second, we characterize slip detection and policy learning with the AnySkin sensor; and third, we demonstrate zero-shot generalization of models trained on one instance of AnySkin to new instances, and compare it with popular existing tactile solutions like DIGIT and ReSkin. Videos of experiments, fabrication details and design files can be found on https://any-skin.github.io/ |
| title | AnySkin: Plug-and-play Skin Sensing for Robotic Touch |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2409.08276 |