Digitizing Touch with an Artificial Multimodal Fingertip
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| author | Lambeta, Mike Wu, Tingfan Sengul, Ali Most, Victoria Rose Black, Nolan Sawyer, Kevin Mercado, Romeo Qi, Haozhi Sohn, Alexander Taylor, Byron Tydingco, Norb Kammerer, Gregg Stroud, Dave Khatha, Jake Jenkins, Kurt Most, Kyle Stein, Neal Chavira, Ricardo Craven-Bartle, Thomas Sanchez, Eric Ding, Yitian Malik, Jitendra Calandra, Roberto |
| author_facet | Lambeta, Mike Wu, Tingfan Sengul, Ali Most, Victoria Rose Black, Nolan Sawyer, Kevin Mercado, Romeo Qi, Haozhi Sohn, Alexander Taylor, Byron Tydingco, Norb Kammerer, Gregg Stroud, Dave Khatha, Jake Jenkins, Kurt Most, Kyle Stein, Neal Chavira, Ricardo Craven-Bartle, Thomas Sanchez, Eric Ding, Yitian Malik, Jitendra Calandra, Roberto |
| contents | Touch is a crucial sensing modality that provides rich information about object properties and interactions with the physical environment. Humans and robots both benefit from using touch to perceive and interact with the surrounding environment (Johansson and Flanagan, 2009; Li et al., 2020; Calandra et al., 2017). However, no existing systems provide rich, multi-modal digital touch-sensing capabilities through a hemispherical compliant embodiment. Here, we describe several conceptual and technological innovations to improve the digitization of touch. These advances are embodied in an artificial finger-shaped sensor with advanced sensing capabilities. Significantly, this fingertip contains high-resolution sensors (~8.3 million taxels) that respond to omnidirectional touch, capture multi-modal signals, and use on-device artificial intelligence to process the data in real time. Evaluations show that the artificial fingertip can resolve spatial features as small as 7 um, sense normal and shear forces with a resolution of 1.01 mN and 1.27 mN, respectively, perceive vibrations up to 10 kHz, sense heat, and even sense odor. Furthermore, it embeds an on-device AI neural network accelerator that acts as a peripheral nervous system on a robot and mimics the reflex arc found in humans. These results demonstrate the possibility of digitizing touch with superhuman performance. The implications are profound, and we anticipate potential applications in robotics (industrial, medical, agricultural, and consumer-level), virtual reality and telepresence, prosthetics, and e-commerce. Toward digitizing touch at scale, we open-source a modular platform to facilitate future research on the nature of touch. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02479 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Digitizing Touch with an Artificial Multimodal Fingertip Lambeta, Mike Wu, Tingfan Sengul, Ali Most, Victoria Rose Black, Nolan Sawyer, Kevin Mercado, Romeo Qi, Haozhi Sohn, Alexander Taylor, Byron Tydingco, Norb Kammerer, Gregg Stroud, Dave Khatha, Jake Jenkins, Kurt Most, Kyle Stein, Neal Chavira, Ricardo Craven-Bartle, Thomas Sanchez, Eric Ding, Yitian Malik, Jitendra Calandra, Roberto Robotics Artificial Intelligence Machine Learning I.2.0; I.2.9 Touch is a crucial sensing modality that provides rich information about object properties and interactions with the physical environment. Humans and robots both benefit from using touch to perceive and interact with the surrounding environment (Johansson and Flanagan, 2009; Li et al., 2020; Calandra et al., 2017). However, no existing systems provide rich, multi-modal digital touch-sensing capabilities through a hemispherical compliant embodiment. Here, we describe several conceptual and technological innovations to improve the digitization of touch. These advances are embodied in an artificial finger-shaped sensor with advanced sensing capabilities. Significantly, this fingertip contains high-resolution sensors (~8.3 million taxels) that respond to omnidirectional touch, capture multi-modal signals, and use on-device artificial intelligence to process the data in real time. Evaluations show that the artificial fingertip can resolve spatial features as small as 7 um, sense normal and shear forces with a resolution of 1.01 mN and 1.27 mN, respectively, perceive vibrations up to 10 kHz, sense heat, and even sense odor. Furthermore, it embeds an on-device AI neural network accelerator that acts as a peripheral nervous system on a robot and mimics the reflex arc found in humans. These results demonstrate the possibility of digitizing touch with superhuman performance. The implications are profound, and we anticipate potential applications in robotics (industrial, medical, agricultural, and consumer-level), virtual reality and telepresence, prosthetics, and e-commerce. Toward digitizing touch at scale, we open-source a modular platform to facilitate future research on the nature of touch. |
| title | Digitizing Touch with an Artificial Multimodal Fingertip |
| topic | Robotics Artificial Intelligence Machine Learning I.2.0; I.2.9 |
| url | https://arxiv.org/abs/2411.02479 |