Neural reservoir control of a soft bio-hybrid arm
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913733033328640 |
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| author | Naughton, Noel Tekinalp, Arman Shivam, Keshav Kim, Seung Hung Kindratenko, Volodymyr Gazzola, Mattia |
| author_facet | Naughton, Noel Tekinalp, Arman Shivam, Keshav Kim, Seung Hung Kindratenko, Volodymyr Gazzola, Mattia |
| contents | A long-standing engineering problem, the control of soft robots is difficult because of their highly non-linear, heterogeneous, anisotropic, and distributed nature. Here, bridging engineering and biology, a neural reservoir is employed for the dynamic control of a bio-hybrid model arm made of multiple muscle-tendon groups enveloping an elastic spine. We show how the use of reservoirs facilitates simultaneous control and self-modeling across a set of challenging tasks, outperforming classic neural network approaches. Further, by implementing a spiking reservoir on neuromorphic hardware, energy efficiency is achieved, with nearly two-orders of magnitude improvement relative to standard CPUs, with implications for the on-board control of untethered, small-scale soft robots. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09477 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural reservoir control of a soft bio-hybrid arm Naughton, Noel Tekinalp, Arman Shivam, Keshav Kim, Seung Hung Kindratenko, Volodymyr Gazzola, Mattia Robotics Machine Learning Neural and Evolutionary Computing A long-standing engineering problem, the control of soft robots is difficult because of their highly non-linear, heterogeneous, anisotropic, and distributed nature. Here, bridging engineering and biology, a neural reservoir is employed for the dynamic control of a bio-hybrid model arm made of multiple muscle-tendon groups enveloping an elastic spine. We show how the use of reservoirs facilitates simultaneous control and self-modeling across a set of challenging tasks, outperforming classic neural network approaches. Further, by implementing a spiking reservoir on neuromorphic hardware, energy efficiency is achieved, with nearly two-orders of magnitude improvement relative to standard CPUs, with implications for the on-board control of untethered, small-scale soft robots. |
| title | Neural reservoir control of a soft bio-hybrid arm |
| topic | Robotics Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2503.09477 |