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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2601.21548 |
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| _version_ | 1866917231397437440 |
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| author | Ambrosini, Irene Blakowski, Ingo Zendrikov, Dmitrii Capone, Cristiano Gava, Luna Indiveri, Giacomo De Luca, Chiara Bartolozzi, Chiara |
| author_facet | Ambrosini, Irene Blakowski, Ingo Zendrikov, Dmitrii Capone, Cristiano Gava, Luna Indiveri, Giacomo De Luca, Chiara Bartolozzi, Chiara |
| contents | Air hockey demands split-second decisions at high puck velocities, a challenge we address with a compact network of spiking neurons running on a mixed-signal analog/digital neuromorphic processor. By co-designing hardware and learning algorithms, we train the system to achieve successful puck interactions through reinforcement learning in a remarkably small number of trials. The network leverages fixed random connectivity to capture the task's temporal structure and adopts a local e-prop learning rule in the readout layer to exploit event-driven activity for fast and efficient learning. The result is real-time learning with a setup comprising a computer and the neuromorphic chip in-the-loop, enabling practical training of spiking neural networks for robotic autonomous systems. This work bridges neuroscience-inspired hardware with real-world robotic control, showing that brain-inspired approaches can tackle fast-paced interaction tasks while supporting always-on learning in intelligent machines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21548 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Training slow silicon neurons to control extremely fast robots with spiking reinforcement learning Ambrosini, Irene Blakowski, Ingo Zendrikov, Dmitrii Capone, Cristiano Gava, Luna Indiveri, Giacomo De Luca, Chiara Bartolozzi, Chiara Robotics Artificial Intelligence Emerging Technologies Air hockey demands split-second decisions at high puck velocities, a challenge we address with a compact network of spiking neurons running on a mixed-signal analog/digital neuromorphic processor. By co-designing hardware and learning algorithms, we train the system to achieve successful puck interactions through reinforcement learning in a remarkably small number of trials. The network leverages fixed random connectivity to capture the task's temporal structure and adopts a local e-prop learning rule in the readout layer to exploit event-driven activity for fast and efficient learning. The result is real-time learning with a setup comprising a computer and the neuromorphic chip in-the-loop, enabling practical training of spiking neural networks for robotic autonomous systems. This work bridges neuroscience-inspired hardware with real-world robotic control, showing that brain-inspired approaches can tackle fast-paced interaction tasks while supporting always-on learning in intelligent machines. |
| title | Training slow silicon neurons to control extremely fast robots with spiking reinforcement learning |
| topic | Robotics Artificial Intelligence Emerging Technologies |
| url | https://arxiv.org/abs/2601.21548 |