Bulletin of the American Physical Society
APS March Meeting 2024
Monday–Friday, March 4–8, 2024; Minneapolis & Virtual
Session PP02: V: Biological Physics at the Organismal and Population Scale
11:30 AM–1:18 PM,
Thursday, March 7, 2024
Room: Virtual Room 02
Sponsoring
Unit:
DBIO
Chair: Mirna Mihovilovic Skanata, Syracuse University; Ee Hou Yong, Nanyang Technological University
Abstract: PP02.00007 : Unveiling decentralized feedback mechanisms in sea star locomotion using deep reinforcement learning
12:42 PM–12:54 PM
Presenter:
Sina Heydari
(Santa Clara University, University of Southern California)
Authors:
Sina Heydari
(Santa Clara University, University of Southern California)
Josh Merel
(Meta Reality Labs)
Matthew McHenry
(University of California, Irvine)
Eva Kanso
(University of Southern California)
Here, we study tube-feet driven locomotion of sea stars in the context of a decentralized control model. We first construct mathematical models of tube feet as soft actuators consisting of force elements. We propose a decentralized control framework where individual tube feet have local autonomy over their actuation, via peripheral sensori-motor feedback loops. We then employ a deep reinforcement learning algorithm to discover the most effective feedback loops and mechanosensory cues for optimal locomotion. We find that the interaction of locally controlled tube feet leads to stable and robust locomotion with minimal cost to the nervous system in terms of sensory integration. Due to the decentralized nature of our proposed framework, we easily generalize our model to 500 tube feet and we show that the robustness of the model increases with the number of tube feet. To conclude, we comment on the utility of this system as a new paradigm for robotic movement using distributed arrays of soft actuators.
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