Whole-body simulation of realistic fruit fly locomotion with deep reinforcement learning

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bioRxiv. 2024 Mar 14;. doi: 10.1101/2024.03.11.584515

Whole-body simulation of realistic fruit fly locomotion with deep reinforcement learning Card LabReiser LabBranson LabTuraga LabProject Technical Resources

Vaxenburg Roman, Siwanowicz Igor, Merel Josh, Robie Alice, Morrow Carmen, Novati Guido, Stefanidi Zinovia, Card Gwyneth, Reiser Michael, Botvinick Matthew, Branson Kristin, Tassa Yuval, Turaga Srinivas

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Abstract

The body of an animal determines how the nervous system produces behavior. Therefore, detailed modeling of the neural control of sensorimotor behavior requires a detailed model of the body. Here we contribute an anatomically-detailed biomechanical whole-body model of the fruit fly Drosophila melanogaster in the MuJoCo physics engine. Our model is general-purpose, enabling the simulation of diverse fly behaviors, both on land and in the air. We demonstrate the generality of our model by simulating realistic locomotion, both flight and walking. To support these behaviors, we have extended MuJoCo with phenomenological models of fluid forces and adhesion forces. Through data-driven end-to-end reinforcement learning, we demonstrate that these advances enable the training of neural network controllers capable of realistic locomotion along complex trajectories based on high-level steering control signals. With a visually guided flight task, we demonstrate a neural controller that can use the vision sensors of the body model to control and steer flight. Our project is an open-source platform for modeling neural control of sensorimotor behavior in an embodied context.Competing Interest StatementThe authors have declared no competing interest.

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bioRxiv preprint http://biorxiv.org/content/early/2024/03/14/2024.03.11.584515.abstract

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Whole-body simulation of realistic fruit fly locomotion with deep reinforcement learning

Abstract

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