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

janelia7_blocks-janelia7_biblio_header | block

bioRxiv. 2024 Nov 22;. doi: 10.1101/2024.03.11.584515

Whole-body simulation of realistic fruit fly locomotion with deep reinforcement learning Branson LabTuraga LabReiser LabProject Technical ResourcesScientific Computing Software

Vaxenburg R, Siwanowicz I, Merel J, Robie AA, Morrow C, Novati G, Stefanidi Z, Both G, Card GM, Reiser MB, Botvinick MM, Branson KM, Tassa Y, Turaga SC

janelia7_blocks-janelia7_biblio_abstract | block

Abstract

The body of an animal influences 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. We demonstrate the use of visual sensors and the re-use of a pre-trained general-purpose flight controller by training the model to perform visually guided flight tasks. 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.

janelia7_blocks-janelia7_biblio_authors | block

janelia7_blocks-janelia7_biblio_tools | block

Main Menu (Mobile)- Block

Main Menu - Block

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

Abstract

Janelia Authors