Massively Parallel Imitation Learning of Mouse Forelimb Musculoskeletal Reaching Dynamics.

ABSTRACT: The brain has evolved to effectively control the body, and in order to understand the relationship we need to model the sensorimotor transformations underlying embodied control. As part of a coordinated effort, we are developing a general-purpose platform for data-driven simulation modeling high fidelity behavioral dynamics, biomechanics, and neural circuit architectures underlying embodied control. We present a pipeline for taking kinematics data from the neuroscience lab and creating a pipeline for recapitulating those natural movements in physics simulation. We implement an imitation learning framework to simulate a dexterous forelimb reaching task with a musculoskeletal model in the Mujoco physics environment. The imitation learning model is currently training at more than 1 million tr

SUBMITTER: Leonardis E 

PROVIDER: S-EPMC12676374 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature