Robot Dynamics Learning

Robot dynamics learning studies how to predict a robot’s motion and its interactions with objects and environments from states, actions, and sensor data. Methods range from physics-informed models such as Lagrangian and Koopman networks to recurrent models, graph neural networks, Gaussian processes, and learned world models; many combine physical structure with neural residuals or uncertainty estimates. These models support model-predictive control, reinforcement learning, manipulation, and sim-to-real transfer, with evaluation spanning MuJoCo and manipulation benchmarks as well as real arms, legged robots, and soft robots. A central challenge is maintaining accurate, stable long-horizon predictions through contact and changing conditions while keeping models efficient and transferable across tasks and robot morphologies.