Reinforcement Learning for Robot Locomotion Manipulation Navigation

Learning and Control of Hamiltonian System Dynamics

Learning and Control of Hamiltonian System Dynamics We previously developed a port-Hamiltonian dynamics model, which was trained as a neural ordinary differential equation (ODE) neural network and subsequently used for energy-shaping control to achieve stabilization and tracking. Recently we have extended our method in two major directions. In work published at ICRA 2024 (Altawaitan et […]

Efficient Reinforcement Learning for Robotic Control

By Angela Berti13 August 2024RoboticsCollaborative and Contextual Robotics, Reinforcement Learning for Robot Locomotion Manipulation Navigation

Efficient Reinforcement Learning for Robotic Control We design efficient reinforcement learning (RL) for generalizing on diverse tasks and environments, as well as for generalizing from simulation to real robots. We specifically made two efforts along this direction: TD-MPC2 Building on our previous effort on TD-MPC, a model-based reinforcement learning algorithm that optimizes local trajectories in […]