Learning Kinematic Feasibility for Mobile Manipulation through Deep Reinforcement Learning

Understand

Mobile manipulation tasks remain one of the critical challenges for the widespread adoption of autonomous robots in both service and industrial scenarios.

• While planning approaches are good at generating feasible whole-body robot trajectories, they struggle with dynamic environments as well as the incorporation of constraints given by the task and the environment.
• On the other hand, dynamic motion models in the action space struggle with generating kinematically feasible trajectories for mobile manipulation actions.
• We propose a deep reinforcement learning approach to learn feasible dynamic motions for a mobile base while the end-effector follows a trajectory in task space generated by an arbitrary system to fulfill the task at hand.