Learning Kinematic Feasibility for Mobile Manipulation through Deep Reinforcement Learning
This addresses a critical bottleneck for autonomous robots in service and industrial scenarios by enabling modular and generalizable mobile manipulation.
The paper tackles the challenge of generating kinematically feasible trajectories for mobile manipulation robots in dynamic environments by proposing a deep reinforcement learning approach that learns feasible dynamic motions for the mobile base while following task-space end-effector trajectories. The method demonstrated capabilities on multiple robot platforms in both simulated and real-world experiments.
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. This modular formulation has several benefits: it enables us to readily transform a broad range of end-effector motions into mobile applications, it allows us to use the kinematic feasibility of the end-effector trajectory as a dense reward signal and its modular formulation allows it to generalise to unseen end-effector motions at test time. We demonstrate the capabilities of our approach on multiple mobile robot platforms with different kinematic abilities and different types of wheeled platforms in extensive simulated as well as real-world experiments.