Learning Kinematic Feasibility for Mobile Manipulation Through Deep Reinforcement Learning

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.

I. Introduction

Mobile manipulation is a key research area on the journey to both autonomous household assistants as well as flexible automation processes and warehouse logistics. Although impressive results have been achieved over the last years [1]–[4], there remain multiple unsolved research problems. One of the major ones being that most current approaches separate navigation and manipulation due to the difficulties in planning the joint movement of the robot base and its end-effector (EE). This restricts the range of tasks that can be solved and constrains the overall efficiency that can be achieved. Typically, the tasks that a robot is expected to perform are linked to conditions in the task space, such as poses at which handled objects can be grasped, orientation that objects should maintain or entire trajectories that must be followed. While there are techniques to position a manipulator to fulfill various task constraints with respect to the kinematics of the robot, based on inverse reachability maps (IRM) [5], performing such tasks while moving the base still remains an unsolved problem.