Learning Kinematic Feasibility for Mobile Manipulation through Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning method that enables mobile robots to generate kinematically feasible dynamic motions for mobile manipulation tasks, improving adaptability in complex environments.

Contribution

It presents a modular deep RL framework that learns feasible motions for mobile bases while following arbitrary end-effector trajectories, generalizing to unseen motions.

Findings

Effective in simulation and real-world tests

Generalizes to unseen end-effector motions

Works across various robot platforms

Abstract

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.