Using policy gradient reinforcement learning on autonomous robot controllers

Robot programmers can often quickly program a robot to approximately execute a task under specific environment conditions. However, achieving robust performance under more general conditions is significantly more difficult. We propose a framework that starts with an existing control system and uses reinforcement feedback from the environment to autonomously improve the controller’s performance. We use the Policy Gradient Reinforcement Learning (PGRL) framework, which estimates a gradient (in controller space) of improved reward, allowing the controller parameters to be incrementally updated to autonomously achieve locally optimal performance. Our approach is experimentally verified on a Cye robot executing a room entry and observation task, showing significant reduction in task execution time and robustness with respect to un-modelled changes in the environment

A Framework and Architecture for Multi-Robot Coordination

In this paper, we present a framework and the software architecture for the deployment of multiple autonomous robots in an unstructured and unknown environment with applications ranging from scouting and reconnaissance, to search and rescue and manipulation tasks. Our software framework provides the methodology and the tools that enable robots to exhibit deliberative and reactive behaviors in autonomous operation, to be reprogrammed by a human operator at run-time, and to learn and adapt to unstructured, dynamic environments and new tasks, while providing performance guarantees. We demonstrate the algorithms and software on an experimental testbed that involves a team of car-like robots using a single omnidirectional camera as a sensor without explicit use of odometry