Reinforcement Learning for Platform-Independent Visual Robot Control

—This paper proposes a new architecture for robot control. A test scenario is outlined to test the proposed system and enable a comparison with an existing system, which is able to fulfil the scenario and thus be used as a benchmark. The scenario is a navigation task, to allow a robot to approach a specified landmark. The proposed architecture will make use of two control units, one to allow a pan/tilt camera to track the landmark as the robot moves, and a second to control the robots drive motors. These units will be trained via reinforcement learning, and provide the potential for platformindependent robot control.

• Type: Conference
• Year: 2006
• Where: IJCNN
• Authors: David Muse, Kevin Burn, Stefan Wermter
• Keywords: IJCNN 2006, Robot, Robot Control, Scenario