Michael Beetz, Thorsten Belker
This paper investigates the problem of improving the performance of general state-of-the-art robot control systems by autonomously adapting them to specific tasks and environments. We propose model- and test-based transformational learning (MTTL) as a computational model for performing this task. MTTL uses abstract models of control systems and environments in order to propose promising adaptations. To account for model deficiencies resulting from abstraction, hypotheses are statistically tested based on experimentation in the physical world. We describe XFRM-Learn, an implementation of MTTL, and apply it to the problem of indoor navigation. We present experiments in which XFRM-Learn improves the navigation performance of a state-of-the-art high-speed navigation system for a given set of navigation tasks by at least 29 percent.
Keywords: Robotics, Autonomous Agents, Adaptive Systems
Citation: Michael Beetz, Thorsten Belker: Autonomous Environment and Task Adaptation for Robotic Agents. In W.Horn (ed.): ECAI2000, Proceedings of the 14th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2000, pp.648-652.