Programming Robots Using Reinforcement Learning and Teaching

Long-Ji Lin

Programming robots is a tedious task. So, there is growing interest in building robots which can learn by themselves. Self-improving, which involves trial and error, however, is often a slow process and could be hazardous in a hostile environment. By teaching robots how tasks can be achieved, learning time can be shortened and hazard can be minimized. This paper presents a general approach to making robots which can improve their performance from experiences as well as from being taught. Based on this proposed approach and other learning speedup techniques, a simulated learning robot was developed and could learn three moderately complex behaviors, which were then integrated in a subsumption style so that the robot could navigate and recharge itself. Interestingly, a real robot could actually use what was learned in the simulator to operate in the real world quite successfully.


This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.