Programming robots using reinforcement learning and teaching

Abstract

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.