Adaptation of the Perception-Action Loop Using Active Channel Sampling

During the lifetime of a real world agent or robot, many changes unforeseen at design time can occur. Whether these are due to a change in environmental conditions or to alterations of the embodiment of the robot, flexibility and adaptation are essential qualities that can help it to keep operating in this new situation. This work is based on an information-theoretic approach and introduces an exploration strategy that allows an agent to detect and adapt to changes in its perception-action loop by actively sampling areas of interest. We define the problem of exploring the sensorimotor channel and establish a measure of the distance between the observed and the real model of the channel. An optimal Oracle-based strategy is used to compare performances of the adaptive sampling strategy and a random strategy. Results for different scenarios of change in a binary channel show that the proposed strategy is highly effective in many cases. We also outline principles to adapt this mechanism to the exploration of multiple channels and we give preliminary results for such a scenario.