Inferring Capabilities from Experiments

Abstract

We present an approach to enable an autonomous agent (learner) in building a model of a new unknown robot’s (subject) performance at a task through experimentation. The subject’s appearance can provide cues to its physical as well as cognitive capabilities. Building on these cues, our active experimentation approach learns a model that captures the effect of relevant extrinsic factors on the subject’s ability to perform a task. As personal robots become increasingly multi-functional and adaptive, such autonomous agents would find use as tools for humans in determining ”What can this robot do?”. We applied our algorithm in modelling a NAO and a Pepper robot at two different tasks. We first demonstrate the advantages of our active experimentation ap- proach, then we show the utility of such models in identifying scenarios a robot is well suited for, in performing a task.