Learning Policy for Robot Anomaly Recovery Based on Robot Introspection

Abstract

In this chapter, the anomaly recovery would be acted when both of the anomaly monitoring and diagnoses are analysed, which aim to respond the external disturbances from the environmental changes or human intervention in the increasingly human-robot scenarios. To effectively evaluate the exploration, we summarize the anomalies in a robot system include only two catalogues: accidental anomalies and persistent anomalies. In particular, we first diagnose the anomaly as accidental one at the beginning such that the reverse execution is called. If and only if robot reverse many times (not less than twice) and still couldn't avoid or eliminate the anomaly, the human interaction is called. Our proposed system would synchronously record the multimodal sensory signals during the process of human-assisted demonstration. That is, a new movement primitive is learned once an exploring demonstration acquired. Then, we heuristically generate a set of synthetic demonstrations for augmenting the learning by appending a multivariate Gaussian noise distribution with mean equal to zeros and covariance equal to ones. Such that the corresponding introspective capabilities are learned and updated when another human demonstration is acquired. Consequently, incrementally learning the introspective movement primitives with few human corrective demonstrations when an unseen anomaly occurs. It is essential that, although there are only two different exploring strategies when anomaly occurs, numerous exploring behaviors can be generated according to different anomaly types and movement behaviors under various circumstances.