From human action understanding to robot action execution: how the physical properties of handled objects modulate non-verbal cues

Abstract

Humans manage to communicate action intentions in a non-verbal way, through body posture and movement. We start from this observation to investigate how a robot can decode a human's non-verbal cues during the manipulation of an object, with specific physical properties, to learn the adequate level of “carefulness” to use when handling that object. We construct dynamical models of the human behaviour using a human-to-human handover dataset consisting of 3 different cups with different levels of fillings. We then included these models into the design of an online classifier that identifies the type of action, based on the human wrist movement. We close the loop from action understanding to robot action execution with an adaptive and robust controller based on the learned classifier, and evaluate the entire pipeline on a collaborative task with a 7-DOF manipulator. Our results show that it is possible to correctly understand the “carefulness” behaviour of humans during object manipulation, even in the pick and place scenario, that was not part of the training set.