Human Intention Detection as a Multiclass Classification Problem: Application in Physical Human–Robot Interaction While Walking

Abstract

In many physical human–robot interaction scenarios, for successful completion of the tasks, robots should be able to recognize the human partner's intention. One of such scenarios that is studied in this letter is the collaborative task of carrying an object by a human–humanoid pair in which the humanoid should be able to interpret specific human partner's intentions (e.g., start/stop-walking, accelerate, etc.) only through haptic feedback. To address this problem, we first performed human-human experiments and obtained a multiclass classifier (with more than 90% of accuracy) for human intention detection using, as features, arm position relative to the shoulder and interaction forces. The results of the multiclass classification, without any modifications, have been used to develop an interlimb coordinator that was integrated in a modular control architecture into human-robot experiments. The interlimb coordinator receives the sensory data of the upper-body and sends appropriate commands (including start/stop-walking, accelerate, and decelerate commands) to the lower body controller, which is responsible for achieving a stable walking gait. This modular control approach is successfully tested in the human–humanoid experiments with the COMAN robot.