Homotopy switching model for dyad haptic interaction in physical collaborative tasks

Abstract

The main result of this paper is a new model based on homotopy switching between intrinsically distinct controllers, to encompass most behaviors encountered in dyadic haptic collaborative tasks through an intermediate object. The basic idea is to switch continuously between two distinct extreme behaviors (leader and follower) for each individual, which creates an implicit bilateral coupling within the dyad. The physical collaborative interaction is then described only with two distinct homotopy time-functions that vary independently. These functions can likely describe the signature of a collaborative task. A virtual reality haptic set-up is used to assess the proposed theory.

Adaptive optimal control for coordination in physical human-robot interaction

“…Substituting (15) and (16) into (18), we obtain ∂z * ∂r2 , and thus ∂e f ∂r2 in (16). After R 2 is updated and P obtained, the actual control of the robot is obtained as follows…”

“…This method considers only the extreme cases where the robot is either a leader or a follower. In [16], a homotopy switching model is proposed to switch leader/follower roles in dyadic haptic collaborative tasks through an intermediate object.…”

Shared control of human and robot by approximate dynamic programming

“…In [12], [13], the terminal constraint receding horizon control is developed for shared control of human and robot, such that the common goal can be achieved subsequently. A homotopy switching model is developed in [14] for dyad haptic interaction in physical collaborative tasks. In [15], a generic framework is proposed to analyze interactive motor behaviors, where system dynamics are required to be known.…”

Rapid Prototyping of Planning, Learning and Control in Physical Human-Robot Interaction

“…So far, most of the existing works investigate individual algorithms for planning [29,24], learning/prediction [19,4,10,18], and Martin Lawitzky, José Ramón Medina Hernández, and Sandra Hirche Technische Universität München, Inst. of Automatic Control Eng., 80290 Munich, Germany, e-mail: {ml, medina, hirche}@tum.de control [27,16,15,15,34,6] for physical human-robot interaction (pHRI) in a separate way. Only very few works consider the combination of learning and control in pHRI [11,20].…”