Feature-Prescribed Iterative Learning Control of Waggle Dance Movement for Social Motor Coordination in Joint Actions

TL;DR

This paper introduces a novel two-dimensional mirror game paradigm using waggle dance movements to study social motor coordination, employing an iterative learning control algorithm for virtual player coordination with human participants.

Contribution

It develops a new 2D experimental setup with a customized virtual player and proposes an iterative learning control algorithm integrating kinematic features for improved coordination.

Findings

The control algorithm effectively matches experimental data.

Convergence of the control method is theoretically guaranteed.

The approach outperforms existing control strategies in coordination tasks.

Abstract

Extensive experiments suggest that motor coordination among human participants may contribute to social affinity and emotional attachment, which has great potential in the clinical treatment of social disorders or schizophrenia. Mirror game provides an effective experimental paradigm for studying social motor coordination. Nevertheless, the lack of movement richness prevents the emergence of high-level coordination in the existing one-dimensional experiments. To tackle this problem, this work develops a two-dimensional experimental paradigm of mirror game by playing waggle dance between two participants. In particular, an online control architecture of customized virtual player is created to coordinate with human player. Therein, an iterative learning control algorithm is proposed by integrating position tracking and behavior imitation with prescribed kinematic feature. Moreover, convergence analysis of control algorithm is conducted to guarantee the online performance of virtual player. Finally, the proposed control strategy is validated by matching experimental data and compared with other control methods using a set of performance indexes.