Two-Layer Attention Optimization for Bimanual Coordination

TL;DR

This paper introduces a two-layer attention-based control system for bimanual tasks, optimizing attention distribution and coordination to improve task performance and minimize effort.

Contribution

It presents a novel two-layer controller that explicitly models attention distribution and hand coordination for bimanual tasks.

Findings

The attention layer improves coordination between hands.

The controller reduces attention and control effort during task execution.

Successful application to a pong game simulation.

Abstract

Bimanual tasks performed by human agents present unique optimal control considerations compared to cyberphysical agents. These considerations include minimizing attention, distributing attention across two isolated hands, and coordinating the two hands to reach a broader goal. In this work, we propose a two-layer controller that captures these considerations. The upper layer solves an attention distribution problem, while the two lower layer controllers (one per hand) tracks a trajectory using the solution given by the upper layer. We introduce a formulation of the attention controller where attention is a vector that is bound within a hyperbolic feasible region, which is determined by specifications of the task the lower layer controllers. This two-layer controller is used to optimize a single-player game of pong, where the agent must rally the ball between two paddles for as long as possible. We find that adding an attention layer on top of the lower controllers allows the agent to coordinate the left and right hands, which minimizes attention and control effort over the course of the rallying task.