Integrating Ergonomics and Manipulability for Upper Limb Postural Optimization in Bimanual Human-Robot Collaboration

TL;DR

This paper presents a novel method for optimizing upper limb posture in bimanual human-robot collaboration, improving ergonomics and manipulability by integrating safety and efficiency considerations.

Contribution

It introduces a joint angle optimization approach combined with a transformation-guided reference pose generation and a bimanual impedance controller for enhanced collaboration.

Findings

Significant reduction in muscle activation after optimization

Improved safety and manipulability during collaboration

Validated across multiple subjects and objects

Abstract

This paper introduces an upper limb postural optimization method for enhancing physical ergonomics and force manipulability during bimanual human-robot co-carrying tasks. Existing research typically emphasizes human safety or manipulative efficiency, whereas our proposed method uniquely integrates both aspects to strengthen collaboration across diverse conditions (e.g., different grasping postures of humans, and different shapes of objects). Specifically, the joint angles of a simplified human skeleton model are optimized by minimizing the cost function to prioritize safety and manipulative capability. To guide humans towards the optimized posture, the reference end-effector poses of the robot are generated through a transformation module. A bimanual model predictive impedance controller (MPIC) is proposed for our human-like robot, CURI, to recalibrate the end effector poses through planned trajectories. The proposed method has been validated through various subjects and objects during human-human collaboration (HHC) and human-robot collaboration (HRC). The experimental results demonstrate significant improvement in muscle conditions by comparing the activation of target muscles before and after optimization.