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

TL;DR

The paper tackles the challenge of safe and efficient bimanual human-robot collaboration by integrating physical ergonomics with force manipulability in an upper-limb postural optimization framework. It couples a simplified 4-DOF human upper-limb model with a manipulability-aware cost function, a pose-generation transformation module, and a dual-arm MPIC controller for the cobot CURI. The approach yields optimized human postures that reduce muscle activation while maintaining or enhancing load-manipulation capabilities, validated through human-human and human-robot experiments across multiple objects. The results demonstrate significant ergonomic and muscular benefits, suggesting strong potential for real-world HRC deployments in industrial and domestic settings.

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.

Figures (7)

• Figure 1: Illustration of upper limb postural optimization based on ergonomics and manipulability in human-robot co-carrying tasks.
• Figure 2: Illustration of the proposed collaborative framework for Human-Robot Co-Carrying. Human Skeleton Model: A simplified human upper limb kinematic model is given to calculate specific joint angles. Postural Optimization: An optimization method is proposed to enhance human posture by integrating ergonomics and manipulability. Pose Generation: The robot desired end effector poses are generated based on a transformation module. Controller: An MPIC is applied for our human-like robot, CURI, to execute the planned trajectories.
• Figure 3: (I) Illustration of the simplified human right upper limb skeleton model and force manipulability ellipsoid, (II) examples of REBA physical ergonomic assessment with human upper limb joints (shoulder flexion/extension and elbow flexion/extension, respectively).
• Figure 4: Diagram of pose generation to calculate the reference end effector poses of the cobot based on human initial and optimized wrist positions, object initial pose, and cobot initial end effector poses.
• Figure 5: Experimental validation by human-human co-carrying of diverse objects. The experimental setup includes: (a) data collection of initial posture with the motion capture system, (b) postural optimization to generate a new posture, (c) real-world reappearance by the subject to mimic the optimized posture, (d) muscle activation analysis of target muscles before and after optimization within 5 $s$. Five demonstrations are conducted to co-carry the side table (A-C), wooden screen (D), and logistics box (E).