🤖 AI Summary
In dual-arm human–robot collaborative manipulation, balancing ergonomic safety with force manipulation capability remains challenging. To address this, we propose a synergistic upper-limb pose optimization method integrating physical ergonomics and force manipulability. We formulate, for the first time, a coupled cost function based on joint-angle dependencies that jointly minimizes muscle activation load and maximizes force manipulability (i.e., minimizes the reciprocal of the force ellipsoid volume), guided by robot-end reference poses to steer the human into optimal collaborative postures. The method leverages a simplified skeletal model, analytical transformation modules, and a bimanual Model Predictive Impedance Controller (MPIC), enabling support for multiple grasp configurations and irregular object geometries. Experimental evaluations across multiple subjects and objects demonstrate a 23.6% reduction in target muscle activation, a 31.4% decrease in peak muscle load, and significant improvements in trajectory smoothness and force-tracking accuracy.
📝 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.