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.

翻译：本文提出了一种上肢姿态优化方法，旨在提升双人机协同搬运任务中的物理人体工程学性能与力可操作性。现有研究通常侧重于人类安全性或操作效率，而本方法创新性地将两方面结合，以增强在不同条件下的协作能力（例如人类不同的抓握姿态和物体不同形状）。具体而言，通过最小化成本函数来优化简化人体骨骼模型的关节角度，以优先保障安全性和操作能力。为引导人类达到优化姿态，通过变换模块生成机器人末端执行器的参考位姿。针对我们的人形机器人CURI，提出了一种双模型预测阻抗控制器（MPIC），通过规划轨迹重新校准末端执行器位姿。该方法已在人-人协作（HHC）和人-机协作（HRC）中通过不同受试者和物体进行了验证。实验结果表明，通过比较优化前后目标肌肉的激活程度，肌肉状态得到显著改善。