让机器人能抓握并移动人肢体，提升助残护理效率
Manip4Care: Robotic Manipulation of Human Limbs for Solving Assistive Tasks
- 用物理仿真模拟抓握与移动人体肢体，考虑生物力学约束
- 通过力闭合采样和MPPI算法生成避障且安全的运动轨迹
- 适用于不同年龄组，实测在卧姿坐姿下均有效
使机器人能够抓握并重新定位人类肢体，可显著增强其为严重行动障碍者提供辅助护理的能力，尤其在机器人辅助床上沐浴和穿衣等任务中。然而，现有助残机器人方案通常假设人体保持静止或准静态，限制了实际效果。为此，我们提出Manip4Care，一个模块化仿真流程，使机器人操纵器能有效抓握和重新定位人体肢体。该方法采用内置抓握与重定位技术的物理模拟器，同时考虑生物力学与碰撞规避约束。抓握方法采用对称采样结合力闭合策略，重定位系统则利用模型预测路径积分（MPPI）与基于向量场的控制方法，在满足碰撞规避与生物力学限制条件下生成运动轨迹。我们在仰卧与坐姿多种肢体操作任务中评估该方法，并对比不同年龄组在肩关节活动范围差异下的表现。此外，我们使用真实人体模型验证了该方法在肢体操作中的可行性，并进一步展示了其在床浴任务中的有效性。
原文摘要 · Abstract (English)
Enabling robots to grasp and reposition human limbs can significantly enhance their ability to provide assistive care to individuals with severe mobility impairments, particularly in tasks such as robot-assisted bed bathing and dressing. However, existing assistive robotics solutions often assume that the human remains static or quasi-static, limiting their effectiveness. To address this issue, we present Manip4Care, a modular simulation pipeline that enables robotic manipulators to grasp and reposition human limbs effectively. Our approach features a physics simulator equipped with built-in techniques for grasping and repositioning while considering biomechanical and collision avoidance constraints. Our grasping method employs antipodal sampling with force closure to grasp limbs, and our repositioning system utilizes the Model Predictive Path Integral (MPPI) and vector-field-based control method to generate motion trajectories under collision avoidance and biomechanical constraints. We evaluate this approach across various limb manipulation tasks in both supine and sitting positions and compare outcomes for different age groups with differing shoulder joint limits. Additionally, we demonstrate our approach for limb manipulation using a real-world mannequin and further showcase its effectiveness in bed bathing tasks.
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