A Task-Invariant Learning Framework of Lower-Limb Exoskeletons for Assisting Human Locomotion
A Task-Invariant Learning Framework of Lower-Limb Exoskeletons for Assisting Human Locomotion
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DOI：
10.23919/acc45564.2020.9147915
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发表时间：
2020-07
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影响因子：
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通讯作者：
Ge Lv;Haosen Xing;Jianping Lin;R. Gregg;C. Atkeson
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文献类型：
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作者：
Ge Lv;Haosen Xing;Jianping Lin;R. Gregg;C. Atkeson
外骨骼的运动学控制方法遵循指定的轨迹，这过度约束了对其下肢具有部分或完全意志控制的个体。在我们以前的工作中，我们提出了一个一般的匹配框架，欠驱动能量整形提供任务不变，充满活力的外骨骼援助。虽然所提出的塑形策略证明了步行过程中人体扭矩降低等益处，但仍不清楚这些塑形策略的参数如何与不同的步态益处相关。同时，研究表明，通过在线优化定制辅助可以大大提高每个人的外骨骼性能。基于这一事实，我们将无导数、样本高效的优化算法与我们的能量整形策略相结合，提出了一种用于下肢外骨骼的任务不变学习框架。通过快速在线优化，该框架使外骨骼能够调整成形参数，以最大限度地减少用户和任务中的人体关节扭矩。仿真结果表明，具有最佳参数的塑形策略有效地降低了模拟行走过程中人体关节力矩和估计的代谢成本。此外，使用健全受试者的运动学数据计算的最佳外骨骼扭矩与不同步行步态的真实的人体关节扭矩非常匹配。
Kinematic control approaches for exoskeletons follow specified trajectories, which overly constrain individuals who have partial or full volitional control over their lower limbs. In our prior work, we proposed a general matching framework for underactuated energy shaping to provide task-invariant, energetic exoskeletal assistance. While the proposed shaping strategies demonstrated benefits such as reduced human torques during walking, it remains unclear how the parameters of these shaping strategies are related to different gait benefits. Meanwhile, research indicates that customizing assistance via online optimization can substantially improve exoskeleton’s performance for each individual. Motivated by this fact, we com-bine derivative-free, sample-efficient optimization algorithms with our energy shaping strategies to propose a task-invariant learning framework for lower-limb exoskeletons. Through rapid online optimization, this framework enables exoskeletons to adjust shaping parameters for minimizing human joint torques across users and tasks. Simulation results show that shaping strategies with optimal parameters effectively reduce human joint torques and estimated metabolic cost during simulated walking. In addition, the optimal exoskeleton torques calculated using able-bodied subjects’ kinematic data closely match the real human joint torques for different walking gaits.