用模仿学习定制虚拟外骨骼，帮手部肌力不足者完成抓握任务。
Imitation Learning for Adaptive Control of a Virtual Soft Exoglove
- 通过演示学习训练抓握模型，再微调实现特定物体操作。
- 模拟肌力下降后，虚拟外骨骼补偿使操作能力保持在原水平的90.5%。
- 适合康复机器人个性化控制研究者参考。
可穿戴机器人在手部运动功能障碍患者的康复训练中广泛应用，但患者肌肉损失的独特性常被忽视。我们利用强化学习与生物逼真的肌肉骨骼仿真模型，提出一种可定制的可穿戴机器人控制器，能够针对特定肌肉缺陷进行补偿，支持手-物体操作任务。通过同一受试者执行抓握动作的视频数据，采用示范学习训练出操作模型，并进一步微调以完成特定物体交互任务。在肌肉骨骼模型中减弱肌力以模拟神经损伤，随后由虚拟可穿戴外骨骼提供驱动补偿。结果显示，集成虚拟可穿戴外骨骼能有效共享助力，支持肌力减弱的手部操作。所学外骨骼控制器实现了原始操作能力的平均90.5%。
原文摘要 · Abstract (English)
The use of wearable robots has been widely adopted in rehabilitation training for patients with hand motor impairments. However, the uniqueness of patients' muscle loss is often overlooked. Leveraging reinforcement learning and a biologically accurate musculoskeletal model in simulation, we propose a customized wearable robotic controller that is able to address specific muscle deficits and to provide compensation for hand-object manipulation tasks. Video data of a same subject performing human grasping tasks is used to train a manipulation model through learning from demonstration. This manipulation model is subsequently fine-tuned to perform object-specific interaction tasks. The muscle forces in the musculoskeletal manipulation model are then weakened to simulate neurological motor impairments, which are later compensated by the actuation of a virtual wearable robotics glove. Results shows that integrating the virtual wearable robotic glove provides shared assistance to support the hand manipulator with weakened muscle forces. The learned exoglove controller achieved an average of 90.5\% of the original manipulation proficiency.
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