Learning to Assist Different Wearers in Multitasks: Efficient and Individualized Human-in-The-Loop Adaptation Framework for Lower-Limb Exoskeleton

Yu Chen1; Shu Miao1; Gong Chen2; Jing Ye2; Chenglong Fu3; Bin Liang1; Shiji Song1; Xiang Li1

2024

10.1109/TRO.2024.3468768

IEEE Transactions on Robotics   影响因子和分区

1941-0468

One of the typical purposes of using lower-limb exoskeleton robots is to provide assistance to the wearer by supporting their weight and augmenting their physical capabilities according to a given task and human motion intentions. The generalizability of robots across different wearers in multiple tasks is important to ensure that the robot can provide correct and effective assistance in actual implementation. However, most lower-limb exoskeleton robots exhibit only limited generalizability. Therefore, this paper proposes a human-in-the-loop learning and adaptation framework for exoskeleton robots to improve their performance in various tasks and for different wearers. To suit different wearers, an individualized walking trajectory is generated online using dynamic movement primitives and Bayes optimization. To accommodate various tasks, a task translator is constructed using a neural network to generalize a trajectory to more complex scenarios. These generalization techniques are integrated into a unified variable impedance model, which regulates the exoskeleton to provide assistance while ensuring safety. In addition, an anomaly detection network is developed to quantitatively evaluate the wearer's comfort, which is considered in the trajectory learning procedure and contributes to the relaxation of conflicts in impedance control. The proposed framework is easy to implement, because it requires proprioceptive sensors only to perform and deploy data-efficient learning schemes. This makes the exoskeleton practical for deployment in complex scenarios, accommodating different walking patterns, habits, tasks, and conflicts. Experiments and comparative studies on a lower-limb exoskeleton robot are performed to demonstrate the effectiveness of the proposed framework.

其他

1.Department of Automation, Tsinghua University, China
2.Shenzhen MileBot Robotics Co., Ltd, China
3.Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China

Yu Chen,Shu Miao,Gong Chen,et al. Learning to Assist Different Wearers in Multitasks: Efficient and Individualized Human-in-The-Loop Adaptation Framework for Lower-Limb Exoskeleton[J]. IEEE Transactions on Robotics,2024,PP(99).

Yu Chen.,Shu Miao.,Gong Chen.,Jing Ye.,Chenglong Fu.,...&Xiang Li.(2024).Learning to Assist Different Wearers in Multitasks: Efficient and Individualized Human-in-The-Loop Adaptation Framework for Lower-Limb Exoskeleton.IEEE Transactions on Robotics,PP(99).

Yu Chen,et al."Learning to Assist Different Wearers in Multitasks: Efficient and Individualized Human-in-The-Loop Adaptation Framework for Lower-Limb Exoskeleton".IEEE Transactions on Robotics PP.99(2024).