Adaptive torque prediction in dynamic exoskeleton scenarios requires expensive motion capture systems, which are infeasible in complex outdoor environments. Trajectory prediction has emerged as one of the effective approaches to address such an issue. However, the core challenges of exoskeleton trajectory prediction are twofold: establishing the mapping from multi-modal features to trajectory information; constructing the mapping from trajectory to torque. For the former, most existing methods perform only single-step prediction and neglect inter-subject trajectory variability, thereby limiting the trajectory optimization space and prediction generalization. To address this, this paper proposes a fast flow matching method that enables accurate trajectory prediction and better generalization for real-time performance, where trajectory generation errors and encoded observations are used to guide the training direction. For the second challenge, due to the high dynamics of the human-robot system and the strong coupling between perception and control, simple control methods struggle to achieve efficient assistance based on the predicted trajectory. This paper utilizes model predictive control and designs a novel optimization objective to optimize torque, ensuring the exoskeleton achieves comfortable and robust assistance. By integrating the above two components, the unified policy, denoted as ExoTraj, is developed to enable adaptive assistance in complex outdoor scenarios without high data acquisition cost. Experimental results show that compared to traditional methods, ExoTraj reduces cross-subject prediction error by 14.0% during the online phase and maintains robustness against external noise. Relative to the zero torque condition, ExoTraj decreases metabolic rate by 11.5-24.4%, heart rate by 1.7-19.5%, and peak muscle activation levels by 10.9-41.3%, respectively.

翻译：动态外骨骼场景中的自适应力矩预测需要昂贵的动作捕捉系统，这在复杂户外环境中难以实现。轨迹预测已成为解决该问题的有效途径之一。然而，外骨骼轨迹预测面临双重核心挑战：建立多模态特征到轨迹信息的映射，以及构建轨迹到力矩的映射。针对前者，现有方法大多仅执行单步预测，且忽略个体间轨迹变异，从而限制了轨迹优化空间与预测泛化能力。为解决此问题，本文提出一种快速流匹配方法，能够实现高精度轨迹预测与良好泛化性能的实时运行，其中轨迹生成误差与编码观测值被用于引导训练方向。针对第二个挑战，由于人-机器人系统的高动态性以及感知与控制间的强耦合，简单控制方法难以基于预测轨迹实现高效辅助。本文采用模型预测控制，并设计一种新颖的优化目标以优化力矩，确保外骨骼实现舒适且鲁棒的辅助。通过整合上述两个模块，本文开发了统一策略ExoTraj，使其能够在无需高数据采集成本的情况下，在复杂户外场景中实现自适应辅助。实验结果表明，与传统方法相比，ExoTraj在在线阶段将跨被试预测误差降低14.0%，并保持对外部噪声的鲁棒性。相对于零力矩条件，ExoTraj分别将代谢率降低11.5-24.4%、心率降低1.7-19.5%、峰值肌肉激活水平降低10.9-41.3%。