发表机构
New Jersey Institute of Technology; Kessler Foundation（新泽西理工学院; 凯斯勒基金会）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出分阶段多智能体训练（SMAT）策略，将其部署于髋部外骨骼并经8名健康成人测试，证实该策略可显著降低代谢率，且在不同步行速度和地形上具备泛化性。
AI 中文摘要
基于学习的控制器可在基于物理的仿真中完成训练后为外骨骼提供助力，但针对人机协同适应的控制器，极少通过全身代谢测量（辅助行走的标准基准）在真实用户上得到验证。协同适应极具挑战性：当设备改变关节动力学时，穿戴者会重组神经肌肉协调，从而产生非平稳学习问题。本文提出分阶段多智能体训练（SMAT），这是一种四阶段课程，逐步训练肌肉骨骼人类智能体和双侧髋部外骨骼智能体，此前已证明其可降低仿真中的髋部肌肉激活并在硬件上提供正向助力。本文对SMAT进行首次生理学验证：将该策略部署在髋部外骨骼上，对8名健康成人进行测试，在无外骨骼、被动、主动三种工况下通过间接量热法测量代谢成本。主动助力相较于被动设备降低了19.7%的净代谢率（p < 0.001）；生物力学分析确认所有受试者髋部机械功率主要为正向（正向功率比为0.98），且该策略可在不同步行速度和地形上泛化。综合来看，这些结果表明，无需针对受试者重新训练的单一仿真训练SMAT策略，可在真实用户上提供显著的代谢益处，同时在超出训练工况时仍保持鲁棒性。
英文摘要
Learning-based controllers can deliver exoskeleton assistance after training entirely in physics-based simulation, yet few controllers that address human-device co-adaptation have been validated on real users by whole-body metabolic measurement, the standard benchmark for assistive walking. Co-adaptation is challenging: as the device alters joint dynamics, the wearer reorganizes neuromuscular coordination, producing a non-stationary learning problem. Staged Multi-Agent Training (SMAT), a four-stage curriculum that progressively trains a musculoskeletal human actor and a bilateral hip exoskeleton actor, was introduced and shown to reduce simulated hip-muscle activation and provide positive assistance on hardware. This article provides the first physiological validation of SMAT. The policy was deployed on a hip exoskeleton and tested with eight healthy adults, with metabolic cost measured by indirect calorimetry across no-exoskeleton, passive, and active conditions. Active assistance lowered net metabolic rate by 19.7% relative to the passive device (p < 0.001). Biomechanical analysis confirmed predominantly positive hip mechanical power across all subjects (positive-power ratio 0.98), and the policy generalized across walking speeds and terrains. Together, these results show that a single simulation-trained SMAT policy, deployed without subject-specific retraining, delivers a significant metabolic benefit on real users while remaining robust beyond the conditions it was trained on.
Comments14 pages, 9 figures. Extended version of a paper to appear at IROS 2026 (arXiv:2603.07618)
