arXiv 2405.011142025-06-05cs.LGcs.RO

通过多任务前瞻性排练从模拟交互中进行持续学习以实现仿生肢体行为建模

Continual Learning from Simulated Interactions via Multitask Prospective Rehearsal for Bionic Limb Behavior Modeling

ETH Zurich（苏黎世联邦理工学院）；University of Goettingen（戈丁根大学）；Bielefeld University（比勒菲尔德大学）；European Neuroscience Institute, Goettingen (ENI-G)（戈丁根欧洲神经科学研究所）；Qatar Computing Institute（卡塔尔计算研究所）；University Medical Center, Goettingen (UMG)（戈丁根大学医学中心）

Sharmita Dey, Benjamin Paassen, Sarath Ravindran Nair, Sabri Boughorbel, Arndt F. Schilling

备注：: Accepted at Transactions on Machine Learning Research (TMLR) 2025

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AI总结：

本文提出一种基于多任务前瞻性排练的持续学习模型，通过人类运动演示学习下肢协同耦合，以预测仿生假肢缺失肢体的运动行为，并在多种步态任务中优于基线模型。

摘要：

下肢截肢和神经肌肉损伤严重限制了行动能力，因此需要超越传统假肢的进步。虽然电动仿生肢体显示出前景，但其有效性取决于能否在多样环境中复制人类运动的动态协调。在本文中，我们针对仿生假肢控制场景引入了一个人类行为模型。我们的方法利用人类运动演示来学习下肢的协同耦合，从而能够在行走、爬坡和上下楼梯等任务中预测缺失肢体的运动学行为。我们提出了一种多任务、持续自适应的模型，该模型能够随时间推移预测并优化运动。我们方法的核心是一种称为多任务前瞻性排练的技术，该技术基于先前的预测来预测和合成未来运动，并为后续预测采用纠正机制。我们不断演进的架构在共享主干上合并轻量级、任务特定的模块，确保专用性和可扩展性。我们通过在真实人体步态数据集（包括小腿截肢者）上跨多种运动任务进行实验来验证我们的模型。结果表明，我们的方法始终优于基线模型，尤其是在存在分布偏移、对抗性扰动和噪声的场景中。

英文摘要：

Lower limb amputations and neuromuscular impairments severely restrict mobility, necessitating advancements beyond conventional prosthetics. While motorized bionic limbs show promise, their effectiveness depends on replicating the dynamic coordination of human movement across diverse environments. In this paper, we introduce a model for human behavior in the context of bionic prosthesis control. Our approach leverages human locomotion demonstrations to learn the synergistic coupling of the lower limbs, enabling the prediction of the kinematic behavior of a missing limb during tasks such as walking, climbing inclines, and stairs. We propose a multitasking, continually adaptive model that anticipates and refines movements over time. At the core of our method is a technique called multitask prospective rehearsal, that anticipates and synthesizes future movements based on the previous prediction and employs a corrective mechanism for subsequent predictions. Our evolving architecture merges lightweight, task-specific modules on a shared backbone, ensuring both specificity and scalability. We validate our model through experiments on real-world human gait datasets, including transtibial amputees, across a wide range of locomotion tasks. Results demonstrate that our approach consistently outperforms baseline models, particularly in scenarios with distributional shifts, adversarial perturbations, and noise.

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