用物理约束提升少数据下人体机器人躯体模型学习效率
PIMBS: Efficient Body Schema Learning for Musculoskeletal Humanoids with Physics-Informed Neural Networks
- 结合物理规律与实测数据，用神经网络学习肌肉张力与关节角关系
- 仅需少量实测数据即可实现高精度躯体模型建模
- 适合数据稀缺的仿人机器人系统建模与控制研究
肌骨骼类人机器人模仿人体肌肉骨骼系统，具备变刚度控制、冗余性和灵活性等优势。然而其结构复杂，肌肉路径常与几何模型偏差显著。现有研究多依赖真实机器人采集的数据来学习躯体模型，即关节角度、肌肉张力与肌肉长度之间的关系，但数据采集耗时费力，数据量少时学习困难。为此，本文提出将物理信息神经网络（PINNs）应用于肌骨骼类人机器人的躯体模型学习，利用实际机器人数据与基于正确关节结构假设下的扭矩-肌肉张力物理规律，实现小样本下的高精度建模。该方法在仿真与真实机器人上均验证有效，显著提升了学习效率。
原文摘要 · Abstract (English)
Musculoskeletal humanoids are robots that closely mimic the human musculoskeletal system, offering various advantages such as variable stiffness control, redundancy, and flexibility. However, their body structure is complex, and muscle paths often significantly deviate from geometric models. To address this, numerous studies have been conducted to learn body schema, particularly the relationships among joint angles, muscle tension, and muscle length. These studies typically rely solely on data collected from the actual robot, but this data collection process is labor-intensive, and learning becomes difficult when the amount of data is limited. Therefore, in this study, we propose a method that applies the concept of Physics-Informed Neural Networks (PINNs) to the learning of body schema in musculoskeletal humanoids, enabling high-accuracy learning even with a small amount of data. By utilizing not only data obtained from the actual robot but also the physical laws governing the relationship between torque and muscle tension under the assumption of correct joint structure, more efficient learning becomes possible. We apply the proposed method to both simulation and an actual musculoskeletal humanoid and discuss its effectiveness and characteristics.
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