通过添加肌肉自适应学习身体模型，提升仿人机器人负载能力
Adaptive Body Schema Learning System Considering Additional Muscles for Musculoskeletal Humanoids
- 模块化设计支持灵活加装肌肉，软件可从少量运动数据中学习新肌肉影响
- 在高负载任务中，新增肌肉使肌腱张力降低47%
- 适合研究肌肉化机器人控制与自适应学习的学者
肌骨仿人机器人的一大优势是能根据需求灵活调整肌肉布局并增加肌肉数量。本文提出一套完整的肌骨仿人机器人增肌系统，包含硬件模块化身体设计和软件自适应身体模型学习方法，可在少量运动数据下学习新增肌肉带来的身体模型变化。我们在一个1-自由度绳索驱动机器人仿真模型及肌骨仿人机器人Musashi的臂部上验证了该方法。结果表明，在高负载任务中，通过增加肌肉可使肌腱张力降低47%，显著改善负载表现。
原文摘要 · Abstract (English)
One of the important advantages of musculoskeletal humanoids is that the muscle arrangement can be easily changed and the number of muscles can be increased according to the situation. In this study, we describe an overall system of muscle addition for musculoskeletal humanoids and the adaptive body schema learning while taking into account the additional muscles. For hardware, we describe a modular body design that can be fitted with additional muscles, and for software, we describe a method that can learn the changes in body schema associated with additional muscles from a small amount of motion data. We apply our method to a simple 1-DOF tendon-driven robot simulation and the arm of the musculoskeletal humanoid Musashi, and show the effectiveness of muscle tension relaxation by adding muscles for a high-load task.
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