在线学习肌肉长度危险概率，预防仿人机器人运动风险
Online Learning of Danger Avoidance for Complex Structures of Musculoskeletal Humanoids and Its Applications
- 基于肌肉长度实时计算危险概率，动态预警
- 在Musashi机器人上验证，有效降低碰撞与高肌力风险
- 适合需安全控制的复杂仿人机器人研发人员
肌腱驱动仿人机器人的复杂结构导致建模困难，体间干涉和高内部肌力不可避免。尽管已有多种安全机制，但不仅需事后应对危险，更应预防其发生。本研究提出一种在线学习方法，通过网络实时输出与肌肉长度对应的危险概率，使机器人逐步主动规避潜在风险。该方法应用于肌腱驱动仿人机器人Musashi，实验验证了其有效性。
原文摘要 · Abstract (English)
The complex structure of musculoskeletal humanoids makes it difficult to model them, and the inter-body interference and high internal muscle force are unavoidable. Although various safety mechanisms have been developed to solve this problem, it is important not only to deal with the dangers when they occur but also to prevent them from happening. In this study, we propose a method to learn a network outputting danger probability corresponding to the muscle length online so that the robot can gradually prevent dangers from occurring. Applications of this network for control are also described. The method is applied to the musculoskeletal humanoid, Musashi, and its effectiveness is verified.
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