让仿人机器人学会踩油门，通过神经网络实时控制动作。
Task-specific Self-body Controller Acquisition by Musculoskeletal Humanoids: Application to Pedal Control in Autonomous Driving
- 用神经网络建模控制输入与任务状态的时序关系。
- 在真实机器人上实现踩油门任务，控制效果有效。
- 适合研究机器人自适应控制与人机协同的学者。
肌骨骼仿人机器人具备类人优势，但其复杂柔性身体建模困难。尽管已开发出关节与肌肉间非线性关系的在线获取方法，仍无法完全匹配实际机器人与其自我体态映射。为实现特定任务，需学习控制输入与任务状态间的直接关系。为此，构建了表示控制输入与任务状态时序关系的神经网络，并应用于实时控制以达成预期任务状态。本研究以加速踏板控制为例，验证了该方法的有效性。
原文摘要 · Abstract (English)
The musculoskeletal humanoid has many benefits that human beings have, but the modeling of its complex flexible body is difficult. Although we have developed an online acquisition method of the nonlinear relationship between joints and muscles, we could not completely match the actual robot and its self-body image. When realizing a certain task, the direct relationship between the control input and task state needs to be learned. So, we construct a neural network representing the time-series relationship between the control input and task state, and realize the intended task state by applying the network to a real-time control. In this research, we conduct accelerator pedal control experiments as one application, and verify the effectiveness of this study.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。