论文标题
使用深钢筋学习对四足动物的人体运动控制
Human Motion Control of Quadrupedal Robots using Deep Reinforcement Learning
论文作者
论文摘要
一个基于运动的控制接口承诺通过将用户直觉与机器人的电动机功能相结合，可以在危险环境中进行灵活的机器人操作。但是，设计针对非人类机器人的运动界面，例如四足动物或六脚架，因为不同的动态和控制策略控制了它们的运动，因此并不简单。我们提出了一个新型的运动控制系统，该系统允许人类用户在四倍体机器人上无缝操作各种电机任务。我们首先使用监督的学习和后处理技术将被捕获的人类运动重新定为相应的机器人运动。然后，我们将运动模仿学习与课程学习一起使用，以制定可以跟踪给定重新定位参考的控制策略。通过培训一组专家，我们进一步提高了运动重新定位和运动模仿的性能。正如我们所证明的那样，用户可以使用我们的系统执行各种电动机任务，包括站立，坐着，倾斜，操纵，步行和转动，在模拟和真实的四倍体上。我们还进行了一组研究，以分析每个组件诱导的绩效增益。
A motion-based control interface promises flexible robot operations in dangerous environments by combining user intuitions with the robot's motor capabilities. However, designing a motion interface for non-humanoid robots, such as quadrupeds or hexapods, is not straightforward because different dynamics and control strategies govern their movements. We propose a novel motion control system that allows a human user to operate various motor tasks seamlessly on a quadrupedal robot. We first retarget the captured human motion into the corresponding robot motion with proper semantics using supervised learning and post-processing techniques. Then we apply the motion imitation learning with curriculum learning to develop a control policy that can track the given retargeted reference. We further improve the performance of both motion retargeting and motion imitation by training a set of experts. As we demonstrate, a user can execute various motor tasks using our system, including standing, sitting, tilting, manipulating, walking, and turning, on simulated and real quadrupeds. We also conduct a set of studies to analyze the performance gain induced by each component.
