论文标题
模仿和重新利用：从人类和动物行为中学习可重复使用的机器人运动技能
Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors
论文作者
论文摘要
我们研究了对人类运动的先验知识的使用，以学习真正的腿机器人可重复使用的运动技能。我们的方法是基于以前模仿人类或狗运动捕获（MOCAP）数据以学习运动技能模块的工作。一旦了解到，该技能模块就可以重复用于复杂的下游任务。重要的是，由于MOCAP数据提出的先前，我们的方法不需要广泛的奖励工程即可在重复使用时产生明智而自然的行为。这使得创建适合在真实机器人部署的良好注册，以任务为导向的控制器变得容易。我们演示了如何将我们的技能模块用于模仿，并为Anymal四足动物和OP3类人动物提供可控制的步行和球运球政策。然后，这些策略将通过零拍模拟传输部署在硬件上。随附的视频可在https://bit.ly/robot-npmp上找到。
We investigate the use of prior knowledge of human and animal movement to learn reusable locomotion skills for real legged robots. Our approach builds upon previous work on imitating human or dog Motion Capture (MoCap) data to learn a movement skill module. Once learned, this skill module can be reused for complex downstream tasks. Importantly, due to the prior imposed by the MoCap data, our approach does not require extensive reward engineering to produce sensible and natural looking behavior at the time of reuse. This makes it easy to create well-regularized, task-oriented controllers that are suitable for deployment on real robots. We demonstrate how our skill module can be used for imitation, and train controllable walking and ball dribbling policies for both the ANYmal quadruped and OP3 humanoid. These policies are then deployed on hardware via zero-shot simulation-to-reality transfer. Accompanying videos are available at https://bit.ly/robot-npmp.
