UC Berkeley | 让四足机器人掌握更多交互技能

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作者：X Cheng, A Kumar, D Pathak
[CMU & UC Berkeley]

总结:
展示了一种新框架，使四足机器人可以通过运动和操纵技能，以更灵活的方式进行物体交互，攀爬墙壁和完成高级任务。

要点:

1. 通过训练，四足机器人不仅可以行走，还可以使用前腿爬墙、按按钮、在现实世界中与物体互动；
2. 将低层次技能组合成长程复杂任务的框架，从而赋予机器人更多的人类环境访问能力；
3. 通过模拟和现实世界的严格评估，证明了该方法的灵活性和鲁棒性；
4. 当前存在的限制是高层次的决策和低层次的命令跟踪仍然解耦，但未来将会实现完全的端到端架构。

https://arxiv.org/abs/2303.11330

Locomotion has seen dramatic progress for walking or running across challenging terrains. However, robotic quadrupeds are still far behind their biological counterparts, such as dogs, which display a variety of agile skills and can use the legs beyond locomotion to perform several basic manipulation tasks like interacting with objects and climbing. In this paper, we take a step towards bridging this gap by training quadruped robots not only to walk but also to use the front legs to climb walls, press buttons, and perform object interaction in the real world. To handle this challenging optimization, we decouple the skill learning broadly into locomotion, which involves anything that involves movement whether via walking or climbing a wall, and manipulation, which involves using one leg to interact while balancing on the other three legs. These skills are trained in simulation using curriculum and transferred to the real world using our proposed sim2real variant that builds upon recent locomotion success. Finally, we combine these skills into a robust long-term plan by learning a behavior tree that encodes a high-level task hierarchy from one clean expert demonstration. We evaluate our method in both simulation and real-world showing successful executions of both short as well as long-range tasks and how robustness helps confront external perturbations.