论文标题
通过学习的机器人嵌入的腿部机器人学习导航技能
Learning Navigation Skills for Legged Robots with Learned Robot Embeddings
论文作者
论文摘要
最近的工作显示了在模拟中为理想化的圆柱体学习导航政策的结果，并将其转移到真实的车轮机器人中。由于其复杂的动态，以及气缸代理和腿部系统之间的巨大动态差异，在腿部机器人上部署此类导航政策可能会具有挑战性。在这项工作中，我们学习了分层导航策略，这些导航策略说明了腿部机器人的低水平动态，例如最大速度，滑动，联系人，并学会了成功地导航杂乱无章的室内环境。为了使模拟中学到的策略转移到新的腿机器人和硬件中，我们学习了具有特定于机器人特定嵌入的多个机器人的动态感知导航策略。学习的嵌入在新机器人上进行了优化，而其余的策略则保持固定，从而可以快速适应。我们在模拟的三个腿部机器人中训练政策-2个四足动物（A1，Aliengo）和一个Hexapod（Daisy）。在测试时，我们研究了对两个新的模拟机器人（Laikago，4尾雏菊）和一个现实世界中的四足机器人（A1）的学习政策的表现。我们的实验表明，我们学到的政策可以有效地概括为以前看不见的机器人，并启用SIM卡转移到腿部机器人的导航策略。
Recent work has shown results on learning navigation policies for idealized cylinder agents in simulation and transferring them to real wheeled robots. Deploying such navigation policies on legged robots can be challenging due to their complex dynamics, and the large dynamical difference between cylinder agents and legged systems. In this work, we learn hierarchical navigation policies that account for the low-level dynamics of legged robots, such as maximum speed, slipping, contacts, and learn to successfully navigate cluttered indoor environments. To enable transfer of policies learned in simulation to new legged robots and hardware, we learn dynamics-aware navigation policies across multiple robots with robot-specific embeddings. The learned embedding is optimized on new robots, while the rest of the policy is kept fixed, allowing for quick adaptation. We train our policies across three legged robots in simulation - 2 quadrupeds (A1, AlienGo) and a hexapod (Daisy). At test time, we study the performance of our learned policy on two new legged robots in simulation (Laikago, 4-legged Daisy), and one real-world quadrupedal robot (A1). Our experiments show that our learned policy can sample-efficiently generalize to previously unseen robots, and enable sim-to-real transfer of navigation policies for legged robots.
