Navigation policies are commonly learned on idealized cylinder agents in simulation, without modelling complex dynamics, like contact dynamics, arising from the interaction between the robot and the environment. Such policies perform poorly when deployed on complex and dynamic robots, such as legged robots. In this work, we learn hierarchical navigation policies that account for the low-level dynamics of legged robots, such as maximum speed, slipping, and achieve good performance at navigating cluttered indoor environments. Once such a policy is learned on one legged robot, it does not directly generalize to a different robot due to dynamical differences, which increases the cost of learning such a policy on new robots. To overcome this challenge, we learn dynamics-aware navigation policies across multiple robots with robot-specific embeddings, which enable generalization to new unseen robots. We train our policies across three legged robots - 2 quadrupeds (A1, AlienGo) and a hexapod (Daisy). At test time, we study the performance of our learned policy on two new legged robots (Laikago, 4-legged Daisy) and show that our learned policy can sample-efficiently generalize to previously unseen robots.

中文翻译：

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通过学习的机器人嵌入知识学习有腿机器人的导航技巧

导航策略通常是在仿真中通过理想化的圆柱体代理学习的，而无需对由于机器人与环境之间的相互作用而产生的复杂动力学（如接触动力学）建模。当将这些策略部署在有腿的机器人等复杂且动态的机器人上时，这些策略的效果会很差。在这项工作中，我们学习了分层导航策略，这些策略解决了有腿机器人的低级动态问题，例如最大速度，滑移，并在混乱的室内环境中获得了良好的性能。一旦在一个腿式机器人上学习到这样的策略，由于动态差异，它就不会直接推广到其他机器人，这增加了在新机器人上学习这样的策略的成本。为了克服这一挑战，我们学习了跨多个具有特定于机器人的嵌入的机器人的动态感知导航策略，可以推广到新的看不见的机器人。我们在三足机器人上训练我们的政策-两足机器人（A1，AlienGo）和六足机器人（Daisy）。在测试时，我们研究了我们的学习策略在两个新的有腿机器人（Laikago，4腿雏菊）上的性能，并表明我们的学习策略可以有效地采样到以前未见过的机器人。