Learning When to Switch: Composing Controllers to Traverse a Sequence of Terrain Artifacts
Brendan Tidd,Akansel Cosgun,Jürgen Leitner,Nicolas Hudson,Brendan Tidd,Akansel Cosgun,Jürgen Leitner,Nicolas Hudson
Legged robots often use separate control policies that are highly engineered for traversing difficult terrain such as stairs, gaps, and steps, where switching between policies is only possible when the robot is in a region that is common to adjacent controllers. Deep Reinforcement Learning (DRL) is a promising alternative to hand-crafted control design, though typically requires the full set of te...
