5 citations · 5 across the 1 of their papers we have counts for
4 papers
Residual Skill Policies: Learning an Adaptable Skill-based Action Space for Reinforcement Learning for Robotics
Krishan Rana, Ming Xu, Brendan Tidd +2
Skill-based reinforcement learning (RL) has emerged as a promising strategy to leverage prior knowledge for accelerated robot learning. Skills are typically extracted from expert d…
Passing Through Narrow Gaps with Deep Reinforcement Learning
Brendan Tidd, Akansel Cosgun, Jurgen Leitner +1
The U.S. Defense Advanced Research Projects Agency (DARPA) Subterranean Challenge requires teams of robots to traverse difficult and diverse underground environments. Traversing sm…
Learning When to Switch: Composing Controllers to Traverse a Sequence of Terrain Artifacts
Brendan Tidd, Nicolas Hudson, Akansel Cosgun +1
Legged robots often use separate control policiesthat are highly engineered for traversing difficult terrain suchas stairs, gaps, and steps, where switching between policies isonly…
Guided Curriculum Learning for Walking Over Complex Terrain
Brendan Tidd, Nicolas Hudson, Akansel Cosgun
Reliable bipedal walking over complex terrain is a challenging problem, using a curriculum can help learning. Curriculum learning is the idea of starting with an achievable version…