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Found 5 papers, 1 papers with code
Residual Skill Policies: Learning an Adaptable Skill-based Action Space for Reinforcement Learning for Robotics
Furthermore, the downstream RL agent is limited to learning structurally similar tasks to those used to construct the skill space.
Passing Through Narrow Gaps with Deep Reinforcement Learning
While we show the feasibility of our approach in simulation, the difference in performance between simulated and real world scenarios highlight the difficulty of direct sim-to-real transfer for deep reinforcement learning policies.
Learning Setup Policies: Reliable Transition Between Locomotion Behaviours
Dynamic platforms that operate over many unique terrain conditions typically require many behaviours.
Learning When to Switch: Composing Controllers to Traverse a Sequence of Terrain Artifacts
Legged robots often use separate control policiesthat are highly engineered for traversing difficult terrain suchas stairs, gaps, and steps, where switching between policies isonly possible when the robot is in a region that is commonto adjacent controllers.
Guided Curriculum Learning for Walking Over Complex Terrain
Reliable bipedal walking over complex terrain is a challenging problem, using a curriculum can help learning.
