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Found 4 papers, 0 papers with code
ROSO: Improving Robotic Policy Inference via Synthetic Observations
In this paper, we propose the use of generative artificial intelligence (AI) to improve zero-shot performance of a pre-trained policy by altering observations during inference.
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.
