Primary Research Goals

Reward Design

We seek to discover learning methods to produce real dynamic behavior using first principles of legged locomotion. Our process entails integrating physics first, low-level objectives into reinforcement learning to generate control policies that achieve efficient, robust locomotion.

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We explore the problem of how to effectively and consistently transfer learned policies from simulation to the real world, without loss of performance or robustness. We seek to identify the factors and best practices to achieve reliable sim to real transfer.

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Hierarchical Design for Learned Systems

We are investigating how best to structure a control hierarchy, mixing learned components with classical methods

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Efficient Learning

We seek to investigate how to properly guide learned policies towards effective states by structuring our action space and exploration, speeding up learning and producing better action output.

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