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David Mark Bossens

2 accepted papers

2025

Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration

ICLR 2025poster

Imitation learning is a central problem in reinforcement learning where the goal is to learn a policy that mimics the expert's behavior. In practice, it is often challenging to learn the expert policy from a limited number of demonstrations accurately due to the complexity of the state space. Moreov…

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2025

Diversifying Robot Locomotion Behaviors with Extrinsic Behavioral Curiosity

ICML 2025poster

Imitation learning (IL) has shown promise in robot locomotion but is often limited to learning a single expert policy, constraining behavior diversity and robustness in unpredictable real-world scenarios. To address this, we introduce Quality Diversity Inverse Reinforcement Learning (QD-IRL), a nove…