Research and deployment
Selected work
Work on imitation and reward learning, curriculum learning and environment design, and robust robot learning under distribution shift.
Reinforcement Learning for Robust Legged Locomotion
Training and robustness evaluation for locomotion policies under terrain, sensing, and dynamics shift, using identified failure modes to define targeted evaluation and curriculum scenarios.
Physical deployment, robustness evaluation and failure analysis, and large-scale GPU simulation.
Reward Learning under Distribution Shift
Uses variation across demonstrators as structural information to discourage reward features that fail under dynamics and nuisance shifts.
Evaluated reward transfer across five MuJoCo tasks under dynamics interventions.
Distribution-Matching Imitation Learning
Develops distribution-matching formulations of imitation learning, including sliced-Wasserstein objectives for occupancy matching.
Remained effective under up to 100× demonstration subsampling on Ant and Humanoid.
FASTRL: Reinforcement Learning in Surgical Digital Twins
Peer-reviewed work on reinforcement-learning benchmarks and assistance policies in surgical digital-twin environments.
Peer-reviewed benchmark and assistance-policy study in surgical digital twins.
Hyperbolic Wasserstein Autoencoders
Generative modeling with Wasserstein autoencoders in hyperbolic latent spaces.