Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …

The rise of deep learning has caused a paradigm shift in robotics research, favoring
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …

Testing the full autonomy system in simulation is the safest and most scalable way to
evaluate autonomous vehicle performance before deployment. This requires simulating …

We study off-dynamics Reinforcement Learning (RL), where the policy is trained on a source
domain and deployed to a distinct target domain. We aim to solve this problem via online …

Integrating physics models within machine learning models holds considerable promise
toward learning robust models with improved interpretability and abilities to extrapolate. In …

Shifting from traditional control strategies to Deep Reinforcement Learning (RL) for legged
robots poses inherent challenges, especially when addressing real-world physical …