Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously
difficult for robots because they require precision, careful coordination of contact forces, and …

In this paper, we study the problem of enabling a vision-based robotic manipulation system
to generalize to novel tasks, a long-standing challenge in robot learning. We approach the …

Our goal is to train a policy for autonomous driving via imitation learning that is robust
enough to drive a real vehicle. We find that standard behavior cloning is insufficient for …

Vision-based urban driving is hard. The autonomous system needs to learn to perceive the
world and act in it. We show that this challenging learning problem can be simplified by …

Behavioral cloning reduces policy learning to supervised learning by training a
discriminative model to predict expert actions given observations. Such discriminative …

Expert demonstrations are a rich source of supervision for training visual robotic
manipulation policies, but imitation learning methods often require either a large number of …

Exhaustive enumeration of all possible join orders is often avoided, and most optimizers
leverage heuristics to prune the search space. The design and implementation of heuristics …

Offline reinforcement learning proposes to learn policies from large collected datasets
without interacting with the physical environment. These algorithms have made it possible to …

We learn an interactive vision-based driving policy from pre-recorded driving logs via a
model-based approach. A forward model of the world supervises a driving policy that …