Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

As robots and other intelligent agents move from simple environments and problems to more
complex, unstructured settings, manually programming their behavior has become …

By transferring knowledge from large, diverse, task-agnostic datasets, modern machine
learning models can solve specific downstream tasks either zero-shot or with small task …

For 3D object manipulation, methods that build an explicit 3D representation perform better
than those relying only on camera images. But using explicit 3D representations like voxels …

End-to-end autonomous driving provides a simple and efficient framework for autonomous
driving systems, which can directly obtain control commands from raw perception data …

Deep networks trained on demonstrations of human driving have learned to follow roads
and avoid obstacles. However, driving policies trained via imitation learning cannot be …

In order for a robot to be a generalist that can perform a wide range of jobs, it must be able to
acquire a wide variety of skills quickly and efficiently in complex unstructured environments …

Deep reinforcement learning (DRL) augments the reinforcement learning framework, which
learns a sequence of actions that maximizes the expected reward, with the representative …

General-purpose robotic systems must master a large repertoire of diverse skills to be useful
in a range of daily tasks. While reinforcement learning provides a powerful framework for …

Robot learning has emerged as a promising tool for taming the complexity and diversity of
the real world. Methods based on high-capacity models, such as deep networks, hold the …