The grand aim of having a single robot that can manipulate arbitrary objects in diverse
settings is at odds with the paucity of robotics datasets. Acquiring and growing such datasets …

The field of visual representation learning has seen explosive growth in the past years, but
its benefits in robotics have been surprisingly limited so far. Prior work uses generic visual …

The ability to leverage heterogeneous robotic experience from different robots and tasks to
quickly master novel skills and embodiments has the potential to transform robot learning …

Prompt-based learning has emerged as a successful paradigm in natural language
processing, where a single general-purpose language model can be instructed to perform …

Large policies pretrained on diverse robot datasets have the potential to transform robotic
learning: instead of training new policies from scratch, such generalist robot policies may be …

Robots operating in the real world require both rich manipulation skills as well as the ability
to semantically reason about when to apply those skills. Towards this goal, recent works …

The ability to leverage heterogeneous robotic experience from different robots and tasks to
quickly master novel skills and embodiments has the potential to transform robot learning …

Foundation models have made significant strides in various applications, including text-to-
image generation, panoptic segmentation, and natural language processing. This paper …

The creation of large, diverse, high-quality robot manipulation datasets is an important
stepping stone on the path toward more capable and robust robotic manipulation policies …

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 …