Large, high-capacity models trained on diverse datasets have shown remarkable successes
on efficiently tackling downstream applications. In domains from NLP to Computer Vision …

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 …

Progress in deep learning highlights the tremendous potential of utilizing diverse robotic
datasets for attaining effective generalization and makes it enticing to consider leveraging …

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 …

Task specification is at the core of programming autonomous robots. A low-effort modality for
task specification is critical for engagement of non-expert end users and ultimate adoption of …

Robot learning holds the promise of learning policies that generalize broadly. However,
such generalization requires sufficiently diverse datasets of the task of interest, which can be …

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 …

The use of broad datasets has proven to be crucial for generalization for a wide range of
fields. However, how to effectively make use of diverse multi-task data for novel downstream …

We consider the problem of learning useful robotic skills from previously collected offline
data without access to manually specified rewards or additional online exploration, a setting …

It is a long-standing problem in robotics to develop agents capable of executing diverse
manipulation tasks from visual observations in unstructured real-world environments. To …