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

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 …

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 …

We address the challenge of acquiring real-world manipulation skills with a scalable
framework. Inspired by the success of large-scale auto-regressive prediction in Large …

A key challenge for robotic manipulation in open domains is how to acquire diverse and
generalizable skills for robots. Recent progress in one-shot imitation learning and robotic …

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 …

ROBEL is an open-source platform of cost-effective robots designed for reinforcement
learning in the real world. ROBEL introduces two robots, each aimed to accelerate …

We present RoboGen, a generative robotic agent that automatically learns diverse robotic
skills at scale via generative simulation. RoboGen leverages the latest advancements in …