Recent developments in foundation models, like Large Language Models (LLMs) and Vision-
Language Models (VLMs), trained on extensive data, facilitate flexible application across …

Conventional wisdom holds that autoregressive models for image generation are typically
accompanied by vector-quantized tokens. We observe that while a discrete-valued space …

Open-vocabulary generalization requires robotic systems to perform tasks involving complex
and diverse environments and task goals. While the recent advances in vision language …

Imitation learning from human demonstrations has shown impressive performance in
robotics. However, most results focus on table-top manipulation, lacking the mobility and …

One of the roadblocks for training generalist robotic models today is heterogeneity. Previous
robot learning methods often collect data to train with one specific embodiment for one task …

Modern machine learning systems rely on large datasets to attain broad generalization, and
this often poses a challenge in robot learning, where each robotic platform and task might …

We present GR-2, a state-of-the-art generalist robot agent for versatile and generalizable
robot manipulation. GR-2 is first pre-trained on a vast number of Internet videos to capture …

Robot learning holds tremendous promise to unlock the full potential of flexible, general, and
dexterous robot systems, as well as to address some of the deepest questions in artificial …

Vision-Language-Action (VLA) models have shown remarkable potential in visuomotor
control and instruction comprehension through end-to-end learning processes. However …

Generalization capabilities, or rather a lack thereof, is one of the most important unsolved
problems in the field of robot learning, and while several large scale efforts have set out to …