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

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

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

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 …

The dominant paradigm for end-to-end robot learning focuses on optimizing task-specific
objectives that solve a single robotic problem such as picking up an object or reaching a …

This work proposes a retrieve-and-transfer framework for zero-shot robotic manipulation,
dubbed RAM, featuring generalizability across various objects, environments, and …

We explore whether surgical manipulation tasks can be learned on the da Vinci robot via
imitation learning. However, the da Vinci system presents unique challenges which hinder …

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

Vision Language Models (VLMs) have achieved impressive performance in 2D image
understanding, however they are still struggling with spatial understanding which is the …