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

Large language models (LLMs) have undergone significant expansion and have been
increasingly integrated across various domains. Notably, in the realm of robot task planning …

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 …

Humans can teleoperate robots to accomplish complex manipulation tasks. Imitation
learning has emerged as a powerful framework that leverages human teleoperated …

Recent work has shown promising results for learning end-to-end robot policies using
imitation learning. In this work we address the question of how far can we push imitation …

In this paper, we propose a real-world benchmark for studying robotic learning in the context
of functional manipulation: a robot needs to accomplish complex long-horizon behaviors by …

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 …

We introduce UMI-on-Legs, a new framework that combines real-world and simulation data
for quadruped manipulation systems. We scale task-centric data collection in the real world …

Accurate reconstruction of complex dynamic scenes from just a single viewpoint continues to
be a challenging task in computer vision. Current dynamic novel view synthesis methods …