Sampling-based motion planning is one of the fundamental paradigms to generate robot
motions, and a cornerstone of robotics research. This comparative review provides an up-to …

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

Transformers have revolutionized vision and natural language processing with their ability to
scale with large datasets. But in robotic manipulation, data is both limited and expensive …

Abstract We propose Text2Motion, a language-based planning framework enabling robots
to solve sequential manipulation tasks that require long-horizon reasoning. Given a natural …

We present a framework for robot skill acquisition, which 1) efficiently scale up data
generation of language-labelled robot data and 2) effectively distills this data down into a …

We survey applications of pretrained foundation models in robotics. Traditional deep
learning models in robotics are trained on small datasets tailored for specific tasks, which …

Large-scale task planning is a major challenge. Recent work exploits large language
models (LLMs) directly as a policy and shows surprisingly interesting results. This paper …

Abstract We introduce Habitat 2.0 (H2. 0), a simulation platform for training virtual robots in
interactive 3D environments and complex physics-enabled scenarios. We make …

Loco-manipulation planning skills are pivotal for expanding the utility of robots in everyday
environments. These skills can be assessed on the basis of a system's ability to coordinate …

For effective human-robot interaction, robots need to understand, plan, and execute
complex, long-horizon tasks described by natural language. Recent advances in large …