Recent works have shown how the reasoning capabilities of Large Language Models (LLMs) can be applied to domains beyond natural language processing, such as planning …
H Ha, P Florence, S Song - Conference on Robot Learning, 2023 - proceedings.mlr.press
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 …
Large pretrained (eg," foundation") models exhibit distinct capabilities depending on the domain of data they are trained on. While these domains are generic, they may only barely …
This paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot's visuomotor policy as a conditional denoising diffusion process. We …
I Radosavovic, T Xiao, S James… - … on Robot Learning, 2023 - proceedings.mlr.press
In this work, we explore self-supervised visual pre-training on images from diverse, in-the- wild videos for real-world robotic tasks. Like prior work, our visual representations are pre …
Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously difficult for robots because they require precision, careful coordination of contact forces, and …
How can we imbue robots with the ability to manipulate objects precisely but also to reason about them in terms of abstract concepts? Recent works in manipulation have shown that …
Building a robot that can understand and learn to interact by watching humans has inspired several vision problems. However, despite some successful results on static datasets, it …
NM Shafiullah, Z Cui… - Advances in neural …, 2022 - proceedings.neurips.cc
While behavior learning has made impressive progress in recent times, it lags behind computer vision and natural language processing due to its inability to leverage large …