524 citations · 1.9k across the 45 of their papers we have counts for
7 papers · 1 filter
RT-1: Robotics Transformer for Real-World Control at Scale
Anthony Brohan, Noah Brown, Justice Carbajal +48
By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specif…
Open-vocabulary Queryable Scene Representations for Real World Planning
Boyuan Chen, Fei Xia, Brian Ichter +5
Large language models (LLMs) have unlocked new capabilities of task planning from human instructions. However, prior attempts to apply LLMs to real-world robotic tasks are limited…
Code as Policies: Language Model Programs for Embodied Control
Jacky Liang, Wenlong Huang, Fei Xia +5
Large language models (LLMs) trained on code completion have been shown to be capable of synthesizing simple Python programs from docstrings [1]. We find that these code-writing LL…
LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action
Dhruv Shah, Blazej Osinski, Brian Ichter +1
Goal-conditioned policies for robotic navigation can be trained on large, unannotated datasets, providing for good generalization to real-world settings. However, particularly in v…
Inner Monologue: Embodied Reasoning through Planning with Language Models
Wenlong Huang, Fei Xia, Ted Xiao +14
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 and interactio…
Mechanical Search on Shelves with Efficient Stacking and Destacking of Objects
Huang Huang, Letian Fu, Michael Danielczuk +6
Stacking increases storage efficiency in shelves, but the lack of visibility and accessibility makes the mechanical search problem of revealing and extracting target objects diffic…
Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
Michael Ahn, Anthony Brohan, Noah Brown +42
Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extend…