activity
20172023
most citedPaLM-E: An Embodied Multimodal Language Model

356 citations · 1.5k across the 30 of their papers we have counts for

collaborators

31 papers

cs.CV2023★ 3 cited

Video Language Planning

Yilun Du, Mengjiao Yang, Pete Florence +10

We are interested in enabling visual planning for complex long-horizon tasks in the space of generated videos and language, leveraging recent advances in large generative models pr…

cs.RO2023★ 273 cited

RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Anthony Brohan, Noah Brown, Justice Carbajal +51

We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic…

cs.CL2023★ 28 cited

Towards Generalist Biomedical AI

Tao Tu, Shekoofeh Azizi, Danny Driess +29

Medicine is inherently multimodal, with rich data modalities spanning text, imaging, genomics, and more. Generalist biomedical artificial intelligence (AI) systems that flexibly en…

cs.RO2023★ 12 cited

Scaling Up and Distilling Down: Language-Guided Robot Skill Acquisition

Huy Ha, Pete Florence, Shuran Song

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…

cs.AI2023★ 33 cited

Large Language Models as General Pattern Machines

Suvir Mirchandani, Fei Xia, Pete Florence +6

We observe that pre-trained large language models (LLMs) are capable of autoregressively completing complex token sequences -- from arbitrary ones procedurally generated by probabi…

cs.LG2023★ 356 cited

PaLM-E: An Embodied Multimodal Language Model

Danny Driess, Fei Xia, Mehdi S. M. Sajjadi +19

Large language models excel at a wide range of complex tasks. However, enabling general inference in the real world, e.g., for robotics problems, raises the challenge of grounding.…