306 citations · 371 across the 12 of their papers we have counts for
4 papers · 1 filter
From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessons
Andrew Szot, Bogdan Mazoure, Omar Attia +6
We examine the capability of Multimodal Large Language Models (MLLMs) to tackle diverse domains that extend beyond the traditional language and vision tasks these models are typica…
Grounding Multimodal Large Language Models in Actions
Andrew Szot, Bogdan Mazoure, Harsh Agrawal +3
Multimodal Large Language Models (MLLMs) have demonstrated a wide range of capabilities across many domains, including Embodied AI. In this work, we study how to best ground a MLLM…
Large Language Models as Generalizable Policies for Embodied Tasks
Andrew Szot, Max Schwarzer, Harsh Agrawal +6
We show that large language models (LLMs) can be adapted to be generalizable policies for embodied visual tasks. Our approach, called Large LAnguage model Reinforcement Learning Po…
Fabrik: An Online Collaborative Neural Network Editor
Utsav Garg, Viraj Prabhu, Deshraj Yadav +3
We present Fabrik, an online neural network editor that provides tools to visualize, edit, and share neural networks from within a browser. Fabrik provides a simple and intuitive G…