activity
20182024
most citedMixtral of Experts

136 citations · 206 across the 13 of their papers we have counts for

collaborators

13 papers

cs.CV2024★ 6 cited

Pixtral 12B

Pravesh Agrawal, Szymon Antoniak, Emma Bou Hanna +39

We introduce Pixtral-12B, a 12--billion-parameter multimodal language model. Pixtral-12B is trained to understand both natural images and documents, achieving leading performance o…

cs.RO2024

Towards Open-World Mobile Manipulation in Homes: Lessons from the Neurips 2023 HomeRobot Open Vocabulary Mobile Manipulation Challenge

Sriram Yenamandra, Arun Ramachandran, Mukul Khanna +42

In order to develop robots that can effectively serve as versatile and capable home assistants, it is crucial for them to reliably perceive and interact with a wide variety of obje…

cs.AI2024

GOAT-Bench: A Benchmark for Multi-Modal Lifelong Navigation

Mukul Khanna, Ram Ramrakhya, Gunjan Chhablani +7

The Embodied AI community has made significant strides in visual navigation tasks, exploring targets from 3D coordinates, objects, language descriptions, and images. However, these…

cs.LG2024★ 136 cited

Mixtral of Experts

Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux +23

We introduce Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model. Mixtral has the same architecture as Mistral 7B, with the difference that each layer is composed of 8…

cs.RO2023★ 2 cited

GOAT: GO to Any Thing

Matthew Chang, Theophile Gervet, Mukul Khanna +10

In deployment scenarios such as homes and warehouses, mobile robots are expected to autonomously navigate for extended periods, seamlessly executing tasks articulated in terms that…

cs.HC2023★ 15 cited

Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots

Xavier Puig, Eric Undersander, Andrew Szot +20

We present Habitat 3.0: a simulation platform for studying collaborative human-robot tasks in home environments. Habitat 3.0 offers contributions across three dimensions: (1) Accur…

cs.RO2023★ 6 cited

Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation

Theophile Gervet, Zhou Xian, Nikolaos Gkanatsios +1

3D perceptual representations are well suited for robot manipulation as they easily encode occlusions and simplify spatial reasoning. Many manipulation tasks require high spatial p…

cs.RO2023★ 13 cited

HomeRobot: Open-Vocabulary Mobile Manipulation

Sriram Yenamandra, Arun Ramachandran, Karmesh Yadav +15

HomeRobot (noun): An affordable compliant robot that navigates homes and manipulates a wide range of objects in order to complete everyday tasks. Open-Vocabulary Mobile Manipulatio…

cs.RO2023★ 2 cited

Towards Generalist Robots: A Promising Paradigm via Generative Simulation

Zhou Xian, Theophile Gervet, Zhenjia Xu +3

This document serves as a position paper that outlines the authors' vision for a potential pathway towards generalist robots. The purpose of this document is to share the excitemen…

cs.CV2023

Navigating to Objects Specified by Images

Jacob Krantz, Theophile Gervet, Karmesh Yadav +7

Images are a convenient way to specify which particular object instance an embodied agent should navigate to. Solving this task requires semantic visual reasoning and exploration o…

cs.RO2022

Navigating to Objects in the Real World

Theophile Gervet, Soumith Chintala, Dhruv Batra +2

Semantic navigation is necessary to deploy mobile robots in uncontrolled environments like our homes, schools, and hospitals. Many learning-based approaches have been proposed in r…

cs.LG2020★ 1 cited

Autonomous Graph Mining Algorithm Search with Best Speed/Accuracy Trade-off

Minji Yoon, Théophile Gervet, Bryan Hooi +1

Graph data is ubiquitous in academia and industry, from social networks to bioinformatics. The pervasiveness of graphs today has raised the demand for algorithms that can answer va…

cs.LG2018★ 25 cited

TarMAC: Targeted Multi-Agent Communication

Abhishek Das, Théophile Gervet, Joshua Romoff +4

We propose a targeted communication architecture for multi-agent reinforcement learning, where agents learn both what messages to send and whom to address them to while performing…