Research

Computer Vision

An Exploration of Embodied Visual Exploration

August 21, 2020

Abstract

How should a robot equipped with a camera scope out a new environment? Despite the progress thus far, many basic questions pertinent to this problem remain unanswered: (i) What does it mean for an agent to explore its environment well? (ii) Which methods work well, and under which assumptions and environmental settings? (iii) Where do current approaches fall short, and where might future work seek to improve? Seeking answers to these questions, we first present a taxonomy for existing visual exploration algorithms and create a standard framework for benchmarking them. We then perform a thorough empirical study of the four leading paradigms with two photorealistic simulated 3D environments, a state-of-the-art exploration architecture, and diverse evaluation metrics. Our experimental results offer insights and suggest new performance metrics and baselines for future work in visual exploration. Code, models and data are publicly available: https://github.com/facebookresearch/exploring_exploration

Download the Paper

AUTHORS

Written by

Santhosh K. Ramakrishnan

Dinesh Jayaraman

Kristen Grauman

Research Topics

Computer Vision

Related Publications

May 26, 2026

Human & Machine Intelligence

Theory

Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images

Valentin Wyart, Huy V. Vo, Jean Remi King, Josephine Raugel, Jérémy Rapin, Marc Szafraniec, Max Seitzer, Patrick Labatut, Piotr Bojanowski

May 26, 2026

May 19, 2026

Human & Machine Intelligence

EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Data

Alvin W. M. Tan, Nicolas Hamilakis, Manel Khentout, Sho Tsuji, Balázs Kégl, Michael C. Frank, Angel Villar Corrales, Charles-Eric Saint-James, Dongyan Lin, Emmanuel Dupoux, Jiayi Shen, Juan Pino, Mahi Luthra, Martin Gleize, Phillip Rust, Rashel Moritz, Sheila Krogh-Jespersen, Surya Parimi, Tom Fizycki, Vanessa Stark, Yosuke Higuchi, Youssef Benchekroun

May 19, 2026

May 12, 2026

Human & Machine Intelligence

NeuralSet: A High-Performing Python Package for Neuro-AI

Corentin Bel, Linnea Evanson, Julien Gadonneix, Andrea Santos Revilla, Mingfang (Lucy) Zhang, Julie Bonnaire, Charlotte Caucheteux, Alexandre Défossez, Théo Desbordes, Pablo Diego-Simón, Shubh Khanna, Juliette Millet, Pierre Orhan, Saarang Panchavati, Antoine Ratouchniak, Alexis Thual, Hubert Jacob Banville, Jarod Levy, Jean Remi King, Josephine Raugel, Jérémy Rapin, Katelyn Begany, Marlene Careil, Simon Dahan, Sophia Houhamdi, Stéphane d'Ascoli, Teon Brooks, Yohann Benchetrit

May 12, 2026

February 27, 2026

Human & Machine Intelligence

Unified Vision–Language Modeling via Concept Space Alignment

Yifu Qiu, Holger Schwenk, Paul-Ambroise Duquenne

February 27, 2026

June 11, 2019

Computer Vision

ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero | Facebook AI Research

Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, Larry Zitnick

June 11, 2019

April 30, 2018

NLP

Computer Vision

Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent | Facebook AI Research

Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander H. Miller, Arthur Szlam, Douwe Kiela, Jason Weston

April 30, 2018

October 10, 2016

Speech & Audio

Computer Vision

Polysemous Codes | Facebook AI Research

Matthijs Douze, Hervé Jégou, Florent Perronnin

October 10, 2016

June 18, 2018

Speech & Audio

Computer Vision

Low-shot learning with large-scale diffusion | Facebook AI Research

Matthijs Douze, Arthur Szlam, Bharath Hariharan, Hervé Jégou

June 18, 2018

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.