Computer Science > Computer Vision and Pattern Recognition
arXiv:2210.06849 (cs)
[Submitted on 13 Oct 2022 (v1), last revised 5 Dec 2022 (this version, v3)]
Title:Retrospectives on the Embodied AI Workshop
Authors:Matt Deitke,Dhruv Batra,Yonatan Bisk,Tommaso Campari,Angel X. Chang,Devendra Singh Chaplot,Changan Chen,Claudia Pérez D'Arpino,Kiana Ehsani,Ali Farhadi,Li Fei-Fei,Anthony Francis,Chuang Gan,Kristen Grauman,David Hall,Winson Han,Unnat Jain,Aniruddha Kembhavi,Jacob Krantz,Stefan Lee,Chengshu Li,Sagnik Majumder,Oleksandr Maksymets,Roberto Martín-Martín,Roozbeh Mottaghi,Sonia Raychaudhuri,Mike Roberts,Silvio Savarese,Manolis Savva,Mohit Shridhar,Niko Sünderhauf,Andrew Szot,Ben Talbot,Joshua B. Tenenbaum,Jesse Thomason,Alexander Toshev,Joanne Truong,Luca Weihs,Jiajun Wu
View a PDF of the paper titled Retrospectives on the Embodied AI Workshop, by Matt Deitke and 38 other authors
View PDFAbstract:We present a retrospective on the state of Embodied AI research. Our analysis focuses on 13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are grouped into three themes: (1) visual navigation, (2) rearrangement, and (3) embodied vision-and-language. We discuss the dominant datasets within each theme, evaluation metrics for the challenges, and the performance of state-of-the-art models. We highlight commonalities between top approaches to the challenges and identify potential future directions for Embodied AI research.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2210.06849 [cs.CV] |
| (orarXiv:2210.06849v3 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2210.06849 arXiv-issued DOI via DataCite |
Submission history
From: Andrew Szot [view email][v1] Thu, 13 Oct 2022 09:00:52 UTC (27,882 KB)
[v2] Mon, 17 Oct 2022 18:33:29 UTC (25,725 KB)
[v3] Mon, 5 Dec 2022 04:52:40 UTC (25,724 KB)
Full-text links:
Access Paper:
- View PDF
- TeX Source
- Other Formats
View a PDF of the paper titled Retrospectives on the Embodied AI Workshop, by Matt Deitke and 38 other authors
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.