Retrospectives on the Embodied AI Workshop

Alexander Toshev; Ali Farhadi; Andrew Szot; Angel X. Chang; Aniruddha Kembhavi; Anthony Francis; Ben Talbot; Changan Chen; Chengshu Li; Chuang Gan

Retrospectives on the Embodied AI Workshop

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2210.06849 v3 pith:7EAPK2DO submitted 2022-10-13 cs.CV

Matt Deitke , Dhruv Batra , Yonatan Bisk , Tommaso Campari , Angel X. Chang , Devendra Singh Chaplot , Changan Chen , Claudia Pérez D'Arpino

show 31 more authors

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

This is my paper · ORCID

classification cs.CV

keywords embodiedchallengesresearchworkshopanalysisapproachescommonalitiescvpr

verification ladder T0 review T1 audit T2 compute T3 formal

0 comments

read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

DAgger Diffusion Navigation: DAgger Boosted Diffusion Policy for Vision-Language Navigation
cs.RO 2025-08 unverdicted novelty 6.0 of 10

A single diffusion policy trained with DAgger, without a waypoint predictor, reports better performance than two-stage waypoint-based models on VLN-CE benchmarks.

Pith tools