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pith:EFBOAY3N

pith:2018:EFBOAY3NFQ2XQGIPSTPK7P4HYN

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On Evaluation of Embodied Navigation Agents

Alexey Dosovitskiy, Amir R. Zamir, Angel Chang, Devendra Singh Chaplot, Jana Kosecka, Jitendra Malik, Manolis Savva, Peter Anderson, Roozbeh Mottaghi, Saurabh Gupta, Vladlen Koltun

Embodied navigation research requires standardized evaluation measures and scenarios to allow direct comparison of agents.

arxiv:1807.06757 v1 · 2018-07-18 · cs.AI · cs.CV · cs.LG · cs.RO

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

To coordinate ongoing and future research in this area, we have convened a working group to study empirical methodology in navigation research. The present document summarizes the consensus recommendations of this working group. We discuss different problem statements and the role of generalization, present evaluation measures, and provide standard scenarios that can be used for benchmarking.

C2weakest assumption

That the research community will adopt the proposed evaluation measures and standard scenarios rather than continuing with incompatible custom protocols.

C3one line summary

Consensus recommendations for standardized evaluation measures, problem statements, and benchmarking scenarios in embodied navigation research.

References

32 extracted · 32 resolved · 1 Pith anchors

[1] P. Anderson, Q. Wu, D. Teney, J. Bruce, M. Johnson, N. S ¨underhauf, I. Reid, S. Gould, and A. van den Hen- gel. Vision-and-language navigation: Interpreting visually- grounded navigation instructions 2018

[2] DeepMind Lab 2016 · arXiv:1612.03801

[3] S. Brahmbhatt and J. Hays. DeepNav: Learning to navigate large cities. In CVPR, 2017 2017

[4] S. Brodeur, E. Perez, A. Anand, F. Golemo, L. Celotti, F. Strub, J. Rouat, H. Larochelle, and A. C. Courville. HoME: A household multimodal environment. arXiv:1711.11017, 2017 2017

[5] R. A. Brooks and M. J. Mataric. Real robots, real learning problems. In Robot Learning. 1993 1993

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Cited by

40 papers in Pith

Collision-Aware Object-Goal Visual Navigation via Two-Stage Deep Reinforcement Learning

AwareVLN: Reasoning with Self-awareness for Vision-Language Navigation

CLUE: Adaptively Prioritized Contextual Cues by Leveraging a Unified Semantic Map for Effective Zero-Shot Object-Goal Navigation

MORN: Metacognitive Object-Goal Regulation for Resource-Rational Long-Horizon Navigation

NORM-Nav: Zero-Shot Mobile Robot Navigation with Natural Language Behavioral Constraints

Receipt and verification

First computed	2026-05-18T04:05:26.137734Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

2142e0636d2c3578190f94deafbf87c3557980a226eb7042789571153780bb09

Aliases

arxiv: 1807.06757 · arxiv_version: 1807.06757v1 · doi: 10.48550/arxiv.1807.06757 · pith_short_12: EFBOAY3NFQ2X · pith_short_16: EFBOAY3NFQ2XQGIP · pith_short_8: EFBOAY3N

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/EFBOAY3NFQ2XQGIPSTPK7P4HYN \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 2142e0636d2c3578190f94deafbf87c3557980a226eb7042789571153780bb09

Canonical record JSON

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