CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation
Not on the map. Why?
Cited by 29 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
For robots to be generally useful, they must be able to find arbitrary objects described by people (i.e., be language-driven) even without expensive navigation training on in-domain data (i.e., perform zero-shot inference). We explore these capabilities in a unified setting: language-driven zero-shot object navigation (L-ZSON). Inspired by the recent success of open-vocabulary models for image classification, we investigate a straightforward framework, CLIP on Wheels (CoW), to adapt open-vocabulary models to this task without fine-tuning. To better evaluate L-ZSON, we introduce the Pasture benchmark, which considers finding uncommon objects, objects described by spatial and appearance attributes, and hidden objects described relative to visible objects. We conduct an in-depth empirical study by directly deploying 21 CoW baselines across Habitat, RoboTHOR, and Pasture. In total, we evaluate over 90k navigation episodes and find that (1) CoW baselines often struggle to leverage language descriptions, but are proficient at finding uncommon objects. (2) A simple CoW, with CLIP-based object localization and classical exploration -- and no additional training -- matches the navigation efficiency of a state-of-the-art ZSON method trained for 500M steps on Habitat MP3D data. This same CoW provides a 15.6 percentage point improvement in success over a state-of-the-art RoboTHOR ZSON model.
Key facts. Cited by 194 papers in the map (data through 7 Oct 2026). Builds on 63 of its 90 references that are in the map. First posted on arXiv 20 Mar 2022.
Builds on
63 of 90 references.
References · 63
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- 2020Object Goal Navigation using Goal-Oriented Semantic Explorationcited by 421
- 2020Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Groundingcited by 401
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- 2016Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learningcited by 351
- 2019REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environmentscited by 330
- 2020Learning to Explore using Active Neural SLAMcited by 308
- 2019Zero-Shot Semantic Segmentationcited by 245
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- 2017Cognitive Mapping and Planning for Visual Navigationcited by 209
- 2021Simple but Effective: CLIP Embeddings for Embodied AIcited by 191
- 2020RoboTHOR: An Open Simulation-to-Real Embodied AI Platformcited by 190
- 2018Zero-Shot Object Detectioncited by 189
- 2021Combined Scaling for Zero-shot Transfer Learningcited by 182
- 2022PONI: Potential Functions for ObjectGoal Navigation with Interaction-free Learningcited by 149
- 2020RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environmentscited by 135
- 2018Visual Semantic Navigation using Scene Priorscited by 127
- 2021SOON: Scenario Oriented Object Navigation with Graph-based Explorationcited by 118
- 2020PhraseCut: Language-based Image Segmentation in the Wildcited by 116
- 2019Learning Exploration Policies for Navigationcited by 113
- 2021Visual Room Rearrangementcited by 101
- 2020MultiON: Benchmarking Semantic Map Memory using Multi-Object Navigationcited by 95
- 2018Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learningcited by 91
- 2018QuadricSLAM: Dual Quadrics from Object Detections as Landmarks in Object-oriented SLAMcited by 86
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- 2021No RL, No Simulation: Learning to Navigate without Navigatingcited by 63
- 2020Semantic Visual Navigation by Watching YouTube Videoscited by 60
50 most cited of 63 All 63 (JSON)
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Citing papers per year: 2022: 1; 2023: 25; 2024: 61; 2025: 56; 2026: 51.
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