Can an Embodied Agent Find Your "Cat-shaped Mug"? LLM-Guided Exploration for Zero-Shot Object Navigation
On the map: LLM agents and tool use → Multimodal and embodied agents
Abstract (arXiv)
We present LGX (Language-guided Exploration), a novel algorithm for Language-Driven Zero-Shot Object Goal Navigation (L-ZSON), where an embodied agent navigates to a uniquely described target object in a previously unseen environment. Our approach makes use of Large Language Models (LLMs) for this task by leveraging the LLM's commonsense reasoning capabilities for making sequential navigational decisions. Simultaneously, we perform generalized target object detection using a pre-trained Vision-Language grounding model. We achieve state-of-the-art zero-shot object navigation results on RoboTHOR with a success rate (SR) improvement of over 27% over the current baseline of the OWL-ViT CLIP on Wheels (OWL CoW). Furthermore, we study the usage of LLMs for robot navigation and present an analysis of various prompting strategies affecting the model output. Finally, we showcase the benefits of our approach via textitreal-world experiments that indicate the superior performance of LGX in detecting and navigating to visually unique objects.
Key facts. Cited by 74 papers in the map (data through 7 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 34 of its 56 references that are in the map. First posted on arXiv 6 Mar 2023.
Builds on
34 of 56 references.
Closest ancestors · 1
Same direction, newest first
- 2022Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents18 Jan
Other references · 33
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
- 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,310
- 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
- 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,302
- 2019Habitat: A Platform for Embodied AI Researchcited by 1,163
- 2021Grounded Language-Image Pre-trainingcited by 1,147
- 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,020
- 2017Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environmentscited by 1,013
- 2022ProgPrompt: Generating Situated Robot Task Plans using Large Language Modelscited by 598
- 2021ClipCap: CLIP Prefix for Image Captioningcited by 557
21 more
- 2018On Evaluation of Embodied Navigation Agentscited by 526
- 2022LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Actioncited by 435
- 2020Object Goal Navigation using Goal-Oriented Semantic Explorationcited by 421
- 2022Visual Language Maps for Robot Navigationcited by 362
- 2019REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environmentscited by 330
- 2019Vision-and-Dialog Navigationcited by 246
- 2020ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objectscited by 240
- 2022CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigationcited by 194
- 2020RoboTHOR: An Open Simulation-to-Real Embodied AI Platformcited by 190
- 2022Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language Navigationcited by 187
- 2022ZSON: Zero-Shot Object-Goal Navigation using Multimodal Goal Embeddingscited by 177
- 2022PONI: Potential Functions for ObjectGoal Navigation with Interaction-free Learningcited by 149
- 2022Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detectioncited by 145
- 2020Sim-to-Real Transfer for Vision-and-Language Navigationcited by 88
- 2022DialFRED: Dialogue-Enabled Agents for Embodied Instruction Followingcited by 63
- 2017Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructionscited by 61
- 2022CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigationcited by 55
- 2019Improving Grounded Natural Language Understanding through Human-Robot Dialogcited by 25
- 2022Object Memory Transformer for Object Goal Navigationcited by 19
- 2018Safe Navigation with Human Instructions in Complex Scenescited by 9
- 2020Can a Robot Trust You? A DRL-Based Approach to Trust-Driven Human-Guided Navigationcited by 6
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Citing papers per year: 2023: 9; 2024: 37; 2025: 19; 2026: 9.
74 papers.
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- 2025Curriculum-Based Multi-Tier Semantic Exploration via Deep Reinforcement Learning11 Sep 2025
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- 2025From Seeing to Experiencing: Scaling Navigation Foundation Models with Reinforcement Learning29 Jul 2025
- 2025ForesightNav: Learning Scene Imagination for Efficient Exploration22 Apr 2025
- 2025HomeEmergency -- Using Audio to Find and Respond to Emergencies in the Home1 Apr 2025
12 newest of 31 All 31 (JSON)
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