Data to 7 Oct 2026

Learning to Explore using Active Neural SLAM

arXiv 2004.05155 ↗First posted 10 Apr 2020cs.CV cs.AI cs.LGcited by 308

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Abstract (arXiv)

This work presents a modular and hierarchical approach to learn policies for exploring 3D environments, called `Active Neural SLAM'. Our approach leverages the strengths of both classical and learning-based methods, by using analytical path planners with learned SLAM module, and global and local policies. The use of learning provides flexibility with respect to input modalities (in the SLAM module), leverages structural regularities of the world (in global policies), and provides robustness to errors in state estimation (in local policies). Such use of learning within each module retains its benefits, while at the same time, hierarchical decomposition and modular training allow us to sidestep the high sample complexities associated with training end-to-end policies. Our experiments in visually and physically realistic simulated 3D environments demonstrate the effectiveness of our approach over past learning and geometry-based approaches. The proposed model can also be easily transferred to the PointGoal task and was the winning entry of the CVPR 2019 Habitat PointGoal Navigation Challenge.

Key facts. Cited by 308 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Apr 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 57; 2022: 63; 2023: 64; 2024: 55; 2025: 32; 2026: 37.

308 papers.

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2023DUSt3R: Geometric 3D Vision Made Easycited by 1,526
2024Grounding Image Matching in 3D with MASt3Rcited by 960
2022LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Actioncited by 435
2024Navigation World Modelscited by 262
2021A Survey of Embodied AI: From Simulators to Research Taskscited by 256
2023Diffusion-based Generation, Optimization, and Planning in 3D Scenescited by 237
2024NaVILA: Legged Robot Vision-Language-Action Model for Navigationcited by 201
2022CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigationcited by 194
2021Simple but Effective: CLIP Embeddings for Embodied AIcited by 191
2022Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language Navigationcited by 187
2023ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environmentscited by 165
2022Open-vocabulary Queryable Scene Representations for Real World Planningcited by 159

38 more

2022PONI: Potential Functions for ObjectGoal Navigation with Interaction-free Learningcited by 149
2022Bridging the Gap Between Learning in Discrete and Continuous Environments for Vision-and-Language Navigationcited by 127
2023ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigationcited by 126
2021Waypoint Models for Instruction-guided Navigation in Continuous Environmentscited by 125
2021FILM: Following Instructions in Language with Modular Methodscited by 119
2021SOON: Scenario Oriented Object Navigation with Graph-based Explorationcited by 118
2021Rapid Exploration for Open-World Navigation with Latent Goal Modelscited by 118
2022Navigating to Objects in the Real Worldcited by 104
2021Visual Room Rearrangementcited by 101
2021Structured Scene Memory for Vision-Language Navigationcited by 95
2022Cross-modal Map Learning for Vision and Language Navigationcited by 91
2022Sim-2-Sim Transfer for Vision-and-Language Navigation in Continuous Environmentscited by 80
2023Bird's-Eye-View Scene Graph for Vision-Language Navigationcited by 71
2021Memory-Augmented Reinforcement Learning for Image-Goal Navigationcited by 64
2021The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AIcited by 64
2021No RL, No Simulation: Learning to Navigate without Navigatingcited by 63
2024Volumetric Environment Representation for Vision-Language Navigationcited by 63
2024PoliFormer: Scaling On-Policy RL with Transformers Results in Masterful Navigatorscited by 55
2022NeurAR: Neural Uncertainty for Autonomous 3D Reconstruction with Implicit Neural Representationscited by 54
2023How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontierscited by 53
2021RobustNav: Towards Benchmarking Robustness in Embodied Navigationcited by 52
2023Renderable Neural Radiance Map for Visual Navigationcited by 51
2021Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networkscited by 50
2023Navigating to Objects Specified by Imagescited by 50
2024GaussNav: Gaussian Splatting for Visual Navigationcited by 49
2021SASRA: Semantically-aware Spatio-temporal Reasoning Agent for Vision-and-Language Navigation in Continuous Environmentscited by 48
2024RoboEXP: Action-Conditioned Scene Graph via Interactive Exploration for Robotic Manipulationcited by 48
2021Learning to Map for Active Semantic Goal Navigationcited by 47
2021SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistencycited by 47
2023SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real Worldcited by 47
2021Coupling Vision and Proprioception for Navigation of Legged Robotscited by 46
2022Sound Adversarial Audio-Visual Navigationcited by 46
2022Multi-skill Mobile Manipulation for Object Rearrangementcited by 46
2022Retrospectives on the Embodied AI Workshopcited by 46
20223D-Aware Object Goal Navigation via Simultaneous Exploration and Identificationcited by 46
2025ACTIVE-o3: Empowering MLLMs with Active Perception via Pure Reinforcement Learningcited by 41
2021Catch Me If You Hear Me: Audio-Visual Navigation in Complex Unmapped Environments with Moving Soundscited by 40
2023Learning Navigational Visual Representations with Semantic Map Supervisioncited by 40

50 most cited of 308 All 308 (JSON)