GLEAM: Learning Generalizable Exploration Policy for Active Mapping in Complex 3D Indoor Scenes
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Abstract (arXiv)
Generalizable active mapping in complex unknown environments remains a critical challenge for mobile robots. Existing methods, constrained by insufficient training data and conservative exploration strategies, exhibit limited generalizability across scenes with diverse layouts and complex connectivity. To enable scalable training and reliable evaluation, we introduce GLEAM-Bench, the first large-scale benchmark designed for generalizable active mapping with 1,152 diverse 3D scenes from synthetic and real-scan datasets. Building upon this foundation, we propose GLEAM, a unified generalizable exploration policy for active mapping. Its superior generalizability comes mainly from our semantic representations, long-term navigable goals, and randomized strategies. It significantly outperforms state-of-the-art methods, achieving 66.50% coverage (+9.49%) with efficient trajectories and improved mapping accuracy on 128 unseen complex scenes. Project page: https://xiao-chen.tech/gleam/.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 42 of its 59 references that are in the map. First posted on arXiv 26 May 2025.
Builds on
42 of 59 references.
References · 42
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30 more
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Built on by
Citing papers per year: 2025: 5; 2026: 6.
11 papers.
- 2025InfiniBench: Infinite Benchmarking for Visual Spatial Reasoning with Customizable Scene Complexitycited by 5
- 2026MAGICIAN: Efficient Long-Term Planning with Imagined Gaussians for Active Mappingcited by 5
- 2025AREA3D: Active Reconstruction Agent with Unified Feed-Forward 3D Perception and Vision-Language Guidancecited by 4
- 2025Video4Spatial: Towards Visuospatial Intelligence with Context-Guided Video Generationcited by 3
- 2026Explore Like Humans: Autonomous Exploration with Online SG-Memo Construction for Embodied Agentscited by 3
- 2025SEA: Semantic Map Prediction for Active Exploration of Uncertain Areascited by 2
- 2026ObjSplat: Geometry-Aware Gaussian Surfels for Active Object Reconstructioncited by 2
- 2025Hestia: Voxel-Face-Aware Hierarchical Next-Best-View Acquisition for Efficient 3D Reconstructioncited by 1
- 2026Remember to be Curious: Episodic Context and Persistent Worlds for 3D Explorationcited by 0
- 2026VANDERER: Map-Free Exploration using Future-Aware and Visual-Curiosity-Guided Diffusion Policycited by 0
- 2026Multi-Scale Semantic Mapping in Urban Environments via Observation Calibration and Policy Dependence Regularizationcited by 0
- 2026Multi-Scale Semantic Mapping in Urban Environments via Observation Calibration and Policy Dependence Regularization28 Sep 2026
- 2026VANDERER: Map-Free Exploration using Future-Aware and Visual-Curiosity-Guided Diffusion Policy12 Jun 2026
- 2026Remember to be Curious: Episodic Context and Persistent Worlds for 3D Exploration21 May 2026
- 2026Explore Like Humans: Autonomous Exploration with Online SG-Memo Construction for Embodied Agents21 Apr 2026
- 2026MAGICIAN: Efficient Long-Term Planning with Imagined Gaussians for Active Mapping23 Mar 2026
- 2026ObjSplat: Geometry-Aware Gaussian Surfels for Active Object Reconstruction11 Jan 2026
- 2025Video4Spatial: Towards Visuospatial Intelligence with Context-Guided Video Generation2 Dec 2025
- 2025AREA3D: Active Reconstruction Agent with Unified Feed-Forward 3D Perception and Vision-Language Guidance28 Nov 2025
- 2025InfiniBench: Infinite Benchmarking for Visual Spatial Reasoning with Customizable Scene Complexity22 Nov 2025
- 2025SEA: Semantic Map Prediction for Active Exploration of Uncertain Areas22 Oct 2025
- 2025Hestia: Voxel-Face-Aware Hierarchical Next-Best-View Acquisition for Efficient 3D Reconstruction1 Aug 2025