title: GLEAM: Learning Generalizable Exploration Policy for Active Mapping in
    Complex 3D Indoor Scenes
summary: 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/.
paper_authors._id: 683552b7d34b8e5da4d9dfe3
paper_authors.name: Xiao Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T07:49:12.484Z
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paper_authors.avatarUrl: /avatars/bca0a20c305e178a3f316581a2636cb6.svg
paper_authors.fullname: Xiao Chen
paper_authors.type: user
paper_authors.user: Xiao-HF
paper_authors._id: 683552b7d34b8e5da4d9dfe4
paper_authors.name: Tai Wang
paper_authors.status: admin_assigned
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paper_authors.fullname: Tai Wang
paper_authors.type: user
paper_authors.user: taiwang
paper_authors._id: 683552b7d34b8e5da4d9dfe5
paper_authors.name: Quanyi Li
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/4b0c9bd4c207faf81a13d5b24cbf7864.svg
paper_authors.fullname: Quanyi Li
paper_authors.type: user
paper_authors.user: kok123
paper_authors._id: 683552b7d34b8e5da4d9dfe6
paper_authors.name: Tao Huang
paper_authors._id: 683552b7d34b8e5da4d9dfe7
paper_authors.name: Jiangmiao Pang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T12:47:14.318Z
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paper_authors.fullname: Jiangmiao Pang
paper_authors.type: user
paper_authors.user: Jiangmiao
paper_authors._id: 683552b7d34b8e5da4d9dfe8
paper_authors.name: Tianfan Xue
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T12:47:08.189Z
paper_authors._id: 67631f57abfbd60470d4b3c3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/5VrG0IZjYrDLiatOr4y06.png
paper_authors.fullname: Tianfan Xue
paper_authors.type: user
paper_authors.user: littlemouse9
paper_title: GLEAM: Learning Generalizable Exploration Policy for Active Mapping in
    Complex 3D Indoor Scenes
paper_summary: 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/.
paper_ai_summary: A new benchmark and policy, GLEAM-Bench and GLEAM, improve the scalability and reliability of active mapping in complex environments through semantic representations and efficient exploration strategies.
paper_ai_keywords: active mapping generalizable exploration semantic representations navigable goals randomized strategies 3D scenes synthetic datasets real-scan datasets benchmark mapping accuracy coverage trajectories