GLAM: 大域的時空間記憶に基づく潜在世界モデルによる能動的探索とナビゲーション
GLAM: Training a latent world model over global spatiotemporal memory for active exploration and navigation
地図レベルの潜在トークン上で未来の地図表現とウェイポイントを予測するJEPA型世界モデルGLAMを提案し、ObjectNavタスクでベースラインを上回る成功率と経路長効率を達成した。
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著者: I-Tak Ieong, Ruizhi Feng, Zhaoyang Lu, Yifei Cao, Jiayao Zhao, Leon Li, Senhua Zhu, Wenbo Ding
分類: cs.RO
原文アブストラクト
Active exploration and semantic navigation require an embodied agent to build memory from partial observations, predict how the evolution of observed spatial memory may support future motion, and convert that prediction into actionable plans. We present GLAM, a goal-conditioned latent world model trained over global spatiotemporal memory, and GLAM NAV, the complete navigation system built around it. Given historical map tokens, a navigation goal, and the current robot pose, GLAM jointly predicts future map representations and robot-centric waypoint latents, allowing future spatial context and navigation intent to be inferred in a shared representation space. The model follows a JEPA-like latent prediction paradigm, operates directly on map-level latent tokens rather than RGB reconstruction, and uses a pretrained waypoint encoder-decoder to supervise and decode navigation plans within GLAM NAV. Training data are collected by replaying ObjectNav expert trajectories in Habitat over HM3D v0.2 scene assets and slicing them into multi-timescale prediction samples. On a controlled HM3D-ObjectNav subset reproduction setting, GLAM NAV improves over a reproduced BSC-Nav baseline in both success rate and success weighted by path length.