GLAM: Training a latent world model over global spatiotemporal memory for active exploration and navigation

2026-09-13

AI Summary

为了解决主动探索和语义导航问题，提出GLAM模型，通过全局时空记忆训练目标条件潜世界模型，预测未来地图表示及机器人路径，提高导航成功率。

Abstract

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.

Problem

• active exploration
• semantic navigation
• spatiotemporal memory

Innovation

• Latent World Model
• Spatiotemporal Memory
• Waypoint Latents
• JEPA-like Paradigm
• Map Tokens