让机器人在复杂环境中自主感知，通过智能检索和生成预测提升探索效率。
Active World-Model with 4D-informed Retrieval for Exploration and Awareness
- 基于4D信息检索与动作条件几何支持，构建感知驱动的虚拟环境
- 在极端视角变化下仍保持预测一致性，优于现有几何感知基线
- 适合需要高效探索与感知决策的机器人系统研发者
物理感知在大型动态环境中由感知决策决定可观测性，而观测结果又反向影响感知决策，形成循环信息结构，使物理感知成为具有部分观测的复杂决策问题。尽管强化学习在全可观测问题上取得突破，但面对部分可观测问题（如POMDP），现实探索成本过高，且仿真到现实的迁移存在未观测视角问题。本文提出AW4RE（主动世界模型与4D信息检索用于探索与感知），一种以感知为中心的生成式世界模型，为感知查询提供传感器原生的代理环境。该模型根据所查询的感知动作，估计动作条件下的观测过程，结合4D信息检索、动作条件几何支撑与时间一致性，以及条件生成补全机制。实验表明，在极端视角偏移、时间间隙和稀疏几何支持条件下，AW4RE生成的预测更真实、一致，优于几何感知生成基线。
原文摘要 · Abstract (English)
Physical awareness, especially in a large and dynamic environment, is shaped by sensing decisions that determine observability across space, time, and scale, while observations impact the quality of sensing decisions. This loopy information structure makes physical awareness a fundamentally challenging decision problem with partial observations. While in the past decade we have witnessed the unprecedented success of reinforcement learning (RL) in problems with full observability, decision problems with partial observation, such as POMDPs, remain largely open: real-world explorations are excessively costly, while sim-to-real pipeline suffer from unobserved viewpoints. We introduce AW4RE (Active World-model with 4D-informed Retrieval for Exploration), an awareness-centric generative world model that provides a sensor-native surrogate environment for exploring sensing queries. Conditioned on a queried sensing action, AW4RE estimates the action-conditioned observation process. This is done by combining 4D-informed evidence retrieval, action-conditioned geometric support with temporal coherence, and conditional generative completion. Experiments demonstrate that AW4RE produces more grounded and consistent predictions than geometry-aware generative baselines under extreme viewpoint shifts, temporal gaps, and sparse geometric support.
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