学习时空决策先验以实现部分可观测性下的高效路径规划
Learning Spatiotemporal Decision Priors for Efficient Path Planning under Partial Observability
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- College of Intelligent Robotics and Advanced Manufacturing, Fudan University（智能机器人与先进制造学院，复旦大学）
- School of Information Science and Technology, Fudan University（信息科学与技术学院，复旦大学）
- Department of Information Technology, Old Dominion University（信息技术系，老 Dominion 大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
研究部分可观测性下的路径规划问题，提出ImiPath框架，从示范轨迹提炼时空决策先验，经局部时空观测表示和时空注意力策略网络转换为决策先验并纳入异构规划器，提升路径质量与搜索效率，验证了框架的适应性和实际部署潜力。
中文摘要 AI 辅助
部分可观测性下的路径规划具有挑战性，因为智能体只能根据局部有限观测做出长期导航决策。不过历史轨迹包含可复用的经验引导方向偏好。经典规划器通常从头解决每个实例，缺乏利用此类可转移决策知识的机制，常导致冗余节点扩展和局部近视搜索行为。本文提出ImiPath，一个先验引导学习框架，从示范轨迹中提炼可复用的时空决策先验，并用作经验导向的方向指引，使规划器在部分可观测性下偏向可靠且有前景的搜索方向。具体而言，ImiPath首先构建局部时空观测表示，编码局部环境空间信息和历史轨迹的时间信息。然后时空注意力策略网络将此表示转换为决策先验。这些先验进一步作为方向指引纳入异构规划器，使搜索偏向局部有前景区域。大量实验表明ImiPath在局部可观测性下实现了有竞争力的路径质量并通过减少冗余节点扩展提高了搜索效率。在磁微机器人平台上的额外物理实验进一步验证了该框架的适应性和实际部署潜力。
英文摘要
Path planning under partial observability remains challenging because an agent must make long-horizon navigation decisions from only locally bounded observations. Nevertheless, historical trajectories contain reusable experience-guided directional preferences. Classical planners, however, typically solve each instance from scratch and lack an explicit mechanism to exploit such transferable decision knowledge, often leading to redundant node expansions and locally myopic search behaviors. Motivated by this limitation, this paper proposes ImiPath, a prior-guided learning framework that distills reusable spatiotemporal decision priors from demonstration trajectories and uses them as experience-informed directional guidance to bias planners toward reliable and promising search directions under partial observability. Specifically, ImiPath first constructs a local spatiotemporal observation representation, which encodes the spatial information of the local environment and the temporal information of historical trajectories. The SpatioTemporal-Attention Policy Network (STAPNet) then transforms this representation into dicision priors. These priors are further incorporated into heterogeneous planners as directional guidance, biasing the search toward locally promising regions. Extensive experiments demonstrate that ImiPath achieves competitive path quality and improves search efficiency by reducing redundant node expansions under local observability. Additional physical experiments on a magnetic microrobot platform further validate the adaptability and practical deployment potential of the proposed framework.
