ImiPath: Learning Spatiotemporal Decision Priors for Path Planning
Researchers propose ImiPath, a framework that learns reusable spatiotemporal decision priors from demonstration trajectories to guide path planning under partial observability. Classical planners solve each instance from scratch, leading to redundant node expansions and myopic search. ImiPath constructs a local spatiotemporal observation representation encoding spatial information, then distills decision priors to bias planners toward promising directions. The approach aims to improve efficiency in long-horizon navigation tasks where agents have only locally bounded observations.
Key facts
- ImiPath is a prior-guided learning framework for path planning under partial observability.
- It distills reusable spatiotemporal decision priors from demonstration trajectories.
- Classical planners lack mechanisms to exploit transferable decision knowledge.
- ImiPath constructs a local spatiotemporal observation representation.
- The framework biases planners toward reliable and promising search directions.
- It addresses challenges of long-horizon navigation with locally bounded observations.
- The paper is published on arXiv with ID 2607.22166.
- The approach aims to reduce redundant node expansions and myopic search behaviors.
Entities
Institutions
- arXiv