TIDE: A Trace-Informed Depth-First Exploration for Planning with Temporally Extended Goals

TL;DR

TIDE is a planning algorithm that decomposes complex temporal goals into manageable sub-problems, guiding search with heuristics and adaptive backtracking to improve efficiency in achieving temporally extended objectives.

Contribution

TIDE introduces a trace-informed, heuristic-guided depth-first exploration method that decomposes temporal planning problems into sub-problems, enhancing planning efficiency for temporally extended goals.

Findings

TIDE outperforms baseline planners in complex temporal goal scenarios.

It effectively decomposes problems into smaller reach-avoid tasks.

Adaptive backtracking improves planning completeness and robustness.

Abstract

Task planning with temporally extended goals (TEGs) is a critical challenge in AI and robotics, enabling agents to achieve complex sequences of objectives over time rather than addressing isolated, immediate tasks. Linear Temporal Logic on finite traces (LTLf ) provides a robust formalism for encoding these temporal goals. Traditional LTLf task planning approaches often transform the temporal planning problem into a classical planning problem with reachability goals, which are then solved using off-the-shelf planners. However, these methods often lack informed heuristics to provide a guided search for temporal goals. We introduce TIDE (Trace-Informed Depth-first Exploration), a novel approach that addresses this limitation by decomposing a temporal problem into a sequence of smaller, manageable reach-avoid sub-problems, each solvable using an off-the-shelf planner. TIDE identifies and prioritizes promising automaton traces within the domain graph, using cost-driven heuristics to guide exploration. Its adaptive backtracking mechanism systematically recovers from failed plans by recalculating costs and penalizing infeasible transitions, ensuring completeness and efficiency. Experimental results demonstrate that TIDE achieves promising performance and is a valuable addition to the portfolio of planning methods for temporally extended goals.