Two papers have been accepted at the 36th International Conference on Automated Planning and Scheduling (ICAPS 2026).
1️. "Interleaving Scheduling and Motion Planning with Incremental Learning of Symbolic Space-Time Motion Abstractions" by Elisa Tosello, Arthur Bit-Monnot, Davide Lusuardi, Alessandro Valentini and Andrea Micheli:
Task and Motion Planning combines high-level task sequencing with low-level motion planning to produce feasible, collision-free execution plans. In domains like automated warehouses, where tasks are predefined, the challenge is deciding if, when, and how to execute them under resource, time and motion constraints. We formalize this as the Scheduling and Motion Planning problem and propose a framework that interleaves off-the-shelf schedulers and motion planners: the scheduler generates candidate plans, while the motion planner checks feasibility and provides feedback on spatial conflicts and timing adjustments to guide motion-feasible planning.
2️. Compiling Temporal Numeric Planning into Discrete PDDL+ " by Andrea Micheli, Enrico Scala, and Alessandro Valentini:
Since the introduction of the PDDL+ modeling language, it was known that temporal planning with durative actions (as modeled in PDDL 2.1) could be compiled into PDDL+. However, no practical compilation was presented in the literature ever since. In this paper, we present a practical compilation from temporal planning with durative actions into PDDL+, fully capturing the semantics and only assuming the non-self-overlapping of actions. Our compilation retains the plan length up to a constant factor and is shown to be of practical relevance for hard temporal numeric planning problems in our experiments.