5 papers
Interleaving Scheduling and Motion Planning with Incremental Learning of Symbolic Space-Time Motion Abstractions
Elisa Tosello, Arthur Bit-Monnot, Davide Lusuardi +2
Task and Motion Planning combines high-level task sequencing (what to do) with low-level motion planning (how to do it) to generate feasible, collision-free execution plans. Howeve…
Compiling Temporal Numeric Planning into Discrete PDDL+: Extended Version
Andrea Micheli, Enrico Scala, Alessandro Valentini
Since the introduction of the PDDL+ modeling language, it was known that temporal planning with durative actions (as in PDDL 2.1) could be compiled into PDDL+. However, no practica…
Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learning
Irene Brugnara, Alessandro Valentini, Andrea Micheli
Recent work investigated the use of Reinforcement Learning (RL) for the synthesis of heuristic guidance to improve the performance of temporal planners when a domain is fixed and a…
A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements
Elisa Tosello, Alessandro Valentini, Andrea Micheli
Task And Motion Planning (TAMP) is the problem of finding a solution to an automated planning problem that includes discrete actions executable by low-level continuous motions. Thi…
Temporal Planning with Intermediate Conditions and Effects
Alessandro Valentini, Andrea Micheli, Alessandro Cimatti
Automated temporal planning is the technology of choice when controlling systems that can execute more actions in parallel and when temporal constraints, such as deadlines, are nee…