Generating SAS+ Planning Tasks of Specified Causal Structure
Michael Katz, Junkyu Lee, et al.
SoCS 2023
Robotic planning in real-world scenarios typically requires joint optimization of logic and continuous variables. A core challenge to combine the strengths of logic planners and continuous solvers is the design of an efficient interface that informs the logical search about continuous infeasibilities. In this paper we present a novel iterative algorithm that connects logic planning with nonlinear optimization through a bidirectional interface, achieved by the detection of minimal subsets of nonlinear constraints that are infeasible. The algorithm continuously builds a database of graphs that represent (in)feasible subsets of continuous variables and constraints, and encodes this knowledge in the logical description. As a foundation for this algorithm, we introduce Planning with Nonlinear Transition Constraints (PNTC), a novel planning formulation that clarifies the exact assumptions our algorithm requires and can be applied to model Task and Motion Planning (TAMP) efficiently. Our experimental results show that our framework significantly outperforms alternative optimization-based approaches for TAMP.
Michael Katz, Junkyu Lee, et al.
SoCS 2023
Vishal Pallagani, Keerthiram Murugesan, et al.
AAAI 2024
Brandon Rozek, Junkyu Lee, et al.
AAAI 2024
Clement Gehring, Masataro Asai, et al.
ICAPS 2022