Gradient-Based Mixed Planning with Symbolic and Numeric Action Parameters

TL;DR

This paper introduces a gradient-based algorithm framework for mixed logical and numeric planning problems, enabling more effective handling of real-world numeric effects without discretization or convexity constraints.

Contribution

It extends planning syntax to include real-valued parameters and develops a gradient descent approach to optimize numeric parameters and generate plans simultaneously.

Findings

Effective in solving mixed logical and numeric planning problems.

Handles obstacles and non-linear numeric effects efficiently.

Outperforms existing methods in empirical evaluations.

Abstract

Dealing with planning problems with both logical relations and numeric changes in real-world dynamic environments is challenging. Existing numeric planning systems for the problem often discretize numeric variables or impose convex constraints on numeric variables, which harms the performance when solving problems. In this paper, we propose a novel algorithm framework to solve numeric planning problems mixed with logical relations and numeric changes based on gradient descent. We cast the numeric planning with logical relations and numeric changes as an optimization problem. Specifically, we extend syntax to allow parameters of action models to be either objects or real-valued numbers, which enhances the ability to model real-world numeric effects. Based on the extended modeling language, we propose a gradient-based framework to simultaneously optimize numeric parameters and compute appropriate actions to form candidate plans. The gradient-based framework is composed of an algorithmic heuristic module based on propositional operations to select actions and generate constraints for gradient descent, an algorithmic transition module to update states to next ones, and a loss module to compute loss. We repeatedly minimize loss by updating numeric parameters and compute candidate plans until it converges into a valid plan for the planning problem. In the empirical study, we exhibit that our algorithm framework is both effective and efficient in solving planning problems mixed with logical relations and numeric changes, especially when the problems contain obstacles and non-linear numeric effects.