Hybrid Conditional Planning using Answer Set Programming

TL;DR

This paper presents HCP-ASP, a parallel offline algorithm for hybrid conditional planning in robotics, leveraging answer set programming to model actions and sensing, enabling efficient plan computation and formalization of partial knowledge.

Contribution

The paper introduces HCP-ASP, a novel parallel ASP-based method for hybrid conditional planning that integrates continuous feasibility checks and formalizes partial knowledge without requiring prior sensing order.

Findings

Effective in robotics domain with experimental validation

Outperforms other compilation-based planners on benchmarks

Integrates continuous checks within ASP formalism

Abstract

We introduce a parallel offline algorithm for computing hybrid conditional plans, called HCP-ASP, oriented towards robotics applications. HCP-ASP relies on modeling actuation actions and sensing actions in an expressive nonmonotonic language of answer set programming (ASP), and computation of the branches of a conditional plan in parallel using an ASP solver. In particular, thanks to external atoms, continuous feasibility checks (like collision checks) are embedded into formal representations of actuation actions and sensing actions in ASP; and thus each branch of a hybrid conditional plan describes a feasible execution of actions to reach their goals. Utilizing nonmonotonic constructs and nondeterministic choices, partial knowledge about states and nondeterministic effects of sensing actions can be explicitly formalized in ASP; and thus each branch of a conditional plan can be computed by an ASP solver without necessitating a conformant planner and an ordering of sensing actions in advance. We apply our method in a service robotics domain and report experimental evaluations. Furthermore, we present performance comparisons with other compilation based conditional planners on standardized benchmark domains. This paper is under consideration for acceptance in TPLP.