Learning Task Knowledge and its Scope of Applicability in Experience-Based Planning Domains

TL;DR

This paper introduces a method to automatically determine the applicability scope of activity schemata in experience-based planning domains using Three-Valued Logic Analysis, enhancing problem-solving in robotics.

Contribution

It extends previous work by generating a bounded, logical representation of task applicability, enabling automatic schema selection in complex problem classes.

Findings

Effective scope inference for activity schemata in simulated domains

Successful application to real-world tasks with a PR2 robot

Improved problem-solving efficiency in experience-based planning

Abstract

Experience-based planning domains (EBPDs) have been recently proposed to improve problem solving by learning from experience. EBPDs provide important concepts for long-term learning and planning in robotics. They rely on acquiring and using task knowledge, i.e., activity schemata, for generating concrete solutions to problem instances in a class of tasks. Using Three-Valued Logic Analysis (TVLA), we extend previous work to generate a set of conditions as the scope of applicability for an activity schema. The inferred scope is a bounded representation of a set of problems of potentially unbounded size, in the form of a 3-valued logical structure, which allows an EBPD system to automatically find an applicable activity schema for solving task problems. We demonstrate the utility of our approach in a set of classes of problems in a simulated domain and a class of real world tasks in a fully physically simulated PR2 robot in Gazebo.