Functional Task Tree Generation from a Knowledge Graph to Solve Unseen Problems

TL;DR

This paper presents a method for generating flexible and novel task plans, called task trees, from a knowledge graph to enable robots to adapt to unseen problems, demonstrated through recipe planning.

Contribution

The paper introduces a novel approach to derive task trees with unseen object and state combinations using a knowledge graph, enhancing robot adaptability.

Findings

High accuracy in generating task plans for unseen ingredient combinations

Effective visualization of task trees showing ingredient transformations

Demonstrated flexibility with Recipe1M+ dataset recipes

Abstract

A major component for developing intelligent and autonomous robots is a suitable knowledge representation, from which a robot can acquire knowledge about its actions or world. However, unlike humans, robots cannot creatively adapt to novel scenarios, as their knowledge and environment are rigidly defined. To address the problem of producing novel and flexible task plans called task trees, we explore how we can derive plans with concepts not originally in the robot's knowledge base. Existing knowledge in the form of a knowledge graph is used as a base of reference to create task trees that are modified with new object or state combinations. To demonstrate the flexibility of our method, we randomly selected recipes from the Recipe1M+ dataset and generated their task trees. The task trees were then thoroughly checked with a visualization tool that portrays how each ingredient changes with each action to produce the desired meal. Our results indicate that the proposed method can produce task plans with high accuracy even for never-before-seen ingredient combinations.