title: Leveraging Large-Scale Semantic Networks for Adaptive Robot Task Learning and Execution.
abstract: This work seeks to leverage semantic networks containing millions of entries encoding assertions of commonsense knowledge to enable improvements in robot task execution and learning. The specific application we explore in this project is object substitution in the context of task adaptation. Humans easily adapt their plans to compensate for missing items in day-to-day tasks, substituting a wrap for bread when making a sandwich, or stirring pasta with a fork when out of spoons. Robot plan execution, however, is far less robust, with missing objects typically leading to failure if the robot is not aware of alternatives. In this article, we contribute a context-aware algorithm that leverages the linguistic information embedded in the task description to identify candidate substitution objects without reliance on explicit object affordance information. Specifically, we show that the task context provided by the task labels within the action structure of a task plan can be leveraged to disambiguate information within a noisy large-scale semantic network containing hundreds of potential object candidates to identify successful object substitutions with high accuracy. We present two extensive evaluations of our work on both abstract and real-world robot tasks, showing that the substitutions made by our system are valid, accepted by users, and lead to a statistically significant reduction in robot learning time. In addition, we report the outcomes of testing our approach with a large number of crowd workers interacting with a robot in real time.
authors.affiliation: 1 Worcester Polytechnic Institute , Worcester, Massachusetts.
authors.forename: Adrian
authors.initials: A
authors.lastname: Boteanu
authors.affiliation: 2 Georgia Institute of Technology , Atlanta, Georgia .
authors.forename: Aaron
authors.initials: A
authors.lastname: St Clair
authors.affiliation: 1 Worcester Polytechnic Institute , Worcester, Massachusetts.
authors.forename: Anahita
authors.initials: A
authors.lastname: Mohseni-Kabir
authors.affiliation: 2 Georgia Institute of Technology , Atlanta, Georgia .
authors.forename: Carl
authors.initials: C
authors.lastname: Saldanha
authors.affiliation: 2 Georgia Institute of Technology , Atlanta, Georgia .
authors.forename: Sonia
authors.initials: S
authors.lastname: Chernova
keywords: adaptation; human robot interaction; object substitution; robotics