Action graphs for proactive robot assistance in smart environments

Article type: Research Article

Authors: Harman, Helen^{; *} | Simoens, Pieter

Affiliations: Department of Information Technology – IDLab, Ghent University – imec, Technologiepark 126, B-9052 Ghent, Belgium. E-mails: [email protected], [email protected]

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: Smart environments can already observe the actions of a human through pervasive sensors. Based on these observations, our work aims to predict the actions a human is likely to perform next. Predictions can enable a robot to proactively assist humans by autonomously executing an action on their behalf. In this paper, Action Graphs are introduced to model the order constraints between actions. Action Graphs are derived from a problem defined in Planning Domain Definition Language (PDDL). When an action is observed, the node values are updated and next actions predicted. Subsequently, a robot executes one of the predicted actions if it does not impact the flow of the human by obstructing or delaying them. Our Action Graph approach is applied to a kitchen domain.

Keywords: Action prediction, proactive assistance, intention recognition, symbolic AI, smart environment

DOI: 10.3233/AIS-200556

Journal: Journal of Ambient Intelligence and Smart Environments, vol. 12, no. 2, pp. 79-99, 2020