Pointing Gestures for Human-Robot Interaction in Service Robotics: A Feasibility Study

Research in service robotics strives at having a positive impact on people's quality of life by the introduction of robotic helpers for everyday activities. From this ambition arises the need of enabling natural communication between robots and ordinary people. For this reason, Human-Robot Interaction (HRI) is an extensively investigated topic, exceeding language-based exchange of information, to include all the relevant facets of communication. Each aspect of communication (e.g. hearing, sight, touch) comes with its own peculiar strengths and limits, thus they are often combined to improve robustness and naturalness. In this contribution, an HRI framework is presented, based on pointing gestures as the preferred interaction strategy. Pointing gestures are selected as they are an innate behavior to direct another attention, and thus could represent a natural way to require a service to a robot. To complement the visual information, the user could be prompted to give voice commands to resolve ambiguities and prevent the execution of unintended actions. The two layers (perceptive and semantic) architecture of the proposed HRI system is described. The perceptive layer is responsible for objects mapping, action detection, and assessment of the indicated direction. Moreover, it has to listen to uses' voice commands. To avoid privacy issues and not burden the computational resources of the robot, the interaction would be triggered by a wake-word detection system. The semantic layer receives the information processed by the perceptive layer and determines which actions are available for the selected object. The decision is based on object's characteristics, contextual information and user vocal feedbacks are exploited to resolve ambiguities. A pilot implementation of the semantic layer is detailed, and qualitative results are shown. The preliminary findings on the validity of the proposed system, as well as on the limitations of a purely vision-based approach, are discussed.