Abstract
This paper proposes a novel system for managing visual attention in social robots. This system is based on a client/server approach that allows integration with a cognitive architecture controlling the robot. The core of this architecture is a distributed knowledge graph, in which the perceptual needs are expressed by the presence of arcs to stimuli that need to be perceived. The attention server sends motion commands to the actuators of the robot, while the attention clients send requests through the common knowledge representation. The common knowledge graph is shared by all levels of the architecture. This system has been implemented on ROS and tested on a social robot to verify the validity of the approach and was used to solve the tests proposed in RoboCup @ Home and SciROc robotic competitions. The tests have been used to quantitatively compare the proposal to traditional visual attention mechanisms.
Highlights
Mobile social robots incorporate a myriad of sensors and actuators (Kanda and Ishiguro, 2017), for example sonar and LIDAR sensors for obstacle detection, autonomous location and navigation, microphones and speakers for human-robot interaction, and more and more commonly, different types of cameras
The new approach presented in this paper differs from that work in that the attention system does not arbitrate among behavioral needs, but among elements to be perceived that are indicated by the planning system at the highest level of the cognitive architecture
The main contribution made in this paper is the design of the visual attention system. This system is integrated into the cognitive architecture through the knowledge graph, where visual perception requirements are expressed through the creation of arcs between nodes that indicate these requirements
Summary
Mobile social robots incorporate a myriad of sensors and actuators (Kanda and Ishiguro, 2017), for example sonar and LIDAR sensors for obstacle detection, autonomous location and navigation, microphones and speakers for human-robot interaction, and more and more commonly, different types of cameras. Visual Attention in Social Robots account in the evolution of visual attention systems is the integration into complex robotic software architectures which are in charge of selecting the most adequate behavior to fulfill the robot’s task This integration requires the attention system to be modular, parametrizable, and able to share a common way of representing information. The new approach presented in this paper differs from that work in that the attention system does not arbitrate among behavioral needs, but among elements to be perceived that are indicated by the planning system at the highest level of the cognitive architecture. The main contribution made in this paper is the design of the visual attention system This system is integrated into the cognitive architecture through the knowledge graph, where visual perception requirements are expressed through the creation of arcs between nodes that indicate these requirements.
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