Top-down and Bottom-up Feature Combination for Multi-sensor Attentive Robots

TL;DR

This paper presents a multi-sensor attentive model for robots that combines features from range scanners and sonar sensors, enabling better detection of salient stimuli and goal-driven attention in complex environments.

Contribution

It introduces feature mapping functions for non-visual sensors and demonstrates their effectiveness in a simulated robotics environment.

Findings

Model effectively detects salient stimuli across multiple sensors

Enables goal-driven attention in multi-sensor robotic systems

Works in high fidelity simulated environments

Abstract

The information available to robots in real tasks is widely distributed both in time and space, requiring the agent to search for relevant data. In humans, that face the same problem when sounds, images and smells are presented to their sensors in a daily scene, a natural system is applied: Attention. As vision plays an important role in our routine, most research regarding attention has involved this sensorial system and the same has been replicated to the robotics field. However,most of the robotics tasks nowadays do not rely only in visual data, that are still costly. To allow the use of attentive concepts with other robotics sensors that are usually used in tasks such as navigation, self-localization, searching and mapping, a generic attentional model has been previously proposed. In this work, feature mapping functions were designed to build feature maps to this attentive model from data from range scanner and sonar sensors. Experiments were performed in a high fidelity simulated robotics environment and results have demonstrated the capability of the model on dealing with both salient stimuli and goal-driven attention over multiple features extracted from multiple sensors.