Deep Learning-Based Recognition and Analysis of Limb-Independent Dog Behavior for Ethorobotical Application

Abstract

This paper presents a Behavior Transfer System (BTS) to model the behavior patterns of dogs and make it possible to implement the behavior patterns on mobile robots. The system relies on an iSpace based measurement system and a deep learning prediction algorithm. With the help of the measurement system, ethological measurements can be automatized to eliminate human coding errors and make the data collection process more robust and consistent. The trained neural networks have a dual purpose. First, the neural networks can be utilized to analyze ethological measurements and predict different behavior patterns of the dog. Test results show that the implemented neural networks can effectively predict the attention of the dog with 88% accuracy, the tail waging with 82% accuracy, and the contact seeking behavior with 88% accuracy. Second, implementing the neural networks previously trained on dogs can serve as a robot operational behavioral model which mimics the behavior pattern of a dog after an adequate mathematical abstraction that maps the movements of the dog into a robot movement set. The presented method of this paper can be applied to automatize the behavior coding work of ethologists and the trained neural network can be used as an abstract robot behavior control module.