Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Challenges in building robots that imitate people
Imitation in animals and artifacts
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Journal of Intelligent and Robotic Systems
Active vision for sociable robots
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
RoDiCA: a human-robot interaction system for treatment of childhood autism spectrum disorders
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
Hi-index | 0.00 |
Social robotics has emerged as a new research area in recent years. One of the reasons behind this emergence is the rapid pace of improvements in sensor, actuator and processing capabilities in modern hardware enabling robots to interact with humans more effectively than ever before. The motivation for the work presented in this paper is to use advanced human-robot head-eye interaction algorithms in order to create a robotic framework that assists physical therapists treating sensor-motor impairments, such as Autism and Cerebral Palsy by using robotic systems. The robotic platform used in our work is the social robot Zeno, which has a fantastically friendly appearance and bridges the previously reported uncanny valley. In this paper we report on a new coordination algorithm based on reinforcement learning implemented on Zeno for achieving human like head-eye coordination to visually engage patients with cognitive impairments. The experimental results show that the various methods implemented enables social robot Zeno achieve natural head-eye coordination with significant improvement in accuracy without the need of extensive kinematic analysis of the system.