Towards Empathetic Human-Robot Interactions

Abstract

Since the late 1990s when speech companies began providing their customer-service software in the market, people have gotten used to speaking to machines. As people interact more often with voice and gesture controlled machines, they expect the machines to recognize different emotions, and understand other high level communication features such as humor, sarcasm and intention. In order to make such communication possible, the machines need an empathy module in them, which is a software system that can extract emotions from human speech and behavior and can decide the correct response of the robot. Although research on empathetic robots is still in the primary stage, current methods involve using signal processing techniques, sentiment analysis and machine learning algorithms to make robots that can 'understand' human emotion. Other aspects of human-robot interaction include facial expression and gesture recognition, as well as robot movement to convey emotion and intent. We propose Zara the Supergirl as a prototype system of empathetic robots. It is a software-based virtual android, with an animated cartoon character to present itself on the screen. She will get 'smarter' and more empathetic, by having machine learning algorithms, and gathering more data and learning from it. In this paper, we present our work so far in the areas of deep learning of emotion and sentiment recognition, as well as humor recognition. We hope to explore the future direction of android development and how it can help improve people's lives.

Bilingual Word Embeddings for Cross-Lingual Personality Recognition Using Convolutional Neural Nets

“…Personality is a significant factor of communication in human-human interaction [1,2], and as virtual agents and dialogue systems get more intelligent, we expect them to identify and adapt to different user personalities. Having such a personality recognition module in dialogue systems will enable us to have more intelligent and personal human-agent conversations in the future [3,4].…”

Participatory Research Principles in Human-Centered Design: Engaging Teens in the Co-Design of a Social Robot

“…Within the increasing number of studies of human-robot interaction (HRI), there has been a great deal of focus on physical safety [28], teleoperation of robots [29][30][31], the quality of HRI [32], collaboration [33,34] and navigation [35][36][37]. More recently, in relation to social robots, there has been an increasing focus on empathy [38][39][40] and trust [41][42][43][44], important factors in developing a social robot. However, increasing our understanding of these important factors as they relate to HRI requires appropriate and contextually valid methods to inform research findings and robotic design.…”

Proceedings of ACL 2017, System Demonstrations

“…The default method is to utilize the global attention mechanism. However there are many other types of attention that have recently proposed including local attention (Luong et al, 2015), sparse-max attention (Martins and Astudillo, 2016), hierarchical attention (Yang et al, 2016) among others. As this is simply a module in OpenNMT it can easily be substituted.…”