Artificial Empathy Classification: A Survey of Deep Learning Techniques, Datasets, and Evaluation Scales. (arXiv:2310.00010v1 [cs.RO])

From the last decade, researchers in the field of machine learning (ML) and
assistive developmental robotics (ADR) have taken an interest in artificial
empathy (AE) as a possible future paradigm for human-robot interaction (HRI).
Humans learn empathy since birth, therefore, it is challenging to instill this
sense in robots and intelligent machines. Nevertheless, by training over a vast
amount of data and time, imitating empathy, to a certain extent, can be
possible for robots. Training techniques for AE, along with findings from the
field of empathetic AI research, are ever-evolving. The standard workflow for
artificial empathy consists of three stages: 1) Emotion Recognition (ER) using
the retrieved features from video or textual data, 2) analyzing the perceived
emotion or degree of empathy to choose the best course of action, and 3)
carrying out a response action. Recent studies that show AE being used with
virtual agents or robots often include Deep Learning (DL) techniques. For
instance, models like VGGFace are used to conduct ER. Semi-supervised models
like Autoencoders generate the corresponding emotional states and behavioral
responses. However, there has not been any study that presents an independent
approach for evaluating AE, or the degree to which a reaction was empathetic.
This paper aims to investigate and evaluate existing works for measuring and
evaluating empathy, as well as the datasets that have been collected and used
so far. Our goal is to highlight and facilitate the use of state-of-the-art
methods in the area of AE by comparing their performance. This will aid
researchers in the area of AE in selecting their approaches with precision.



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