Learning Empathy-Driven Emotion Expressions using Affective Modulations

Churamani, Nikhil; Barros, Pablo; Strahl, Erik; Wermter, Stefan

doi:10.1109/ijcnn.2018.8489158

Cited by 25 publications

(43 citation statements)

References 22 publications

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“…As an example, in Qureshi et al [ 36 , 73 , 74 ]’s study, the model performance on a test dataset was evaluated by three volunteers who judged if the robot’s action was an appropriate one for the current scenario. In [ 87 ], there both annotators and participants rated whether the robot was able to associate the facial expressions with the conversation context. The independent annotators’ ratings were higher than the participants’, which, as the authors argued, might be explained by discrepancies between the participants’ actual expressed emotion and the intended emotion.…”

Section: Evaluation Methodologiesmentioning

confidence: 99%

“…In these cases, DRL can be useful. In fact, several researchers have begun to examine the applicability of DRL in social robotics [ 35 , 36 , 73 , 74 , 85 , 86 , 87 ].…”

Section: Categorization Of Rl Approaches In Social Robotics Based mentioning

confidence: 99%

“…Expressive robot behaviors including facial expressions, gestures, and posture are found to be useful to express the robots’ internal states, goals, and desires [ 89 ]. To date, several studies have investigated the production of expressive robot behaviors using DRL, including gaze [ 85 , 86 ] and facial expressions [ 87 ]. Lathuiliere et al [ 85 ] modeled Q-learning with a Long Short Term Memory (LSTM) to fuse audio and visual data for controlling the gaze of the robotic head to direct it towards the targets of interest.…”

Section: Categorization Of Rl Approaches In Social Robotics Based mentioning

confidence: 99%

“…Changing the LSTM size did not make a substantial difference in the results. In a similar vein, Churamani et al [ 87 ] utilized visual and audial data for enabling the Nico robot to express empathy towards the users. They focused on both recognizing the emotions of the user and generating emotions for the robot to display.…”

Section: Categorization Of Rl Approaches In Social Robotics Based mentioning

confidence: 99%

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Reinforcement Learning Approaches in Social Robotics

Akalın

Loutfi

2021

Sensors

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This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field.

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Section: Evaluation Methodologiesmentioning

confidence: 99%

“…In these cases, DRL can be useful. In fact, several researchers have begun to examine the applicability of DRL in social robotics [ 35 , 36 , 73 , 74 , 85 , 86 , 87 ].…”

Section: Categorization Of Rl Approaches In Social Robotics Based mentioning

confidence: 99%

Section: Categorization Of Rl Approaches In Social Robotics Based mentioning

confidence: 99%

Section: Categorization Of Rl Approaches In Social Robotics Based mentioning

confidence: 99%

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Reinforcement Learning Approaches in Social Robotics

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Loutfi

2021

Sensors

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“…It has a symmetrical and abstracted child-like appearance that aims to enable intuitive humanrobot interaction while avoiding the uncanny-valley effect. Behind the surface of the head, in the eyebrow and mouth area, a programmable LED display is placed, that can display basic emotions in the form of stylized facial expressions [14], [15]. The head features two 2 Megapixel sensors with a 70-degree field of vision.…”

Section: Affective Association Modellingmentioning

confidence: 99%

Disambiguating Affective Stimulus Associations for Robot Perception and Dialogue

Siqueira

Sutherland

Barros

et al. 2018

2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids)

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Effectively recognising and applying emotions to interactions is a highly desirable trait for social robots. Implicitly understanding how subjects experience different kinds of actions and objects in the world is crucial for natural HRI interactions, with the possibility to perform positive actions and avoid negative actions. In this paper, we utilize the NICO robot's appearance and capabilities to give the NICO the ability to model a coherent affective association between a perceived auditory stimulus and a temporally asynchronous emotion expression. This is done by combining evaluations of emotional valence from vision and language. NICO uses this information to make decisions about when to extend conversations in order to accrue more affective information if the representation of the association is not coherent. Our primary contribution is providing a NICO robot with the ability to learn the affective associations between a perceived auditory stimulus and an emotional expression. NICO is able to do this for both individual subjects and specific stimuli, with the aid of an emotion-driven dialogue system that rectifies emotional expression incoherences. The robot is then able to use this information to determine a subject's enjoyment of perceived auditory stimuli in a real HRI scenario.

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