A Reinforcement Learning Based Cognitive Empathy Framework for Social Robots

2020 ◽  
Author(s):  
Elahe Bagheri ◽  
Oliver Roesler ◽  
Hoang-Long Cao ◽  
Bram Vanderborght
Keyword(s):  
Reinforcement Learning ◽  
Cognitive Empathy ◽  
Social Robots

scholarly journals Reinforcement Learning Approaches in Social Robotics

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1292
Author(s):  
Neziha Akalin ◽  
Amy Loutfi
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Social Robotics ◽  
Research Field ◽  
Social Robots ◽  
Learning Approaches ◽  
Reward Function ◽  
Optimal Behavior ◽  
Learning Challenges ◽  
Starting Point

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.

Emotion and memory model for social robots: a reinforcement learning based behaviour selection

2021 ◽  
pp. 1-27
Author(s):  
Muneeb Imtiaz Ahmad ◽  
Yuan Gao ◽  
Fady Alnajjar ◽  
Suleman Shahid ◽  
Omar Mubin
Keyword(s):  
Reinforcement Learning ◽  
Social Robots ◽  
Memory Model ◽  
Emotion And Memory

scholarly journals Empathy and Instrumentalization: Late Ancient Cultural Critique and the Challenge of Apparently Personal Robots

2020 ◽  
Author(s):  
Jordan Joseph Wales
Keyword(s):  
Cognitive Empathy ◽  
Consumer Products ◽  
Social Robots ◽  
Text Messages ◽  
Training Data ◽  
Cultural Critique ◽  
North African ◽  
Augustine Of Hippo ◽  
The Social ◽  
Near Future

According to a tradition that we hold variously today, the relational person lives most personally in affective and cognitive empathy, whereby we enter subjective communion with another person. Near future social AIs, including social robots, will give us this experience without possessing any subjectivity of their own. They will also be consumer products, designed to be subservient instruments of their users’ satisfaction. This would seem inevitable. Yet we cannot live as personal when caught between instrumentalizing apparent persons (slaveholding) or numbly dismissing the apparent personalities of our instruments (mild sociopathy). This paper analyzes and proposes a step toward ameliorating this dilemma by way of the thought of a 5th century North African philosopher and theologian, Augustine of Hippo, who is among those essential in giving us our understanding of relational persons. Augustine’s semiotics, deeply intertwined with our affective life, suggest that, if we are to own persuasive social robots humanely, we must join our instinctive experience of empathy for them to an empathic acknowledgment of the real unknown relational persons whose emails, text messages, books, and bodily movements will have provided the training data for the behavior of near-future social AIs. So doing, we may see simulation as simulation (albeit persuasive), while expanding our empathy to include those whose refracted behavioral moments are the seedbed of this simulation. If we naïvely stop at the social robot as the ultimate object of our cognitive and affective empathy, we will suborn the sign to ourselves, undermining rather than sustaining a culture that prizes empathy and abhors the instrumentalization of persons.

scholarly journals On Safety and Time Efficiency Enhancement of Robot Navigation in Crowded Environment utilizing Deep Reinforcement Learning

2021 ◽  
Author(s):  
Sunil Srivatsav Samsani
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Industrial Applications ◽  
Social Robots ◽  
Efficiency Enhancement ◽  
Time Efficiency ◽  
Learning Framework ◽  
Multi Agent ◽  
Ablation Study ◽  
Crowded Environment

<div>The evolution of social robots has increased with the advent of recent artificial intelligence techniques. Alongside humans, social robots play active roles in various household and industrial applications. However, the safety of humans becomes a significant concern when robots navigate in a complex and crowded environment. In literature, the safety of humans in relation to social robots has been addressed by various methods; however, most of these methods compromise the time efficiency of the robot. For robots, safety and time-efficiency are two contrast elements where one dominates the other. To strike a balance between them, a multi-reward formulation in the reinforcement learning framework is proposed, which improves the safety together with time-efficiency of the robot. The multi-reward formulation includes both positive and negative rewards that encourage and punish the robot, respectively. The proposed reward formulation is tested on state-of-the-art methods of multi-agent navigation. In addition, an ablation study is performed to evaluate the importance of individual rewards. Experimental results signify that the proposed approach balances the safety and the time-efficiency of the robot while navigating in a crowded environment.</div>

scholarly journals On Safety and Time Efficiency Enhancement of Robot Navigation in Crowded Environment utilizing Deep Reinforcement Learning

2021 ◽  
Author(s):  
Sunil Srivatsav Samsani
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Industrial Applications ◽  
Social Robots ◽  
Efficiency Enhancement ◽  
Time Efficiency ◽  
Learning Framework ◽  
Multi Agent ◽  
Ablation Study ◽  
Crowded Environment

<div>The evolution of social robots has increased with the advent of recent artificial intelligence techniques. Alongside humans, social robots play active roles in various household and industrial applications. However, the safety of humans becomes a significant concern when robots navigate in a complex and crowded environment. In literature, the safety of humans in relation to social robots has been addressed by various methods; however, most of these methods compromise the time efficiency of the robot. For robots, safety and time-efficiency are two contrast elements where one dominates the other. To strike a balance between them, a multi-reward formulation in the reinforcement learning framework is proposed, which improves the safety together with time-efficiency of the robot. The multi-reward formulation includes both positive and negative rewards that encourage and punish the robot, respectively. The proposed reward formulation is tested on state-of-the-art methods of multi-agent navigation. In addition, an ablation study is performed to evaluate the importance of individual rewards. Experimental results signify that the proposed approach balances the safety and the time-efficiency of the robot while navigating in a crowded environment.</div>

Bayes factors for reinforcement-learning models of the Iowa gambling task.

Decision ◽  
2016 ◽  
Vol 3 (2) ◽  
pp. 115-131 ◽  
Author(s):  
Helen Steingroever ◽  
Ruud Wetzels ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Reinforcement Learning ◽  
Iowa Gambling Task ◽  
Bayes Factors ◽  
Gambling Task ◽  
Learning Models ◽  
Reinforcement Learning Models
Sign in / Sign up

Export Citation Format

Share Document