There is a growing body of literature that formulates social human-robot interactions as sequential decision-making tasks. In such cases, reinforcement learning arises naturally since the interaction is a key component in both reinforcement learning and social robotics . This article surveys reinforcement learning approaches in social robotics. We propose a taxonomy that categorizes reinforcement learning methods in social robotics according to the nature of the reward function. We discuss the benefits and challenges of such methodsand outline possible future directions .
This article surveys reinforcement learning (RL) approaches in social robotics. RL 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 RL 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. In addition to a survey, we categorize existent RL approaches based on the design of the reward mechanisms. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. Thus, this paper aims to become a starting point for researchers interested to use and apply reinforcement learning methods in this particular research field .
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There is a growing body of literature that formulates social human-robot interactions as sequential
This article surveys reinforcement learning (RL) approaches in social robotics. RL is a framework for
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tasks. In such cases, reinforcement learning arises naturally since the
problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since
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reinforcement learning
RL
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. This article surveys reinforcement learning approaches in social robotics. We propose a taxonomy that categorizes reinforcement learning methods in social robotics according to the nature
, 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. In addition to a survey, we categorize existent RL approaches based on the design
R
function. We discuss the benefits and challenges of such methodsand outline possible future directions
mechanisms. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. Thus, this paper aims to become a starting point for researchers interested to use and apply reinforcement learning methods in this particular research field