Personalized Robot Tutoring Using the Assistive Tutor POMDP (AT-POMDP)

Ramachandran, Aditi; Sebo, Sarah; Scassellati, Brian

doi:10.1609/aaai.v33i01.33018050

Cited by 24 publications

(16 citation statements)

References 23 publications

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“…Just as humans are capable of influencing the behaviors of the people with whom they interact, robots may exert similar effects. For example, robots can alter how well human participants complete a task (8)(9)(10)(11)(12) and how humans respond to requests (13)(14)(15) in human-robot interactions. Studying such one-on-one interactions between humans and robots is important to understanding how robots can affect direct human-robot relationships.…”

mentioning

confidence: 99%

Vulnerable robots positively shape human conversational dynamics in a human–robot team

Traeger

Sebo

Jung

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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Social robots are becoming increasingly influential in shaping the behavior of humans with whom they interact. Here, we examine how the actions of a social robot can influence human-to-human communication, and not just robot–human communication, using groups of three humans and one robot playing 30 rounds of a collaborative game (n= 51 groups). We find that people in groups with a robot making vulnerable statements converse substantially more with each other, distribute their conversation somewhat more equally, and perceive their groups more positively compared to control groups with a robot that either makes neutral statements or no statements at the end of each round. Shifts in robot speech have the power not only to affect how people interact with robots, but also how people interact with each other, offering the prospect for modifying social interactions via the introduction of artificial agents into hybrid systems of humans and machines.

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mentioning

confidence: 99%

Vulnerable robots positively shape human conversational dynamics in a human–robot team

Traeger

Sebo

Jung

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

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“…Another mentioned solution was model-based RL, of which we see two examples in social robotics [ 49 , 50 ]. In addition, long-term interactions (several sessions [ 58 , 68 , 102 ]) are important for HRI and could be beneficial for RL to collect samples.…”

Section: Discussionmentioning

confidence: 99%

“…The fifth challenge is “tasks that may be partially observable, alternatively viewed as non-stationary or stochastic”. We see several attempts in social robotics to deal with this challenge such as in POMDP based approaches [ 50 , 102 ], and in DRL where several frames are stacked together for incorporating the history of the agent observations. Another mentioned approach to deal with this challenge was using recurrent networks which were applied in [ 63 ].…”

Section: Discussionmentioning

confidence: 99%

“…Several studies presented only the learning evolution of their system without mentioning how a participant would perceive the learned robot behaviors [ 13 , 34 , 61 , 63 , 64 , 65 , 66 , 108 ]. Comparison of user experiences (e.g., learning gains of children) for adaptive and non-adaptive robot is another way of evaluation [ 68 , 102 ]. We also see evaluation by using only interaction related objective measures such as the frequency of turn-taking and dialogue duration with the robot [ 60 ].…”

Section: Evaluation Methodologiesmentioning

confidence: 99%

“…Instead, the human’s emotions or verbal instructions serve as reward or guidance signals. The studies in this category are [ 28 , 31 , 32 , 45 , 50 , 55 , 56 , 57 , 59 , 68 , 90 , 91 , 100 , 101 , 102 ].…”

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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