ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2009.09689
83
105
v1v2v3v4 (latest)

Reinforcement Learning Approaches in Social Robotics

21 September 2020
Neziha Akalin
Amy Loutfi
    OffRL
ArXiv (abs)PDFHTML
Abstract

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.

View on arXiv
Comments on this paper