# Reinforcement Learning Approaches in Social Robotics

arXiv: https://arxiv.org/abs/2009.09689 · HTML: https://mlsanity.com/paper/2009.09689/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2020.

## Built on by (most cited first, 16 of 16)

- [Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQL](https://mlsanity.com/paper/2503.23157/) (2025)
- [A Survey on Human-aware Robot Navigation](https://mlsanity.com/paper/2106.11650/) (2021)
- [SocialAI: Benchmarking Socio-Cognitive Abilities in Deep Reinforcement Learning Agents](https://mlsanity.com/paper/2107.00956/) (2021)
- [Successor Feature Representations](https://mlsanity.com/paper/2110.15701/) (2021)
- [Variational Meta Reinforcement Learning for Social Robotics](https://mlsanity.com/paper/2206.03211/) (2022)
- [MOSEAC: Streamlined Variable Time Step Reinforcement Learning](https://mlsanity.com/paper/2406.01521/) (2024)
- [Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches](https://mlsanity.com/paper/2512.00049/) (2025)
- [MIRACLE: Inverse Reinforcement and Curriculum Learning Model for Human-inspired Mobile Robot Navigation](https://mlsanity.com/paper/2312.03651/) (2023)
- [Communicating Complex Decisions in Robot-Assisted Therapy](https://mlsanity.com/paper/2303.14054/) (2023)
- [FRAC-Q-Learning: A Reinforcement Learning with Boredom Avoidance Processes for Social Robots](https://mlsanity.com/paper/2311.15327/) (2023)
- [Learning to Communicate Functional States with Nonverbal Expressions for Improved Human-Robot Collaboration](https://mlsanity.com/paper/2404.19253/) (2024)
- [Variable Time Step Reinforcement Learning for Robotic Applications](https://mlsanity.com/paper/2407.00290/) (2024)
- [Towards Interpretable Foundation Models of Robot Behavior: A Task Specific Policy Generation Approach](https://mlsanity.com/paper/2407.08065/) (2024)
- [Policy Learning for Social Robot-Led Physiotherapy](https://mlsanity.com/paper/2509.11297/) (2025)
- [Formulating Reinforcement Learning for Human-Robot Collaboration through Off-Policy Evaluation](https://mlsanity.com/paper/2602.02530/) (2026)
- [Generating Natural and Expressive Robot Gestures through Iterative Reinforcement Learning with Human Feedback using LLMs](https://mlsanity.com/paper/2606.18747/) (2026)

## Abstract (arXiv)

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.