# Intrinsically motivated reinforcement learning for human-robot interaction in the real-world

arXiv: https://arxiv.org/abs/1804.05259 · HTML: https://mlsanity.com/paper/1804.05259/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2018.

## Built on by (most cited first, 12 of 12)

- [Adversarial Imitation via Variational Inverse Reinforcement Learning](https://mlsanity.com/paper/1809.06404/) (2018)
- [A Survey on Deep Reinforcement Learning for Audio-Based Applications](https://mlsanity.com/paper/2101.00240/) (2021)
- [Open-Ended Goal Inference through Actions and Language for Human-Robot Collaboration](https://mlsanity.com/paper/2512.04453/) (2025)
- [Autonomous Human-Robot Interaction via Operator Imitation](https://mlsanity.com/paper/2504.02724/) (2025)
- [Artificial Empathy Classification: A Survey of Deep Learning Techniques, Datasets, and Evaluation Scales](https://mlsanity.com/paper/2310.00010/) (2023)
- [Multi-Agent Reinforcement Learning for Maritime Operational Technology Cyber Security](https://mlsanity.com/paper/2401.10149/) (2024)
- [A MultiModal Social Robot Toward Personalized Emotion Interaction](https://mlsanity.com/paper/2110.05186/) (2021)
- [Reinforcement Learning via Conservative Agent for Environments with Random Delays](https://mlsanity.com/paper/2507.18992/) (2025)
- [Analyzing the Hidden Activations of Deep Policy Networks: Why Representation Matters](https://mlsanity.com/paper/2103.06398/) (2021)
- [CRC-RL: A Novel Visual Feature Representation Architecture for Unsupervised Reinforcement Learning](https://mlsanity.com/paper/2301.13473/) (2023)
- [Open Problems and Modern Solutions for Deep Reinforcement Learning](https://mlsanity.com/paper/2302.02298/) (2023)
- [Boosting Deep Reinforcement Learning with Semantic Knowledge for Robotic Manipulators](https://mlsanity.com/paper/2601.16866/) (2026)

## Abstract (arXiv)

For a natural social human-robot interaction, it is essential for a robot to learn the human-like social skills. However, learning such skills is notoriously hard due to the limited availability of direct instructions from people to teach a robot. In this paper, we propose an intrinsically motivated reinforcement learning framework in which an agent gets the intrinsic motivation-based rewards through the action-conditional predictive model. By using the proposed method, the robot learned the social skills from the human-robot interaction experiences gathered in the real uncontrolled environments. The results indicate that the robot not only acquired human-like social skills but also took more human-like decisions, on a test dataset, than a robot which received direct rewards for the task achievement.