# Personalization in Human-Robot Interaction through Preference-based Action Representation Learning

arXiv: https://arxiv.org/abs/2409.13822 · HTML: https://mlsanity.com/paper/2409.13822/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2024.

## Built on by (most cited first, 4 of 4)

- [Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees](https://mlsanity.com/paper/2410.08934/) (2024)
- [Coloring Between the Lines: Personalization in the Null Space of Planning Constraints](https://mlsanity.com/paper/2505.15503/) (2025)
- [PrefMoE: Robust Preference Modeling with Mixture-of-Experts Reward Learning](https://mlsanity.com/paper/2605.00384/) (2026)
- [Beyond Partner Diversity: An Influence-Based Team Steering Framework for Zero-Shot Human-Machine Teaming](https://mlsanity.com/paper/2605.15400/) (2026)

## Abstract (arXiv)

Preference-based reinforcement learning (PbRL) has shown significant promise for personalization in human-robot interaction (HRI) by explicitly integrating human preferences into the robot learning process. However, existing practices often require training a personalized robot policy from scratch, resulting in inefficient use of human feedback. In this paper, we propose preference-based action representation learning (PbARL), an efficient fine-tuning method that decouples common task structure from preference by leveraging pre-trained robot policies. Instead of directly fine-tuning the pre-trained policy with human preference, PbARL uses it as a reference for an action representation learning task that maximizes the mutual information between the pre-trained source domain and the target user preference-aligned domain. This approach allows the robot to personalize its behaviors while preserving original task performance and eliminates the need for extensive prior information from the source domain, thereby enhancing efficiency and practicality in real-world HRI scenarios. Empirical results on the Assistive Gym benchmark and a real-world user study (N=8) demonstrate the benefits of our method compared to state-of-the-art approaches.