# Feedback-efficient Active Preference Learning for Socially Aware Robot Navigation

arXiv: https://arxiv.org/abs/2201.00469 · HTML: https://mlsanity.com/paper/2201.00469/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2022.

## Built on by (most cited first, 7 of 7)

- [Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning](https://mlsanity.com/paper/2405.00746/) (2024)
- [Learning Implicit Social Navigation Behavior using Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/2501.06946/) (2025)
- [Boosting Feedback Efficiency of Interactive Reinforcement Learning by Adaptive Learning from Scores](https://mlsanity.com/paper/2307.05405/) (2023)
- [The Impact of VR and 2D Interfaces on Human Feedback in Preference-Based Robot Learning](https://mlsanity.com/paper/2503.16500/) (2025)
- [PrefMoE: Robust Preference Modeling with Mixture-of-Experts Reward Learning](https://mlsanity.com/paper/2605.00384/) (2026)
- [Belief Aided Navigation using Bayesian Reinforcement Learning for Avoiding Humans in Blind Spots](https://mlsanity.com/paper/2403.10105/) (2024)
- [From Imitation to Alignment: Human-Preference Flow Policies for Long-Horizon Sidewalk Navigation](https://mlsanity.com/paper/2606.12603/) (2026)

## Abstract (arXiv)

Socially aware robot navigation, where a robot is required to optimize its trajectory to maintain comfortable and compliant spatial interactions with humans in addition to reaching its goal without collisions, is a fundamental yet challenging task in the context of human-robot interaction. While existing learning-based methods have achieved better performance than the preceding model-based ones, they still have drawbacks: reinforcement learning depends on the handcrafted reward that is unlikely to effectively quantify broad social compliance, and can lead to reward exploitation problems; meanwhile, inverse reinforcement learning suffers from the need for expensive human demonstrations. In this paper, we propose a feedback-efficient active preference learning approach, FAPL, that distills human comfort and expectation into a reward model to guide the robot agent to explore latent aspects of social compliance. We further introduce hybrid experience learning to improve the efficiency of human feedback and samples, and evaluate benefits of robot behaviors learned from FAPL through extensive simulation experiments and a user study (N=10) employing a physical robot to navigate with human subjects in real-world scenarios. Source code and experiment videos for this work are available at:https://sites.google.com/view/san-fapl.