# EnQuery: Ensemble Policies for Diverse Query-Generation in Preference Alignment of Robot Navigation

arXiv: https://arxiv.org/abs/2404.04852 · HTML: https://mlsanity.com/paper/2404.04852/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2024.

## Built on by (most cited first, 1 of 1)

- [The Impact of VR and 2D Interfaces on Human Feedback in Preference-Based Robot Learning](https://mlsanity.com/paper/2503.16500/) (2025)

## Abstract (arXiv)

To align mobile robot navigation policies with user preferences through reinforcement learning from human feedback (RLHF), reliable and behavior-diverse user queries are required. However, deterministic policies fail to generate a variety of navigation trajectory suggestions for a given navigation task. In this paper, we introduce EnQuery, a query generation approach using an ensemble of policies that achieve behavioral diversity through a regularization term. For a given navigation task, EnQuery produces multiple navigation trajectory suggestions, thereby optimizing the efficiency of preference data collection with fewer queries. Our methodology demonstrates superior performance in aligning navigation policies with user preferences in low-query regimes, offering enhanced policy convergence from sparse preference queries. The evaluation is complemented with a novel explainability representation, capturing full scene navigation behavior of the mobile robot in a single plot. Our code is available online at https://github.com/hrl-bonn/EnQuery.