# HALO: Human Preference Aligned Offline Reward Learning for Robot Navigation

arXiv: https://arxiv.org/abs/2508.01539 · HTML: https://mlsanity.com/paper/2508.01539/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2025.

## Built on by (most cited first, 5 of 5)

- [World-in-World: World Models in a Closed-Loop World](https://mlsanity.com/paper/2510.18135/) (2025)
- [MM-Nav: Multi-View VLA Model for Robust Visual Navigation via Multi-Expert Learning](https://mlsanity.com/paper/2510.03142/) (2025)
- [RoDiF: Robust Direct Fine-Tuning of Diffusion Policies with Corrupted Human Feedback](https://mlsanity.com/paper/2602.00886/) (2026)
- [From Imitation to Alignment: Human-Preference Flow Policies for Long-Horizon Sidewalk Navigation](https://mlsanity.com/paper/2606.12603/) (2026)
- [Beyond Pairwise Feedback: Listwise Vision-Language Supervision for Preference-Based Reward Learning](https://mlsanity.com/paper/2608.25350/) (2026)

## Abstract (arXiv)

In this paper, we introduce HALO, a novel Offline Reward Learning algorithm that quantifies human intuition in navigation into a vision-based reward function for robot navigation. HALO learns a reward model from offline data, leveraging expert trajectories collected from mobile robots. During training, actions are uniformly sampled around a reference action and ranked using preference scores derived from a Boltzmann distribution centered on the preferred action, and shaped based on binary user feedback to intuitive navigation queries. The reward model is trained via the Plackett-Luce loss to align with these ranked preferences. To demonstrate the effectiveness of HALO, we deploy its reward model in two downstream applications: (i) an offline learned policy trained directly on the HALO-derived rewards, and (ii) a model-predictive-control (MPC) based planner that incorporates the HALO reward as an additional cost term. This showcases the versatility of HALO across both learning-based and classical navigation frameworks. Our real-world deployments on a Clearpath Husky across diverse scenarios demonstrate that policies trained with HALO generalize effectively to unseen environments and hardware setups not present in the training data. HALO outperforms state-of-the-art vision-based navigation methods, achieving at least a 33.3% improvement in success rate, a 12.9% reduction in normalized trajectory length, and a 26.6% reduction in Frechet distance compared to human expert trajectories.