# Reactive Multi-Robot Navigation in Outdoor Environments Through Uncertainty-Aware Active Learning of Human Preference Landscape

arXiv: https://arxiv.org/abs/2409.16577 · HTML: https://mlsanity.com/paper/2409.16577/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 13 of 13)

- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Gaussian Processes for Big Data](https://mlsanity.com/paper/1309.6835/) (2013)
- [Fast active learning for pure exploration in reinforcement learning](https://mlsanity.com/paper/2007.13442/) (2020)
- [Batch Active Preference-Based Learning of Reward Functions](https://mlsanity.com/paper/1810.04303/) (2018)
- [Asking Easy Questions: A User-Friendly Approach to Active Reward Learning](https://mlsanity.com/paper/1910.04365/) (2019)
- [A Framework for Interdomain and Multioutput Gaussian Processes](https://mlsanity.com/paper/2003.01115/) (2020)
- [Active Preference Learning using Maximum Regret](https://mlsanity.com/paper/2005.04067/) (2020)
- [Learning an Urban Air Mobility Encounter Model from Expert Preferences](https://mlsanity.com/paper/1907.05575/) (2019)
- [Human-guided Robot Behavior Learning: A GAN-assisted Preference-based Reinforcement Learning Approach](https://mlsanity.com/paper/2010.07467/) (2020)
- [Meta Preference Learning for Fast User Adaptation in Human-Supervisory Multi-Robot Deployments](https://mlsanity.com/paper/2103.08008/) (2021)
- [Real-time Scene Segmentation Using a Light Deep Neural Network Architecture for Autonomous Robot Navigation on Construction Sites](https://mlsanity.com/paper/1901.08630/) (2019)
- [Proficiency Constrained Multi-Agent Reinforcement Learning for Environment-Adaptive Multi UAV-UGV Teaming](https://mlsanity.com/paper/2002.03910/) (2020)
- [Synthesized Trust Learning from Limited Human Feedback for Human-Load-Reduced Multi-Robot Deployments](https://mlsanity.com/paper/2104.03151/) (2021)

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

- [Distributed Zeroth-Order Policy Gradient for Networked Multi-agent Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2605.15697/) (2026)

## Abstract (arXiv)

Compared with single robots, Multi-Robot Systems (MRS) can perform missions more efficiently due to the presence of multiple members with diverse capabilities. However, deploying an MRS in wide real-world environments is still challenging due to uncertain and various obstacles (e.g., building clusters and trees). With a limited understanding of environmental uncertainty on performance, an MRS cannot flexibly adjust its behaviors (e.g., teaming, load sharing, trajectory planning) to ensure both environment adaptation and task accomplishments. In this work, a novel joint preference landscape learning and behavior adjusting framework (PLBA) is designed. PLBA efficiently integrates real-time human guidance to MRS coordination and utilizes Sparse Variational Gaussian Processes with Varying Output Noise to quickly assess human preferences by leveraging spatial correlations between environment characteristics. An optimization-based behavior-adjusting method then safely adapts MRS behaviors to environments. To validate PLBA's effectiveness in MRS behavior adaption, a flood disaster search and rescue task was designed. 20 human users provided 1764 feedback based on human preferences obtained from MRS behaviors related to "task quality", "task progress", "robot safety". The prediction accuracy and adaptation speed results show the effectiveness of PLBA in preference learning and MRS behavior adaption.