# Learning Augmented, Multi-Robot Long-Horizon Navigation in Partially Mapped Environments

arXiv: https://arxiv.org/abs/2303.16654 · HTML: https://mlsanity.com/paper/2303.16654/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2023.

## Built on by (most cited first, 2 of 2)

- [Object Search in Partially-Known Environments via LLM-informed Model-based Planning and Prompt Selection](https://mlsanity.com/paper/2603.23800/) (2026)
- [Scout-Assisted Planning for Heterogeneous Robot Teams under Partially Known Environments](https://mlsanity.com/paper/2605.22693/) (2026)

## Abstract (arXiv)

We present a novel approach for efficient and reliable goal-directed long-horizon navigation for a multi-robot team in a structured, unknown environment by predicting statistics of unknown space. Building on recent work in learning-augmented model based planning under uncertainty, we introduce a high-level state and action abstraction that lets us approximate the challenging Dec-POMDP into a tractable stochastic MDP. Our Multi-Robot Learning over Subgoals Planner (MR-LSP) guides agents towards coordinated exploration of regions more likely to reach the unseen goal. We demonstrate improvement in cost against other multi-robot strategies; in simulated office-like environments, we show that our approach saves 13.29% (2 robot) and 4.6% (3 robot) average cost versus standard non-learned optimistic planning and a learning-informed baseline.