# Learning-based Methods for Adaptive Informative Path Planning

arXiv: https://arxiv.org/abs/2404.06940 · HTML: https://mlsanity.com/paper/2404.06940/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2024.

## Built on by (most cited first, 10 of 10)

- [Semantically-driven Deep Reinforcement Learning for Inspection Path Planning](https://mlsanity.com/paper/2505.14443/) (2025)
- [Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning](https://mlsanity.com/paper/2412.02316/) (2024)
- [DM-OSVP++: One-Shot View Planning Using 3D Diffusion Models for Active RGB-Based Object Reconstruction](https://mlsanity.com/paper/2504.11674/) (2025)
- [OffRIPP: Offline RL-based Informative Path Planning](https://mlsanity.com/paper/2409.16830/) (2024)
- [Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention Mechanisms](https://mlsanity.com/paper/2503.02913/) (2025)
- [Adaptive path planning for efficient object search by UAVs in agricultural fields](https://mlsanity.com/paper/2504.02473/) (2025)
- [Curvature-aware Expected Free Energy as an Acquisition Function for Bayesian Optimization](https://mlsanity.com/paper/2603.26339/) (2026)
- [A Unified Experimental Architecture for Informative Path Planning: from Simulation to Deployment with GuadalPlanner](https://mlsanity.com/paper/2602.10702/) (2026)
- [Hybrid Belief Reinforcement Learning for Efficient Coordinated Spatial Exploration](https://mlsanity.com/paper/2603.03595/) (2026)
- [CAVEAT: Recurrent Multimodal Diffusion Planning for Mapless Aerial Exploration](https://mlsanity.com/paper/2609.06623/) (2026)

## Abstract (arXiv)

Adaptive informative path planning (AIPP) is important to many robotics applications, enabling mobile robots to efficiently collect useful data about initially unknown environments. In addition, learning-based methods are increasingly used in robotics to enhance adaptability, versatility, and robustness across diverse and complex tasks. Our survey explores research on applying robotic learning to AIPP, bridging the gap between these two research fields. We begin by providing a unified mathematical framework for general AIPP problems. Next, we establish two complementary taxonomies of current work from the perspectives of (i) learning algorithms and (ii) robotic applications. We explore synergies, recent trends, and highlight the benefits of learning-based methods in AIPP frameworks. Finally, we discuss key challenges and promising future directions to enable more generally applicable and robust robotic data-gathering systems through learning. We provide a comprehensive catalogue of papers reviewed in our survey, including publicly available repositories, to facilitate future studies in the field.