# Adaptive Informative Path Planning with Multimodal Sensing

arXiv: https://arxiv.org/abs/2003.09746 · HTML: https://mlsanity.com/paper/2003.09746/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2020.

## Built on by (most cited first, 9 of 9)

- [Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing](https://mlsanity.com/paper/2109.13570/) (2021)
- [An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping](https://mlsanity.com/paper/2302.03347/) (2023)
- [Semantically-driven Deep Reinforcement Learning for Inspection Path Planning](https://mlsanity.com/paper/2505.14443/) (2025)
- [Sequential Bayesian Optimization for Adaptive Informative Path Planning with Multimodal Sensing](https://mlsanity.com/paper/2209.07660/) (2022)
- [Active Measuring in Reinforcement Learning With Delayed Negative Effects](https://mlsanity.com/paper/2510.14315/) (2025)
- [PACE: Adaptive Budget Allocation for Time-Efficient Embodied Planning](https://mlsanity.com/paper/2608.03034/) (2026)
- [Cost Aware Asynchronous Multi-Agent Active Search](https://mlsanity.com/paper/2210.02259/) (2022)
- [Attention-based Learning for 3D Informative Path Planning](https://mlsanity.com/paper/2506.08434/) (2025)
- [Cooperative Informative Sensing for Monitoring Dynamic Indoor Environments via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2604.23179/) (2026)

## Abstract (arXiv)

Adaptive Informative Path Planning (AIPP) problems model an agent tasked with obtaining information subject to resource constraints in unknown, partially observable environments. Existing work on AIPP has focused on representing observations about the world as a result of agent movement. We formulate the more general setting where the agent may choose between different sensors at the cost of some energy, in addition to traversing the environment to gather information. We call this problem AIPPMS (MS for Multimodal Sensing). AIPPMS requires reasoning jointly about the effects of sensing and movement in terms of both energy expended and information gained. We frame AIPPMS as a Partially Observable Markov Decision Process (POMDP) and solve it with online planning. Our approach is based on the Partially Observable Monte Carlo Planning framework with modifications to ensure constraint feasibility and a heuristic rollout policy tailored for AIPPMS. We evaluate our method on two domains: a simulated search-and-rescue scenario and a challenging extension to the classic RockSample problem. We find that our approach outperforms a classic AIPP algorithm that is modified for AIPPMS, as well as online planning using a random rollout policy.