# Information-Guided Robotic Maximum Seek-and-Sample in Partially Observable Continuous Environments

arXiv: https://arxiv.org/abs/1909.12216 · HTML: https://mlsanity.com/paper/1909.12216/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2019.

## Built on by (most cited first, 6 of 6)

- [Adaptive Information Belief Space Planning](https://mlsanity.com/paper/2201.05673/) (2022)
- [Multi-Agent Active Search using Detection and Location Uncertainty](https://mlsanity.com/paper/2203.04524/) (2022)
- [Cost Aware Asynchronous Multi-Agent Active Search](https://mlsanity.com/paper/2210.02259/) (2022)
- [Streaming Gaussian Dirichlet Random Fields for Spatial Predictions of High Dimensional Categorical Observations](https://mlsanity.com/paper/2402.15359/) (2024)
- [Cost-Aware Diffusion Active Search](https://mlsanity.com/paper/2602.19538/) (2026)
- [Scalable Rao-Blackwellized Online Planning for High-Dimensional POMDPs](https://mlsanity.com/paper/2609.01351/) (2026)

## Abstract (arXiv)

We present PLUMES, a planner to localizing and collecting samples at the global maximum of an a priori unknown and partially observable continuous environment. The "maximum-seek-and-sample" (MSS) problem is pervasive in the environmental and earth sciences. Experts want to collect scientifically valuable samples at an environmental maximum (e.g., an oil-spill source), but do not have prior knowledge about the phenomenon's distribution. We formulate the MSS problem as a partially-observable Markov decision process (POMDP) with continuous state and observation spaces, and a sparse reward signal. To solve the MSS POMDP, PLUMES uses an information-theoretic reward heuristic with continous-observation Monte Carlo Tree Search to efficiently localize and sample from the global maximum. In simulation and field experiments, PLUMES collects more scientifically valuable samples than state-of-the-art planners in a diverse set of environments, with various platforms, sensors, and challenging real-world conditions.