# Information Gain Is Not All You Need

arXiv: https://arxiv.org/abs/2504.01980 · HTML: https://mlsanity.com/paper/2504.01980/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 28 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [FUEL: Fast UAV Exploration using Incremental Frontier Structure and Hierarchical Planning](https://mlsanity.com/paper/2010.11561/) (2020)
- [An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments](https://mlsanity.com/paper/1909.09548/) (2019)
- [SEER: Safe Efficient Exploration for Aerial Robots using Learning to Predict Information Gain](https://mlsanity.com/paper/2209.11034/) (2022)
- [Beyond the Frontier: Predicting Unseen Walls from Occupancy Grids by Learning from Floor Plans](https://mlsanity.com/paper/2406.09160/) (2024)
- [History-aware Autonomous Exploration in Confined Environments using MAVs](https://mlsanity.com/paper/1803.10558/) (2018)
- [Fast and Compute-efficient Sampling-based Local Exploration Planning via Distribution Learning](https://mlsanity.com/paper/2202.13715/) (2022)
- [Exploration-RRT: A multi-objective Path Planning and Exploration Framework for Unknown and Unstructured Environments](https://mlsanity.com/paper/2104.03724/) (2021)
- [Exploration with Global Consistency Using Real-Time Re-integration and Active Loop Closure](https://mlsanity.com/paper/2204.02693/) (2022)
- [Robotic Exploration of Unknown 2D Environment Using a Frontier-based Automatic-Differentiable Information Gain Measure](https://mlsanity.com/paper/2011.05323/) (2020)
- [Estimating Map Completeness in Robot Exploration](https://mlsanity.com/paper/2406.13482/) (2024)

## Abstract (arXiv)

Autonomous exploration in mobile robotics often involves a trade-off between two objectives: maximizing environmental coverage and minimizing the total path length. In the widely used information gain paradigm, exploration is guided by the expected value of observations. While this approach is effective under budget-constrained settings--where only a limited number of observations can be made--it fails to align with quality-constrained scenarios, in which the robot must fully explore the environment to a desired level of certainty or quality. In such cases, total information gain is effectively fixed, and maximizing it per step can lead to inefficient, greedy behavior and unnecessary backtracking. This paper argues that information gain should not serve as an optimization objective in quality-constrained exploration. Instead, it should be used to filter viable candidate actions. We propose a novel heuristic, distance advantage, which selects candidate frontiers based on a trade-off between proximity to the robot and remoteness from other frontiers. This heuristic aims to reduce future detours by prioritizing exploration of isolated regions before the robot's opportunity to visit them efficiently has passed. We evaluate our method in simulated environments against classical frontier-based exploration and gain-maximizing approaches. Results show that distance advantage significantly reduces total path length across a variety of environments, both with and without access to prior map predictions. Our findings challenge the assumption that more accurate gain estimation improves performance and offer a more suitable alternative for the quality-constrained exploration paradigm.