# Explore-Bench: Data Sets, Metrics and Evaluations for Frontier-based and Deep-reinforcement-learning-based Autonomous Exploration

arXiv: https://arxiv.org/abs/2202.11931 · HTML: https://mlsanity.com/paper/2202.11931/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2022.

## Built on by (most cited first, 8 of 8)

- [Deep Reinforcement Learning-based Large-scale Robot Exploration](https://mlsanity.com/paper/2403.10833/) (2024)
- [DARE: Diffusion Policy for Autonomous Robot Exploration](https://mlsanity.com/paper/2410.16687/) (2024)
- [Learning Coverage Paths in Unknown Environments with Deep Reinforcement Learning](https://mlsanity.com/paper/2306.16978/) (2023)
- [HDPlanner: Advancing Autonomous Deployments in Unknown Environments through Hierarchical Decision Networks](https://mlsanity.com/paper/2408.03768/) (2024)
- [Sim-to-Real Transfer of Deep Reinforcement Learning Agents for Online Coverage Path Planning](https://mlsanity.com/paper/2406.04920/) (2024)
- [HEADER: Hierarchical Robot Exploration via Attention-Based Deep Reinforcement Learning with Expert-Guided Reward](https://mlsanity.com/paper/2510.15679/) (2025)
- [FARE: Fast-Slow Agentic Robotic Exploration](https://mlsanity.com/paper/2601.14681/) (2026)
- [A Graph-Based Reinforcement Learning Approach with Frontier Potential Based Reward for Safe Cluttered Environment Exploration](https://mlsanity.com/paper/2504.11907/) (2025)

## Abstract (arXiv)

Autonomous exploration and mapping of unknown terrains employing single or multiple robots is an essential task in mobile robotics and has therefore been widely investigated. Nevertheless, given the lack of unified data sets, metrics, and platforms to evaluate the exploration approaches, we develop an autonomous robot exploration benchmark entitled Explore-Bench. The benchmark involves various exploration scenarios and presents two types of quantitative metrics to evaluate exploration efficiency and multi-robot cooperation. Explore-Bench is extremely useful as, recently, deep reinforcement learning (DRL) has been widely used for robot exploration tasks and achieved promising results. However, training DRL-based approaches requires large data sets, and additionally, current benchmarks rely on realistic simulators with a slow simulation speed, which is not appropriate for training exploration strategies. Hence, to support efficient DRL training and comprehensive evaluation, the suggested Explore-Bench designs a 3-level platform with a unified data flow and 12 × speed-up that includes a grid-based simulator for fast evaluation and efficient training, a realistic Gazebo simulator, and a remotely accessible robot testbed for high-accuracy tests in physical environments. The practicality of the proposed benchmark is highlighted with the application of one DRL-based and three frontier-based exploration approaches. Furthermore, we analyze the performance differences and provide some insights about the selection and design of exploration methods. Our benchmark is available at https://github.com/efc-robot/Explore-Bench.