# Benchmarking Metric Ground Navigation

arXiv: https://arxiv.org/abs/2008.13315 · HTML: https://mlsanity.com/paper/2008.13315/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2020.

## Built on by (most cited first, 20 of 20)

- [Principles and Guidelines for Evaluating Social Robot Navigation Algorithms](https://mlsanity.com/paper/2306.16740/) (2023)
- [Benchmarking Reinforcement Learning Techniques for Autonomous Navigation](https://mlsanity.com/paper/2210.04839/) (2022)
- [Grounded Curriculum Learning](https://mlsanity.com/paper/2409.19816/) (2024)
- [PIC4rl-gym: a ROS2 modular framework for Robots Autonomous Navigation with Deep Reinforcement Learning](https://mlsanity.com/paper/2211.10714/) (2022)
- [A Study on Learning Social Robot Navigation with Multimodal Perception](https://mlsanity.com/paper/2309.12568/) (2023)
- [Reward Training Wheels: Adaptive Auxiliary Rewards for Robotics Reinforcement Learning](https://mlsanity.com/paper/2503.15724/) (2025)
- [BusyBot: Learning to Interact, Reason, and Plan in a BusyBoard Environment](https://mlsanity.com/paper/2207.08192/) (2022)
- [RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation](https://mlsanity.com/paper/2505.14526/) (2025)
- [GACL: Grounded Adaptive Curriculum Learning with Active Task and Performance Monitoring](https://mlsanity.com/paper/2508.02988/) (2025)
- [Robot Navigation with Reinforcement Learned Path Generation and Fine-Tuned Motion Control](https://mlsanity.com/paper/2210.10639/) (2022)
- [IR-SIM: A Lightweight Declarative Simulator for Navigation Learning and Benchmarking](https://mlsanity.com/paper/2606.08729/) (2026)
- [BiC-MPPI: Goal-Pursuing, Sampling-Based Bidirectional Rollout Clustering Path Integral for Trajectory Optimization](https://mlsanity.com/paper/2410.06493/) (2024)
- [Soft Actor-Critic-based Control Barrier Adaptation for Robust Autonomous Navigation in Unknown Environments](https://mlsanity.com/paper/2503.08479/) (2025)
- [Path Planning using a One-shot-sampling Skeleton Map](https://mlsanity.com/paper/2507.02328/) (2025)
- [Systematic Comparison of Path Planning Algorithms using PathBench](https://mlsanity.com/paper/2203.03092/) (2022)
- [Lazy Probabilistic Roadmaps Revisited](https://mlsanity.com/paper/2209.14471/) (2022)
- [Beyond Visibility Limits: A DRL-Based Navigation Strategy for Unexpected Obstacles](https://mlsanity.com/paper/2503.01127/) (2025)
- [APPLV: Adaptive Planner Parameter Learning from Vision-Language-Action Model](https://mlsanity.com/paper/2603.08862/) (2026)
- [MineXplore: An Open-Source Reinforcement Learning Exploration Benchmark for GNSS-Denied Underground Environment](https://mlsanity.com/paper/2606.04569/) (2026)
- [FlashNav: Training Deployable Robot Navigation Policies in Seconds](https://mlsanity.com/paper/2606.15846/) (2026)

## Abstract (arXiv)

Metric ground navigation addresses the problem of autonomously moving a robot from one point to another in an obstacle-occupied planar environment in a collision-free manner. It is one of the most fundamental capabilities of intelligent mobile robots. This paper presents a standardized testbed with a set of environments and metrics to benchmark difficulty of different scenarios and performance of different systems of metric ground navigation. Current benchmarks focus on individual components of mobile robot navigation, such as perception and state estimation, but the navigation performance as a whole is rarely measured in a systematic and standardized fashion. As a result, navigation systems are usually tested and compared in an ad hoc manner, such as in one or two manually chosen environments. The introduced benchmark provides a general testbed for ground robot navigation in a metric world. The Benchmark for Autonomous Robot Navigation (BARN) dataset includes 300 navigation environments, which are ordered by a set of difficulty metrics. Navigation performance can be tested and compared in those environments in a systematic and objective fashion. This benchmark can be used to predict navigation difficulty of a new environment, compare navigation systems, and potentially serve as a cost function and a curriculum for planning-based and learning-based navigation systems. We have published our dataset and the source code to generate datasets for different robot footprints at www.cs.utexas.edu/~xiao/BARN/BARN.html.