Autonomous Ground Navigation in Highly Constrained Spaces: Lessons learned from The BARN Challenge at ICRA 2022
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Abstract (arXiv)
The BARN (Benchmark Autonomous Robot Navigation) Challenge took place at the 2022 IEEE International Conference on Robotics and Automation (ICRA 2022) in Philadelphia, PA. The aim of the challenge was to evaluate state-of-the-art autonomous ground navigation systems for moving robots through highly constrained environments in a safe and efficient manner. Specifically, the task was to navigate a standardized, differential-drive ground robot from a predefined start location to a goal location as quickly as possible without colliding with any obstacles, both in simulation and in the real world. Five teams from all over the world participated in the qualifying simulation competition, three of which were invited to compete with each other at a set of physical obstacle courses at the conference center in Philadelphia. The competition results suggest that autonomous ground navigation in highly constrained spaces, despite seeming ostensibly simple even for experienced roboticists, is actually far from being a solved problem. In this article, we discuss the challenge, the approaches used by the top three winning teams, and lessons learned to direct future research.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Aug 2022.
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Citing papers per year: 2022: 2; 2023: 0; 2024: 1; 2025: 1.
4 papers.
- 2022Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigationcited by 29
- 2022Benchmarking Reinforcement Learning Techniques for Autonomous Navigationcited by 19
- 2024PACER: Preference-conditioned All-terrain Costmap Generationcited by 5
- 2025Beyond Visibility Limits: A DRL-Based Navigation Strategy for Unexpected Obstaclescited by 0
- 2025Beyond Visibility Limits: A DRL-Based Navigation Strategy for Unexpected Obstacles3 Mar 2025
- 2024PACER: Preference-conditioned All-terrain Costmap Generation30 Oct 2024
- 2022Benchmarking Reinforcement Learning Techniques for Autonomous Navigation10 Oct 2022
- 2022Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation22 Sep 2022