<html aria-label="message body"><head><meta http-equiv="content-type" content="text/html; charset=utf-8"></head><body style="overflow-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><div>Now that the IROS 2026 deadline is behind us, it’s time to gear up for The BARN Challenge! </div><div><br></div>Submission Form: <a href="https://docs.google.com/forms/d/e/1FAIpQLSeIwC6GY_mAl1J18G1uZOaAn-OCONmmtaP_HRAtxkaeM72ufw/viewform">https://docs.google.com/forms/d/e/1FAIpQLSeIwC6GY_mAl1J18G1uZOaAn-OCONmmtaP_HRAtxkaeM72ufw/viewform</a><br><br>Competition Website: <a href="https://people.cs.gmu.edu/~xiao/Research/BARN_Challenge/BARN_Challenge26.html">https://people.cs.gmu.edu/~xiao/Research/BARN_Challenge/BARN_Challenge26.html</a><br><br>ROS Participation Instructions: <a href="https://github.com/Daffan/the-barn-challenge">https://github.com/Daffan/the-barn-challenge</a><br><br>ROS2 Participation Instructions: <a href="https://github.com/Saadmaghani/The-Barn-Challenge-Ros2">https://github.com/Saadmaghani/The-Barn-Challenge-Ros2</a><br><br>Lessons Learned from The BARN Challenge 2025: <a href="https://people.cs.gmu.edu/~xiao/papers/barn25_report.pdf">https://people.cs.gmu.edu/~xiao/papers/barn25_report.pdf</a><br><br>Lessons Learned from The BARN Challenge 2024: <a href="https://people.cs.gmu.edu/~xiao/papers/barn24_report.pdf">https://people.cs.gmu.edu/~xiao/papers/barn24_report.pdf</a><br><br>Lessons Learned from The BARN Challenge 2023: <a href="https://people.cs.gmu.edu/~xiao/papers/barn23_report.pdf">https://people.cs.gmu.edu/~xiao/papers/barn23_report.pdf</a><br><br>Lessons Learned from The BARN Challenge 2022: <a href="https://people.cs.gmu.edu/~xiao/papers/barn22_report.pdf">https://people.cs.gmu.edu/~xiao/papers/barn22_report.pdf</a><br><br><br>Dear roboticists,<br><br>are you interested in agile robot navigation in highly constrained spaces with a lot of obstacles around, e.g., cluttered households or after-disaster scenarios? Do you think mobile robot navigation is mostly a solved problem? Are you looking for a hands-on project for your robotics class, but may not have (sufficient) robot platforms for your students?<br><br>If your answer is yes to any of the above questions, we sincerely invite you to participate in The 5th BARN Challenge at ICRA 2026 (<a href="https://people.cs.gmu.edu/~xiao/Research/BARN_Challenge/BARN_Challenge26.html">https://people.cs.gmu.edu/~xiao/Research/BARN_Challenge/BARN_Challenge26.html</a>)! The BARN Challenge aims at evaluating state-of-the-art autonomous navigation systems to move robots through highly constrained environments in a safe and efficient manner. The task is to navigate a standardized Clearpath Jackal robot from a predefined start to a goal location as quickly as possible without any collision. The challenge will take place both in the simulated BARN dataset and in physical obstacle courses at ICRA 2026.<br><br>1. The competition task is designing ground navigation systems to navigate through all 300 BARN environments (<a href="https://people.cs.gmu.edu/~xiao/Research/BARN/BARN.html">https://people.cs.gmu.edu/~xiao/Research/BARN/BARN.html</a>) and physical obstacle courses constructed at ICRA 2026 as fast as possible without collision.<br><br>2. The 300 BARN environments can be the training set for learning-based methods, or to design classical approaches in. During the simulation competition, we will generate another 50 unseen environments unavailable to the participants before the competition.<br><br>3. We will standardize a Jackal robot in the Gazebo simulation, including a Hokuyo 2D LiDAR, motor controller of 2m/s max speed, etc.<br><br>4. Participants can use any approaches to tackle the navigation problem, such as using classical sampling-based or optimization-based planners, end-to-end learning, or hybrid approaches. We will provide baselines for reference. <br><br>5. A standardized scoring system is provided on the website.<br><br>6. We will invite the top teams in simulation to compete in the real world. The team who achieves the fastest collision-free navigation in the physical obstacle courses wins.<br><br>If you are interested in participating, please submit your navigation system at <a href="https://docs.google.com/forms/d/e/1FAIpQLSeIwC6GY_mAl1J18G1uZOaAn-OCONmmtaP_HRAtxkaeM72ufw/viewform">https://docs.google.com/forms/d/e/1FAIpQLSeIwC6GY_mAl1J18G1uZOaAn-OCONmmtaP_HRAtxkaeM72ufw/viewform</a><br><br>Co-Organizers:<br>Xuesu Xiao (George Mason University)<br>Zifan Xu (UT Austin)<br>Saad Abdul Ghani (George Mason University)<br>Aniket Datar (George Mason University)<br>Daeun Song (Ewha Womnas University)<br>Peter Stone (UT Austin / Sony AI)<br><br>Sponsor:<br>Clearpath Robotics, <a href="https://clearpathrobotics.com/">https://clearpathrobotics.com/</a><br><br><br>Thanks<br>Xuesu<br><br><br>-----------------------<br>Xuesu Xiao, Ph.D.<br>--<br>Assistant Professor<br>Department of Computer Science<br>George Mason University<br><a href="mailto:xiao@gmu.edu">xiao@gmu.edu</a><br><a href="https://people.cs.gmu.edu/~xiao/">https://people.cs.gmu.edu/~xiao/</a><br><a href="https://robotixx.cs.gmu.edu/">https://robotixx.cs.gmu.edu/</a></body></html>