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Autonomous Ground Navigation in Highly Constrained Spaces: Lessons Learned From the Third BARN Challenge at ICRA 2024 [Competitions].
Xuesu Xiao, Zifan Xu, Aniket Datar, Garrett Warnell, Peter
Stone, Joshua Julian Damanik, Jaewon Jung, Chala Adane Deresa, Than
Duc Huy, Chen Jinyu, Chen Yichen, Joshua Adrian Cahyono, Jingda Wu, Longfei Mo, Mingyang Lv, Bowen Lan, Qingyang Meng, Weizhi
Tao, and Li Cheng.
IEEE Robotics \& Automation Magazine, 2024.
The third Benchmark Autonomous Robot Navigation (BARN) Challenge took place atthe 2024 IEEE International Conference on Robotics and Automation (ICRA 2024) inYokohama, Japan and continued to evaluate the performance of state-of-the-artautonomous ground navigation systems in highly constrained environments. Similarto the trend in the first and second BARN Challenges at ICRA 2022 and 2023 inPhiladelphia (North America) and London (Europe), the third BARN Challenge inYokohama (Asia) became more regional, i.e., mostly Asian teams participated. Thesize of the competition has slightly shrunk (six simulation teams, four of whichwere invited to the physical competition). The competition results, compared tothe last two years, suggest that the field has adopted new machine learningapproaches, while at the same time slightly converged to a few common practices.However, the regional nature of the physical participants suggests a challenge topromote wider participation all over the world and provide more resources totravel to the venue. In this article, we discuss the challenge, the approachesused by the three winning teams, and lessons learned to direct future researchand competitions.
@Article{xuesu_xiao_ram_2024,
author = {Xuesu Xiao and Zifan Xu and Aniket Datar and Garrett Warnell and Peter Stone and Joshua Julian Damanik and Jaewon Jung and Chala Adane Deresa and Than Duc Huy and Chen Jinyu and Chen Yichen and Joshua Adrian Cahyono and Jingda Wu and Longfei Mo and Mingyang Lv and Bowen Lan and Qingyang Meng and Weizhi Tao and Li Cheng},
title = {Autonomous Ground Navigation in Highly Constrained Spaces: Lessons Learned From the Third {BARN} Challenge at {ICRA} 2024 [Competitions]},
journal = {{IEEE} Robotics \& Automation Magazine},
year = {2024},
abstract = {The third Benchmark Autonomous Robot Navigation (BARN) Challenge took place at
the 2024 IEEE International Conference on Robotics and Automation (ICRA 2024) in
Yokohama, Japan and continued to evaluate the performance of state-of-the-art
autonomous ground navigation systems in highly constrained environments. Similar
to the trend in the first and second BARN Challenges at ICRA 2022 and 2023 in
Philadelphia (North America) and London (Europe), the third BARN Challenge in
Yokohama (Asia) became more regional, i.e., mostly Asian teams participated. The
size of the competition has slightly shrunk (six simulation teams, four of which
were invited to the physical competition). The competition results, compared to
the last two years, suggest that the field has adopted new machine learning
approaches, while at the same time slightly converged to a few common practices.
However, the regional nature of the physical participants suggests a challenge to
promote wider participation all over the world and provide more resources to
travel to the venue. In this article, we discuss the challenge, the approaches
used by the three winning teams, and lessons learned to direct future research
and competitions.
},
}
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