[agents] [meetings] [news] Benchmark for Autonomous Robot Navigation (BARN) Challenge -- Potential ICRA 2022 Competition

[Xuesu Xiao]

Dear roboticists, 

 

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? 

 

If your answer is yes to any of the above questions, we sincerely invite you
to participate in our BARN challenge! BARN challenge aims at developing fast
and agile ground navigation in highly constrained spaces, which remains to
be a very challenging research problem. 

 

1. The competition will be designing ground navigation systems to navigate
through all 300 BARN environments
(https://www.cs.utexas.edu/~xiao/BARN/BARN.html) as fast as possible without
collision in simulation. 

 

2. The 300 BARN environments can be the training set for learning-based
methods, or to design classical approaches in. During the competition, we
will generate another 100 unseen environments unavailable to the
participants before the competition. 

 

3. We will standardize a Jackal robot in the Gazebo simulation, including a
2D Hokuyo with 720-dim 270-degree field-of-view 2D LiDAR, max speed of 2m/s,
etc. 

 

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.  

 

5. The team who achieves the fastest navigation in the 100 evaluation
environments wins. Standardized metrics/scoring system will be provided in
advance. 

 

6. If ICRA21 will be a physical conference, Clearpath Robotics will provide
a physical Jackal with the specified sensor and actuator at Philadelphia and
we will set up physical obstacle courses in the venue. We will invite the
top five teams in simulation to compete in the real-world. 

 

If you are (potentially) interested in participating (no commitment
required), please leave your information at
https://docs.google.com/forms/d/e/1FAIpQLSdJ6cUMHn8tQDNNkOistlpSmkS5jFt3-Xz6
oh1FCMzRgxpX_g/viewform?usp=sf_link 

 

Co-Organizers: 

Xuesu Xiao (UT Austin/Everyday Robots/GMU), Zifan Xu (UT Austin), Yunlong
Song (University of Zurich)

Steering Committee: 

Garrett Warnell (US Army Research Lab), Peter Stone (UT Austin/Sony AI)

Confirmed Sponsor:

Clearpath Robotics, https://clearpathrobotics.com/

 

Thanks

Xuesu

 

-----------------------

Xuesu Xiao, Ph.D.

--

Incoming Assistant Professor (Fall 2022)

Department of Computer Science

George Mason University

--

Roboticist, The Everyday Robot Project

X, The Moonshot Factory

xuesuxiao at google.com <mailto:xuesuxiao at google.com> 

https://x.company/projects/everyday-robots/

--

Research Affiliate

Department of Computer Science

The University of Texas at Austin

xiao at cs.utexas.edu <mailto:xiao at cs.utexas.edu> 

https://www.cs.utexas.edu/~xiao/

 

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