<html xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:w="urn:schemas-microsoft-com:office:word" xmlns:m="http://schemas.microsoft.com/office/2004/12/omml" xmlns="http://www.w3.org/TR/REC-html40"><head><META HTTP-EQUIV="Content-Type" CONTENT="text/html; charset=us-ascii"><meta name=Generator content="Microsoft Word 15 (filtered medium)"><style><!--
/* Font Definitions */
@font-face
{font-family:"Cambria Math";
panose-1:2 4 5 3 5 4 6 3 2 4;}
@font-face
{font-family:Calibri;
panose-1:2 15 5 2 2 2 4 3 2 4;}
/* Style Definitions */
p.MsoNormal, li.MsoNormal, div.MsoNormal
{margin:0in;
font-size:11.0pt;
font-family:"Calibri",sans-serif;}
a:link, span.MsoHyperlink
{mso-style-priority:99;
color:#0563C1;
text-decoration:underline;}
span.EmailStyle17
{mso-style-type:personal-compose;
font-family:"Calibri",sans-serif;
color:windowtext;}
.MsoChpDefault
{mso-style-type:export-only;
font-family:"Calibri",sans-serif;}
@page WordSection1
{size:8.5in 11.0in;
margin:1.0in 1.0in 1.0in 1.0in;}
div.WordSection1
{page:WordSection1;}
--></style><!--[if gte mso 9]><xml>
<o:shapedefaults v:ext="edit" spidmax="1026" />
</xml><![endif]--><!--[if gte mso 9]><xml>
<o:shapelayout v:ext="edit">
<o:idmap v:ext="edit" data="1" />
</o:shapelayout></xml><![endif]--></head><body lang=EN-US link="#0563C1" vlink="#954F72" style='word-wrap:break-word'><div class=WordSection1><p class=MsoNormal>Dear roboticists, <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>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? <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>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. <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>1. The competition will be designing ground navigation systems to navigate through all 300 BARN environments (<a href="https://www.cs.utexas.edu/~xiao/BARN/BARN.html">https://www.cs.utexas.edu/~xiao/BARN/BARN.html</a>) as fast as possible without collision in simulation. <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>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. <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>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. <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>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. <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>5. The team who achieves the fastest navigation in the 100 evaluation environments wins. Standardized metrics/scoring system will be provided in advance. <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>6. If ICRA21 will be a physical conference, we will provide a physical Jackal with the specified sensor and actuator at Philadelphia and to set up physical obstacle course in the venue. We will invite the top five teams in simulation to compete in the real-world. <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>If you are (potentially) interested in participating (no commitment required), please leave your information at <o:p></o:p></p><p class=MsoNormal><a href="https://docs.google.com/forms/d/e/1FAIpQLSdJ6cUMHn8tQDNNkOistlpSmkS5jFt3-Xz6oh1FCMzRgxpX_g/viewform?usp=sf_link">https://docs.google.com/forms/d/e/1FAIpQLSdJ6cUMHn8tQDNNkOistlpSmkS5jFt3-Xz6oh1FCMzRgxpX_g/viewform?usp=sf_link</a><o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>Co-Organizers: <o:p></o:p></p><p class=MsoNormal>Xuesu Xiao (UT Austin/Everyday Robots/GMU), Zifan Xu (UT Austin), Yunlong Song (University of Zurich), Garrett Warnell (US Army Research Lab), Peter Stone (UT Austin/Sony AI)<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>Thanks<o:p></o:p></p><p class=MsoNormal>Xuesu<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>-----------------------<o:p></o:p></p><p class=MsoNormal>Xuesu Xiao, Ph.D.<o:p></o:p></p><p class=MsoNormal>--<o:p></o:p></p><p class=MsoNormal>Incoming Assistant Professor (Fall 2022)<o:p></o:p></p><p class=MsoNormal>Department of Computer Science<o:p></o:p></p><p class=MsoNormal>George Mason University<o:p></o:p></p><p class=MsoNormal>--<o:p></o:p></p><p class=MsoNormal>Roboticist, The Everyday Robot Project<o:p></o:p></p><p class=MsoNormal>X, The Moonshot Factory<o:p></o:p></p><p class=MsoNormal><a href="mailto:xuesuxiao@google.com">xuesuxiao@google.com</a><o:p></o:p></p><p class=MsoNormal><a href="https://x.company/projects/everyday-robots/">https://x.company/projects/everyday-robots/</a><o:p></o:p></p><p class=MsoNormal>--<o:p></o:p></p><p class=MsoNormal>Research Affiliate<o:p></o:p></p><p class=MsoNormal>Department of Computer Science<o:p></o:p></p><p class=MsoNormal>The University of Texas at Austin<o:p></o:p></p><p class=MsoNormal><a href="mailto:xiao@cs.utexas.edu">xiao@cs.utexas.edu</a><o:p></o:p></p><p class=MsoNormal><a href="https://www.cs.utexas.edu/~xiao/">https://www.cs.utexas.edu/~xiao/</a><o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal><o:p> </o:p></p></div></body></html>