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</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, <br><br>we would like to invite you to participate in our AAAI Spring Symposium <b><i>Machine Learning for Mobile Robot Navigation in the Wild</i></b> (<a href="https://sites.google.com/utexas.edu/ml4nav/">https://sites.google.com/utexas.edu/ml4nav/</a>), which will take place virtually on March 22-24: <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>Day 1 (March 22) Navigation in Unstructured Environments: <a href="https://zoom.us/j/7376724470">https://zoom.us/j/7376724470</a><o:p></o:p></p><p class=MsoNormal>Day 2 (March 23) Navigation in Social Contexts with Other Human or Robotic Agents: <a href="https://zoom.us/j/7376724470">https://zoom.us/j/7376724470</a><o:p></o:p></p><p class=MsoNormal>Day 3 (March 24) Mobile Robot Navigation: Applications: <a href="https://zoom.us/j/7376724470">https://zoom.us/j/7376724470</a><o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>Detailed Schedule: <a href="https://sites.google.com/utexas.edu/ml4nav/schedule">https://sites.google.com/utexas.edu/ml4nav/schedule</a><o:p></o:p></p><p class=MsoNormal>Online Proceedings: <a href="https://sites.google.com/utexas.edu/ml4nav/proceedings">https://sites.google.com/utexas.edu/ml4nav/proceedings</a><o:p></o:p></p><p class=MsoNormal>Invited Speakers: Pratap Tokekar (University of Maryland), Srikanth Saripalli (Texas A&M University), Chris Mavrogiannis (University of Washington), Ji Zhang (Carnegie Mellon University), Laura Herlant (iRobot), Aaron Steinfeld (Carnegie Mellon University)<o:p></o:p></p><p class=MsoNormal>Industrial Partners: Hydronalix, Independent Robotics, HEBI Robotics, Bosch, iRobot, Clearpath Robotics<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>Please see detailed CfP below: <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p align=center style='margin:0in;text-align:center'><b><span style='font-family:"Arial",sans-serif;color:black'>Call for Participation</span></b><o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p style='margin:0in'><span style='font-family:"Arial",sans-serif;color:black'>The Machine Learning for Mobile Robot Navigation in the Wild Symposium in AAAI 2021 SSS will take place March 22-24 at Stanford University in Palo Alto, California, USA. The 2.5-day symposium will consist of invited talks, technical presentations, spotlight posters, robot demonstrations, industry spotlights, breakout sessions, and interactive panel discussions. </span><o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p style='margin:0in'><span style='font-family:"Arial",sans-serif;color:black'>Decades of research efforts have enabled classical navigation systems to move robots from one point to another, observing system and environmental constraints. However, navigation outside a controlled test environment, i.e., navigation in the wild, remains a challenging problem: an extensive amount of engineering is necessary to enable robust navigation in a wide variety of environments, e.g., to calibrate perception or to fine-tune navigational parameters; classical map-based navigation is usually treated as a pure geometric problem, without considering other sources of information, e.g., terrain, risk, social norms, etc. </span><o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p style='margin:0in'><span style='font-family:"Arial",sans-serif;color:black'>On the other hand, advancements in machine learning provide an alternative avenue to develop navigation systems, and arguably an “easier” way to achieve navigation in the wild. Vision input, semantic information, terrain stability, social compliance, etc. have become new modalities of world representations to be learned for navigation beyond pure geometry. Learned navigation systems can also largely reduce engineering effort in developing and tuning classical techniques. However, despite the extensive application of machine learning techniques on navigation problems, it still remains a challenge to deploy mobile robots in the wild in a safe, reliable, and trustworthy manner. </span><o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p style='margin:0in'><span style='font-family:"Arial",sans-serif;color:black'>In this symposium, we focus on navigation in the wild as opposed to navigation in a controlled, well-engineered, sterile environment like labs or factories. In the wild, mobile robots may face a variety of real-world scenarios, other robot or human companions, challenging terrain types, unstructured or confined environments, etc. This symposium aims at bringing together researchers who are interested in using machine learning to enable mobile robot navigation in the wild and to provide a shared platform to discuss learning fundamental navigation (sub)problems, despite different application scenarios. Through this symposium, we want to answer questions about<b> why, where, and how to apply machine learning for navigation in the wild</b>, summarize lessons learned, identify open questions, and point out future research directions. </span><o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p style='margin:0in'><b><span style='font-family:"Arial",sans-serif;color:black'>Symposium URL: </span></b><a href="https://sites.google.com/utexas.edu/ml4nav/"><b><span style='font-family:"Arial",sans-serif;color:#1155CC'>https://sites.google.com/utexas.edu/ml4nav/</span></b></a><o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p style='margin:0in'><b><span style='font-family:"Arial",sans-serif;color:black'>Organizing Committee:</span></b><o:p></o:p></p><p style='mso-margin-top-alt:12.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in'><b><span style='font-family:"Arial",sans-serif;color:black'>Xuesu Xiao </span></b><span style='font-family:"Arial",sans-serif;color:black'>(Symposium Chair), The University of Texas at Austin, Email: </span><a href="mailto:xiao@cs.utexas.edu"><span style='font-size:11.5pt;font-family:"Arial",sans-serif;color:#1155CC;background:#F8F8F8'>xiao@cs.utexas.edu</span></a><o:p></o:p></p><p style='mso-margin-top-alt:12.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in'><b><span style='font-family:"Arial",sans-serif;color:black'>Harel Yedidsion</span></b><span style='font-family:"Arial",sans-serif;color:black'>, The University of Texas at Austin, Email: </span><a href="mailto:harel@cs.utexas.edu"><span style='font-size:11.5pt;font-family:"Arial",sans-serif;color:#1155CC;background:#F8F8F8'>harel@cs.utexas.edu</span></a><o:p></o:p></p><p style='mso-margin-top-alt:12.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in'><b><span style='font-family:"Arial",sans-serif;color:black'>Reuth Mirsky</span></b><span style='font-family:"Arial",sans-serif;color:black'>, The University of Texas at Austin, Email: </span><a href="mailto:harel@cs.utexas.edu"><span style='font-size:11.5pt;font-family:"Arial",sans-serif;color:#1155CC;background:#F8F8F8'>reuth@cs.utexas.edu</span></a><o:p></o:p></p><p style='mso-margin-top-alt:12.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in'><b><span style='font-family:"Arial",sans-serif;color:black'>Justin Hart</span></b><span style='font-family:"Arial",sans-serif;color:black'>, The University of Texas at Austin, Email: </span><a href="mailto:harel@cs.utexas.edu"><span style='font-size:11.5pt;font-family:"Arial",sans-serif;color:#1155CC;background:#F8F8F8'>hart@cs.utexas.edu</span></a><o:p></o:p></p><p style='mso-margin-top-alt:12.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in'><b><span style='font-family:"Arial",sans-serif;color:black'>Peter Stone</span></b><span style='font-family:"Arial",sans-serif;color:black'>, The University of Texas at Austin, Sony AI, Email: </span><a href="mailto:harel@cs.utexas.edu"><span style='font-size:11.5pt;font-family:"Arial",sans-serif;color:#1155CC;background:#F8F8F8'>pstone@cs.utexas.edu</span></a><o:p></o:p></p><p style='mso-margin-top-alt:12.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in'><b><span style='font-family:"Arial",sans-serif;color:black'>Ross Knepper</span></b><span style='font-family:"Arial",sans-serif;color:black'>, Cornell University, Email: </span><a href="mailto:ross.knepper@gmail.com"><span style='font-size:11.5pt;font-family:"Arial",sans-serif;color:#1155CC;background:#F8F8F8'>ross.knepper@gmail.com</span></a><o:p></o:p></p><p style='mso-margin-top-alt:12.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in'><b><span style='font-family:"Arial",sans-serif;color:black'>Hao Zhang</span></b><span style='font-family:"Arial",sans-serif;color:black'>, Colorado School of Mines, Email: </span><a href="mailto:hzhang@mines.edu"><span style='font-size:11.5pt;font-family:"Arial",sans-serif;color:#1155CC;background:#F8F8F8'>hzhang@mines.edu</span></a><o:p></o:p></p><p style='mso-margin-top-alt:12.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in'><b><span style='font-family:"Arial",sans-serif;color:black'>Jean Oh</span></b><span style='font-family:"Arial",sans-serif;color:black'>, Carnegie Mellon University, Email: </span><a href="mailto:jeanoh@nrec.ri.cmu.edu"><span style='font-size:11.5pt;font-family:"Arial",sans-serif;color:#1155CC;background:#F8F8F8'>jeanoh@cmu.edu</span></a><o:p></o:p></p><p style='mso-margin-top-alt:12.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in'><b><span style='font-family:"Arial",sans-serif;color:black'>Davide Scaramuzza</span></b><span style='font-family:"Arial",sans-serif;color:black'>, University of Zurich, ETH Zurich, Email: </span><a href="mailto:sdavide@ifi.uzh.ch"><span style='font-size:11.5pt;font-family:"Arial",sans-serif;color:#1155CC;background:#F8F8F8'>sdavide@ifi.uzh.ch</span></a><o:p></o:p></p><p style='mso-margin-top-alt:12.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in'><b><span style='font-family:"Arial",sans-serif;color:black'>Vaibhav Unhelkar</span></b><span style='font-family:"Arial",sans-serif;color:black'>, Rice University, Email: </span><a href="mailto:vaibhav.unhelkar@rice.edu"><span style='font-family:"Arial",sans-serif;color:#1155CC'>vaibhav.unhelkar@rice.edu</span></a><o:p></o:p></p><p style='mso-margin-top-alt:12.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in'><b id=docs-internal-guid-11fec789-7fff-76e1-a143-e6f90febdd6f><span style='font-family:"Arial",sans-serif;color:black'>Michael Everett</span></b><span style='font-family:"Arial",sans-serif;color:black'>, Massachusetts Institute of Technology, Email: </span><a href="mailto:vaibhav.unhelkar@rice.edu"><span style='font-family:"Arial",sans-serif;color:#1155CC'>mfe@mit.edu</span></a><o:p></o:p></p><p style='mso-margin-top-alt:12.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in'><b id=docs-internal-guid-11fec789-7fff-76e1-a143-e6f90febdd6f><span style='font-family:"Arial",sans-serif;color:black'>Gregory Dudek</span></b><span style='font-family:"Arial",sans-serif;color:black'>, McGill University, Email: </span><a href="mailto:vaibhav.unhelkar@rice.edu"><span style='font-family:"Arial",sans-serif;color:#1155CC'>dudek@cim.mcgill.ca</span></a><o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p style='margin:0in'><span style='font-family:"Arial",sans-serif;color:black'>Topics of Interest: </span><o:p></o:p></p><ul style='margin-top:0in' type=disc><li class=MsoNormal style='color:black;mso-list:l0 level1 lfo1;vertical-align:baseline'><span style='font-family:"Arial",sans-serif'>Learning for social navigation<o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-list:l0 level1 lfo1;vertical-align:baseline'><span style='font-family:"Arial",sans-serif'>Learning for terrain-based navigation<o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-list:l0 level1 lfo1;vertical-align:baseline'><span style='font-family:"Arial",sans-serif'>Learning for vision-based navigation<o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-list:l0 level1 lfo1;vertical-align:baseline'><span style='font-family:"Arial",sans-serif'>Learning for interactive navigation<o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-list:l0 level1 lfo1;vertical-align:baseline'><span style='font-family:"Arial",sans-serif'>Representation learning for navigation <o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-list:l0 level1 lfo1;vertical-align:baseline'><span style='font-family:"Arial",sans-serif'>Sim2real for navigation<o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-list:l0 level1 lfo1;vertical-align:baseline'><span style='font-family:"Arial",sans-serif'>Zero-shot path planning<o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-list:l0 level1 lfo1;vertical-align:baseline'><span style='font-family:"Arial",sans-serif'>Learning for navigation in unstructured or confined environments<o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-list:l0 level1 lfo1;vertical-align:baseline'><span style='font-family:"Arial",sans-serif'>Reinforcement learning for navigation in the wild<o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-list:l0 level1 lfo1;vertical-align:baseline'><span style='font-family:"Arial",sans-serif'>Imitation learning for navigation in the wild<o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-list:l0 level1 lfo1;vertical-align:baseline'><span style='font-family:"Arial",sans-serif;color:#212121'>Active learning for </span><span style='font-family:"Arial",sans-serif'>navigation in the wild<o:p></o:p></span></li><li class=MsoNormal style='color:#212121;mso-list:l0 level1 lfo1;vertical-align:baseline'><span style='font-family:"Arial",sans-serif'>Lifelong/continual learning for navigation in the wild<o:p></o:p></span></li><li class=MsoNormal style='color:#212121;mso-list:l0 level1 lfo1;vertical-align:baseline'><span style='font-family:"Arial",sans-serif;color:black'>Geometric methods for learning navigation </span><span style='font-family:"Arial",sans-serif'><o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-list:l0 level1 lfo1;vertical-align:baseline'><span style='font-family:"Arial",sans-serif'>Real-world validation of learning for navigation<o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-list:l0 level1 lfo1;vertical-align:baseline'><span style='font-family:"Arial",sans-serif'>Navigation problems, benchmarks, and metric<o:p></o:p></span></li></ul><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal><o:p> </o:p></p><p style='margin:0in'><span style='font-family:"Arial",sans-serif;color:black'>For questions, please contact the Symposium Chair</span><o:p></o:p></p><p style='margin:0in'><span style='font-family:"Arial",sans-serif;color:black'>Dr. Xuesu Xiao, </span><o:p></o:p></p><p style='margin:0in'><span style='font-family:"Arial",sans-serif;color:black'>Department of Computer Science</span><o:p></o:p></p><p style='margin:0in'><span style='font-family:"Arial",sans-serif;color:black'>The University of Texas at Austin</span><o:p></o:p></p><p style='margin:0in'><span style='font-family:"Arial",sans-serif;color:black'>2317 Speedway, Austin, Texas 78712-1757 USA</span><o:p></o:p></p><p style='margin:0in'><span style='font-family:"Arial",sans-serif;color:black'>+1 (512) 471-9765</span><o:p></o:p></p><p style='margin:0in'><a href="mailto:xiao@cs.utexas.edu"><span style='font-family:"Arial",sans-serif;color:#1155CC'>xiao@cs.utexas.edu</span></a><o:p></o:p></p><p style='margin:0in'><a href="https://www.cs.utexas.edu/~xiao/"><span style='font-family:"Arial",sans-serif;color:#1155CC'>https://www.cs.utexas.edu/~xiao/</span></a><o:p></o:p></p><p style='margin:0in'><o:p> </o:p></p><p style='margin:0in'>Thanks<o:p></o:p></p><p style='margin:0in'>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>Postdoctoral Researcher<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>GDC 3.418 +1 (512) 471-9765<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><p class=MsoNormal><o:p> </o:p></p></div></body></html>