Dear Roboticists, we would like to invite you to participate in our AAAI Spring Symposium Machine Learning for Mobile Robot Navigation in the Wild (https://sites.google.com/utexas.edu/ml4nav/), which will take place virtually on March 22-24:
Day 1 (March 22) Navigation in Unstructured Environments: https://zoom.us/j/7376724470 Day 2 (March 23) Navigation in Social Contexts with Other Human or Robotic Agents: https://zoom.us/j/7376724470 Day 3 (March 24) Mobile Robot Navigation: Applications: https://zoom.us/j/7376724470 Detailed Schedule: https://sites.google.com/utexas.edu/ml4nav/schedule Online Proceedings: https://sites.google.com/utexas.edu/ml4nav/proceedings 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) Industrial Partners: Hydronalix, Independent Robotics, HEBI Robotics, Bosch, iRobot, Clearpath Robotics Please see detailed CfP below: Call for Participation 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. 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. 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. 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 why, where, and how to apply machine learning for navigation in the wild, summarize lessons learned, identify open questions, and point out future research directions. Symposium URL: <https://sites.google.com/utexas.edu/ml4nav/> https://sites.google.com/utexas.edu/ml4nav/ Organizing Committee: Xuesu Xiao (Symposium Chair), The University of Texas at Austin, Email: <mailto:x...@cs.utexas.edu> x...@cs.utexas.edu Harel Yedidsion, The University of Texas at Austin, Email: <mailto:ha...@cs.utexas.edu> ha...@cs.utexas.edu Reuth Mirsky, The University of Texas at Austin, Email: <mailto:ha...@cs.utexas.edu> re...@cs.utexas.edu Justin Hart, The University of Texas at Austin, Email: <mailto:ha...@cs.utexas.edu> h...@cs.utexas.edu Peter Stone, The University of Texas at Austin, Sony AI, Email: <mailto:ha...@cs.utexas.edu> pst...@cs.utexas.edu Ross Knepper, Cornell University, Email: <mailto:ross.knep...@gmail.com> ross.knep...@gmail.com Hao Zhang, Colorado School of Mines, Email: <mailto:hzh...@mines.edu> hzh...@mines.edu Jean Oh, Carnegie Mellon University, Email: <mailto:jea...@nrec.ri.cmu.edu> jea...@cmu.edu Davide Scaramuzza, University of Zurich, ETH Zurich, Email: <mailto:sdav...@ifi.uzh.ch> sdav...@ifi.uzh.ch Vaibhav Unhelkar, Rice University, Email: <mailto:vaibhav.unhel...@rice.edu> vaibhav.unhel...@rice.edu Michael Everett, Massachusetts Institute of Technology, Email: <mailto:vaibhav.unhel...@rice.edu> m...@mit.edu Gregory Dudek, McGill University, Email: <mailto:vaibhav.unhel...@rice.edu> du...@cim.mcgill.ca Topics of Interest: * Learning for social navigation * Learning for terrain-based navigation * Learning for vision-based navigation * Learning for interactive navigation * Representation learning for navigation * Sim2real for navigation * Zero-shot path planning * Learning for navigation in unstructured or confined environments * Reinforcement learning for navigation in the wild * Imitation learning for navigation in the wild * Active learning for navigation in the wild * Lifelong/continual learning for navigation in the wild * Geometric methods for learning navigation * Real-world validation of learning for navigation * Navigation problems, benchmarks, and metric For questions, please contact the Symposium Chair Dr. Xuesu Xiao, Department of Computer Science The University of Texas at Austin 2317 Speedway, Austin, Texas 78712-1757 USA +1 (512) 471-9765 <mailto:x...@cs.utexas.edu> x...@cs.utexas.edu <https://www.cs.utexas.edu/~xiao/> https://www.cs.utexas.edu/~xiao/ Thanks Xuesu -- Xuesu Xiao, Ph.D. Postdoctoral Researcher Department of Computer Science The University of Texas at Austin GDC 3.418 +1 (512) 471-9765 x...@cs.utexas.edu <mailto:x...@cs.utexas.edu> https://www.cs.utexas.edu/~xiao/
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