[agents] [meetings] Final CfP: AAAI Symposium on Machine Learning for Mobile Robot Navigation in the Wild

Sat Mar 20 12:34:59 EDT 2021

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

Harel Yedidsion, The University of Texas at Austin, Email:
<mailto:harel at cs.utexas.edu> harel at cs.utexas.edu

Reuth Mirsky, The University of Texas at Austin, Email:
<mailto:harel at cs.utexas.edu> reuth at cs.utexas.edu

Justin Hart, The University of Texas at Austin, Email:
<mailto:harel at cs.utexas.edu> hart at cs.utexas.edu

Peter Stone, The University of Texas at Austin, Sony AI, Email:
<mailto:harel at cs.utexas.edu> pstone at cs.utexas.edu

Ross Knepper, Cornell University, Email:  <mailto:ross.knepper at gmail.com>
ross.knepper at gmail.com

Hao Zhang, Colorado School of Mines, Email:  <mailto:hzhang at mines.edu>
hzhang at mines.edu

Jean Oh, Carnegie Mellon University, Email:  <mailto:jeanoh at nrec.ri.cmu.edu>
jeanoh at cmu.edu

Davide Scaramuzza, University of Zurich, ETH Zurich, Email:
<mailto:sdavide at ifi.uzh.ch> sdavide at ifi.uzh.ch

Vaibhav Unhelkar, Rice University, Email:
<mailto:vaibhav.unhelkar at rice.edu> vaibhav.unhelkar at rice.edu

Michael Everett, Massachusetts Institute of Technology, Email:
<mailto:vaibhav.unhelkar at rice.edu> mfe at mit.edu

Gregory Dudek, McGill University, Email:  <mailto:vaibhav.unhelkar at rice.edu>
dudek at 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:xiao at cs.utexas.edu> xiao at 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

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

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

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