(Deadline extended to November 8, 2020, submission site:
**https://easychair.org/conferences/?conf=sss21
<https://sites.google.com/utexas.edu/ml4nav/>**)
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
March 22-24 at Stanford University in Palo Alto, California, USA. We are
also seeking related contributions in the form of *six-page* full paper
and *two-page* abstract, and industrial participants. The submission
deadline is November 1. AAAI EasyChair site link will be posted soon.
For details, please see the following Call for Participation
(https://docs.google.com/document/d/1WHmfNDilpvVieK1JbaTDy6SCNbmuREdQN7YEZu3OU64/edit?usp=sharing):
*
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 aboutwhy, 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/
**
*Submission URL: https://easychair.org/conferences/?conf=sss21
<https://sites.google.com/utexas.edu/ml4nav/>*
**
Organizing Committee:
Xuesu Xiao (Symposium Chair), The University of Texas at Austin, Email:
x...@cs.utexas.edu <mailto:x...@cs.utexas.edu>
Harel Yedidsion, The University of Texas at Austin, Email:
ha...@cs.utexas.edu <mailto:ha...@cs.utexas.edu>
Reuth Mirsky, The University of Texas at Austin, Email:
re...@cs.utexas.edu <mailto:ha...@cs.utexas.edu>
Justin Hart, The University of Texas at Austin, Email:
h...@cs.utexas.edu <mailto:ha...@cs.utexas.edu>
Peter Stone, The University of Texas at Austin, Sony AI, Email:
pst...@cs.utexas.edu <mailto:ha...@cs.utexas.edu>
Ross Knepper, Cornell University, Email: ross.knep...@gmail.com
<mailto:ross.knep...@gmail.com>
Hao Zhang, Colorado School of Mines, Email: hzh...@mines.edu
<mailto:hzh...@mines.edu>
Jean Oh, Carnegie Mellon University, Email: jea...@cmu.edu
<mailto:jea...@nrec.ri.cmu.edu>
Davide Scaramuzza, University of Zurich, ETH Zurich, Email:
sdav...@ifi.uzh.ch <mailto:sdav...@ifi.uzh.ch>
Vaibhav Unhelkar, Rice University, Email: vaibhav.unhel...@rice.edu
<mailto:vaibhav.unhel...@rice.edu>
**
*Michael Everett, Massachusetts Institute of Technology, Email:
m...@mit.edu <mailto:vaibhav.unhel...@rice.edu>*
*
****
**Gregory Dudek, McGill University, Email: du...@cim.mcgill.ca
<mailto:vaibhav.unhel...@rice.edu>**
**** *
Submission Instructions:
Full papers of up to sixpages and abstract papers of up to twopages are
sought in the following topic areas:
*
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
All contributions should be submitted electronically via AAAI EasyChair
site.
Submission deadline is November 1st, 2020.
We also welcome participation of industrial partners, who are encouraged
to bring their mobile robots to the site and share their research and
engineering expertise with all participants of the symposium. For
potential industrial partners, please reach out to the organizing
committee for more details.
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
x...@cs.utexas.edu <mailto:x...@cs.utexas.edu>
https://www.cs.utexas.edu/~xiao/
Thanks
Xuesu
Xuesu Xiao, PhD
Department of Computer Science
The University of Texas at Austin
2317 Speedway, Austin, Texas 78712-1757 USA
+1 (512) 471-9765
x...@cs.utexas.edu
https://www.cs.utexas.edu/~xiao/
*
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