[agents] AAAI Symposium on Machine Learning for Mobile Robot Navigation in the Wild

[Xuesu Xiao]

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/


Organizing Committee:

Xuesu Xiao (Symposium Chair), The University of Texas at Austin, Email: 
xiao at cs.utexas.edu <mailto:xiao at cs.utexas.edu>

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

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

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

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

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

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

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

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

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

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

xiao at cs.utexas.edu

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




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