March 22 to March 24, 2021

Machine Learning for Mobile Robot Navigation in the Wild

at AAAI 2021 Spring Symposium Series

Due to AAAI SS21 conversion to virtual format, paper submission deadline is extended to January 15, 2021

Submission Site:

Important Dates

Paper Submission: November 8, 2020 January 15, 2021

Acceptance Notification: February 14, 2021

Online Registration Open: December 17, 2020

Final Registration Deadline: March 5, 2021

Spring Symposium: March 22- 24, 2021

A 2.5-day symposium of talks, presentations, breakout sessions, and panel discussions, with actual mobile robots!

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.


Navigation in Unstructured Environments

Day 1

Navigation in Social Contexts with Other Human or Robotic Agents

Day 2

Mobile Robot Navigation: Applications

Day 3


Xuesu Xiao

University of Texas at Austin

Harel Yedidsion

University of Texas at Austin

Reuth Mirsky

University of Texas at Austin

Justin Hart

University of Texas at Austin

Peter Stone

University of Texas at Austin

Ross Knepper

Cornell University

Hao Zhang

Colorado School of Mines

Jean Oh

Carnegie Mellon University

Davide Scaramuzza

ETH Zurich

Vaibhav Unhelkar

Rice University

Michael Everett


Gregory Dudek

McGill University

Invited Speakers

Pratap Tokekar

University of Maryland

Srikanth Saripalli

Texas A&M University

Chris Mavrogiannis

University of Washington

Ji Zhang

Carnegie Mellon Univ.

Laura Herlant


Aaron Steinfeld

Carnegie Mellon Univ.

Industrial Participants


Intro Video

Independent Robotics

Intro Video

HEBI Robotics

Intro Video

Clearpath Robotics

Intro Video