Please find below the call for papers to the RSS’20 Workshop titled
“Perception and Control for Fast and Agile Super-Vehicles” to be held on
the 12th of July as a fully virtual event.

We invite 2-page extended abstract submissions for original work in
perception and control for high speed navigation and topics of interest
to this workshop. Topics of interest to this workshop are (but not
limited to):

• Autonomous drone racing
• High speed localization and mapping
• Perception aware control and planning
• Trajectory optimization for aggressive flight
• Robust control for high speed flight
• Accurate simulation of highly agile and fast aerial vehicles

  Authors will have the opportunity to participate in a short invited
talk at the workshop.

** Important dates:
Abstract submission deadline: June 14th 2020
Acceptance Notification: June 21st 2020
Workshop date: July 12th 2020

Please email all submissions to super-vehicles-rss20-submit@mit.edu with
‘RSS20 Super Vehicles’ in the subject line.

** Abstract for the workshop
As autonomous aerial vehicles not only become more robust and capable,
but also are slowly being adopted in many industrial tasks, a novel
branch of autonomy has recently caught the interest of many researchers:
autonomous drone racing. Not only does it combine the difficulties in
perception, estimation, planning, control, and their intersections, but
it also tests their ability to perform under harsh, real-world conditions.
Expert human pilots have demonstrated an astonishing level of control,
racing remotely controlled drones at their physical limits, and
inspiring roboticists to push the algorithmic limits to a
human-competitive level. As advances in algorithmic perception and
control for fast and agile robotic vehicles materialize, autonomous
racing vehicles are quickly approaching the ability to contend against
human pilots in head to head races. Most recently, Lockheed Martin,
NVIDIA and the Drone Racing League (DRL) successfully organized the
first season of the AlphaPilot program
(https://www.herox.com/alphapilot) and the AIRR drone racing challenge
(https://thedroneracingleague.com/airr/), where multiple teams have
successfully deployed and raced their autonomy algorithms against each
other. These advances may ultimately lead to autonomous super-vehicles,
i.e., next-generation autonomous robots that are capable of achieving
super-human maneuvering and racing capabilities. The resulting
algorithms may become invaluable components of high-throughput autonomy
software, e.g., to maneuver cars out of traffic accidents. However, the
development of these super-vehicles brings significant challenges. While
perceiving the environment at high speeds with low latency has been
investigated throughout the last decade, many open research questions
still remain. On the other side, time-optimal planning with well known
or learned dynamic and aerodynamic models could give autonomous drones
an advantage over human pilots, or let them learn from each other. The
purpose of this workshop is to identify gaps in current techniques, and
discuss possible solutions to the remaining and newly uncovered research
questions.. Is end to end deep learning a viable option to solve these
high speed interactions? What can we model, what can we learn, and could
we combine these techniques to achieve superhuman capabilities? What are
the transfer gaps between simulation, learning and real world systems,
and how can we bridge them to achieve truly superior autonomous mobile
robots?

Organizers:
Varun Murali, MIT
Phillip Foehn, UZH
Prof. Davide Scaramuzza, UZH
Prof. Sertac Karaman, MIT