Perception and Control for Fast and Agile Super-Vehicles

July 12th, RSS 2020


All times are in PST

The workshop will be live streamed on YouTube.

7:00 - 7:05 Opening Remarks
7:05 - 7:30 Prof. Guido de Croon, TU Delft
“A computationally efficient approach to autonomous drone racing”
7:30 - 7:55 Prof. Marilyn Smith, Georgia Institute of Technology
“The Role of Unsteady Aerodynamics in Agile Super-Vehicles”
7:55 - 8:20 Teo Tomic, Skydio
“Skydio: Intelligent Flying Machines”
8:20 - 8:45 Dr. Ali-Agha Mohammadi, NASA Jet Propulsion Laboratory
“Vehicle Speed Versus Mission Speed in Off-road Extreme Environment Navigation”
8:45 - 9:10 Dr. Ashish Kapoor, Microsoft
“Toward drone race-worthy simulations”
9:10 - 9:35 Eric Cristofalo, Stanford
“Vision-based Perception and Control in Multi-robot Systems”
9:35 - 9:50 Phillip Foehn, University of Zurich
“Time-Optimal Trajectory Planning for Quadrotors”
9:50 - 10:05 Varun Murali, MIT
“Computationally efficient perception aware planning for high speed flight”
10:05 - 10:20 Prof. Walterio W. Mayol-Cuevas
“Autonomous drone racing using the SCAMP pixel processor array”
10:20 - 10:35 Miguel Fernandez-Cortizas and Carlos Redondo-Plaza,
Universidad Politecnica de Madrid, Spain
“Semantic VSLAM and short term trajectory generation for the AlphaPilot competition”
10:35 - 11:00 Panel


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 organised the first season of the AlphaPilot program ( and the AIRR drone racing challenge (, 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?


Workshop Organizers

Varun Murali (Contact Person)
Affiliation: Laboratory for Information and Decision Systems, Massachusetts Institute of Technology

Phillip Foehn
Affiliation: University of Zurich

Prof. Davide Scaramuzza
Affiliation: University of Zurich

Prof. Sertac Karaman
Affiliation:Laboratory for Information and Decision Systems, Massachusetts Institute of Technology