A reminder of our first robotics talk today!
Date: Monday, March 5
Time: 11:00am - 12:00pm
Location: Galbraith (GB) 303
Speaker: Florian Shkurti, McGill University
Title: Enabling
robot videographers to record the visual footage that human experts
want
Abstract:
The adoption of robotics is becoming widespread in many sectors of
society, most notably in the contexts of automated transportation,
warehousing, and advanced manufacturing. Yet, for robots that
operate in more challenging and unstructured natural domains (e.g.
underwater, air, deserts, forests, lakes), where the promise of
automated environmental monitoring presents exciting
possibilities for societal progress, open research problems
still abound.
In this talk I will focus on the problem of enabling robot
videographers/documentarians that autonomously navigate in
unstructured 3D environments, alongside scientists, to help them
record visual footage that they deem valuable for their work.
I will present a method to infer the expert's reward function over
images, using a small number of labeled and a large number of
unlabeled examples. This reward function is used to guide the
robot's exploration and data collection in unknown environments. I
will also present vision-based algorithms for tracking and navigation
that are robust to long-term loss of visual contact with the
subject, by making use of the subject's learned behavior,
estimated via inverse reinforcement learning. Finally, I will
describe a visual and inertial localization and mapping method that
enables robust navigation in a wide range of challenging environments.
Experimental validation of these methods on underwater, aerial and
ground robots will be shown.
Bio:
Florian Shkurti is a Ph.D. candidate in computer science and
robotics at McGill University, working with Gregory Dudek. His
research is at the intersection of mobile robotics, computer vision,
and machine learning. His favorite research problems revolve around
increasing the autonomy of mobile robots, and include: inverse
reinforcement learning, imitation learning, the control of dynamical
systems under uncertainty and
partial observability,
visibility-aware multi-robot path planning, as well as robust
visual mapping and localization in 3D. He is a recipient
of the Lorne Trottier Fellowship, the AAAI-15 Robotics Fellowship,
and the NSERC Alexander Graham Bell CGS
Doctoral Award. He is also a member of the Center for Intelligent
Machines and the NSERC Canadian Field Robotics Network. He
did his M.Sc. in computer science at McGill, and his Hon. B.Sc. in
computer science and mathematics at the University of Toronto.