IEEE Robotics & Automation Magazine - March 2022 - 113

Striving to build ever-more intelligent
systems, Abbeel is paving the way to the
next generation of robots that can learn
and improve to function outside of traditional
manufacturing settings. Abbeel
helped start the field of robot learning by
cleverly combining learning and optimal
control for hard tasks, including helicopter
flight, manipulating cloth, and
surgical knot tying. He then applied deep
learning to robotics by incorporating
aspects of reinforcement, imitation, and
unsupervised learning to enable artificial
intelligence-powered robots that have
the potential to sense and react to their
environment and meaningfully reason
through the consequences of their
actions in real time. These robots are
now playing a role in warehouse order
fulfillment, logistics, and supply-chain
applications critical to e-commerce.
More About the IEEE Kiyo
Tomiyasu Award
The IEEE Kiyo Tomiyasu Award was
established in 2001 by the IEEE Board
of Directors and is presented for outstanding
early-to-midcareer contributions
to technologies holding the
promise of innovative applications.
The award consists of a bronze medal,
certificate, and cash honorarium. In
the evaluation process, the following
criteria are considered: innovation or
development, social value, uniqueness
of concept, other technical accomplishments,
and the quality of the
nomination.
2022 IEEE Robotics and Automation
Technical Field Award Recipient
Sponsored by the IEEE Robotics and
Automation Society (RAS).
W
e extend heartfelt con -
gratulations to Wolfram
Burgard (see Figure 1),
se lected recipient of the
prestigious 2022 IEEE Robotics and
Automation Award " for contributions to
the fundamentals and applications of
robot navigation and perception. " He
will receive this award during the 2022
IEEE International Conference on
Robotics and Automation in Philadelphia
on 23-27 May 2022.
A pioneer of robust and adaptive
techniques for robot perception, navigation,
and manipulation, Burgard
has addressed the most relevant challenges
facing autonomous mobile
robotics applications, ranging from
vacuum cleaners to self-driving cars.
Burgard led the original contributions
pertaining to simultaneous localization
and mapping (SLAM) and robot
learning, resulting in algorithms
including the grid-based FastSLAM.
His g2o tool is one of the most efficient
solvers for SLAM issues facing
Digital Object Identifier 10.1109/MRA.2022.3143194
Date of current version: 22 March 2022
autonomous systems. He developed
several robots that operated robustly
in challenging real-world environments,
including Rhino and Minerva,
which acted as interactive museum
tour guides; Obelix, which autonomously
navigated like a pedestrian
through the city of Freiburg, Germany;
and a self-driving car that could
navigate through a complex parking
garage and park itself.
More About the IEEE Robotics
and Automation Technical
Field Award
The IEEE Robotics and Automation
Technical Field Award was established
in 2002 by the IEEE Board of DirecFigure
1. An IEEE Fellow, Prof. Wolfram
Burgard is with the Department of
Computer Science at the University of
Freiburg, Germany.
tors and is presented for contributions
in the field of robotics and automation.
It includes manufacturing automation,
robotics and automation in
unstructured environments, sensor
design, integration and fusion, robot
design, modeling, planning and control,
methodologies for robotics and
automation, and the quality of the
nomination. This award is sponsored
by RAS.
MARCH 2022 * IEEE ROBOTICS & AUTOMATION MAGAZINE *
113
IEEE Robotics & Automation Magazine - March 2022

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