Tech Briefs Magazine - August 2022 - MD-20

process three times with different masks
and colored particles to create a light
pattern that spelled M-I-T.
Once they had finetuned the fabrication
process, they tested the mechanical
properties of the actuators and used a
luminescence meter to measure the intensity
of the light.
From there, they ran flight tests
using a specially designed motion-tracking
system. Each electroluminescent
actuator served as an active marker that
could be tracked using iPhone cameras.
The cameras detect each light
color, and a computer program they
developed tracks the position and attitude
of the robots to within 2 millimeters
of state-of-the-art infrared motion
capture systems.
In the future, they plan to enhance
that motion tracking system so it can
track robots in real-time. The team is
working to incorporate control signals
so the robots could turn their
light on and off during flight and
communicate more like real fireflies.
The researchers are also studying how
electroluminescence could even improve
some properties of these soft
artificial muscles.
For more information, contact Abby
Abazorius at 617-253-2709 or abbya@
mit.edu.
Improving Autonomous Robotic Systems
A new general-purpose optimizer can speed up the design of walking robots, self-driving vehicles,
and other autonomous systems.
Massachusetts Institute of Technology, Cambridge, MA
W
hen designing an autonomous
robot, engineers must run
countless trial-and-error simulations,
often informed by intuition. These
simulations are tailored to a particular
robot's components and tasks, in
order to tune and optimize its performance.
Now, MIT engineers have
developed a general design tool for roboticists
to use as a sort of automated
recipe for success.
The team has devised an optimization
code that can be applied to simulations
of virtually any autonomous
robotic system and can be used to
automatically identify how and where
to tweak a system to improve a robot's
performance.
The heart of the code is based on automatic
differentiation, or " autodiff, " a
programming tool that was developed
within the machine learning commuDesired
Box
Turtle1
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Turtle1
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Turtle2
OriginOrigin
Desired Box
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Desired Box Pose
nity and was used initially to train
neural networks. Autodiff is a technique
that can quickly and efficiently
" evaluate the derivative, " or the sensitivity
to change of any parameter in a
computer program. Dawson and Fan
built on recent advances in autodiff
programming to develop a general-purpose
optimization tool for autonomous
robotic systems.
Desired Box Pose
The team showed that the tool was
able to quickly improve the
performance of two very different
autonomous systems: one
in which a robot navigated a
path between two obstacles, and
another in which a pair of robots
worked together to move a
heavy box.
Turtle2
The researchers hope the
new general-purpose optimizer
can help to speed up the development
of a wide range of
autonomous systems, from
walking robots and self-driving
vehicles, to soft and dexterous
robots, and teams of collaborative
robots.
The team has made the
general optimizer available to
Turtle2
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A new general-purpose optimization tool can improve the performance of many autonomous robotic systems. Shown here
is a hardware demonstration in which the tool automatically optimizes the performance of two robots working together to
move a heavy box. (Image: Courtesy of the researchers)
20
Turtle2
download and plans to further
refine the code to apply to
more complex systems, such
as robots that are designed to
interact with and work alongside
humans.
For more information, contact
Abby Abazorius at 617-2532709
or abbya@mit.edu.
Motion Design, August 2022

Tech Briefs Magazine - August 2022

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