From the University of Birmingham –
August 12, 2019 -- Robots need to know the reason why they are doing a job if
they are to effectively and safely work alongside people in the near future. In
simple terms, this means machines need to understand motive the way humans do,
and not just perform tasks blindly, without context.
According to a new article by the
National Centre for Nuclear Robotics, based at the University of Birmingham,
this could herald a profound change for the world of robotics, but one that is
necessary.
Lead author Dr. Valerio Ortenzi, at the
University of Birmingham argues the shift in thinking will be necessary as
economies embrace automation, connectivity and digitization ('Industry 4.0')
and levels of human -- robot interaction, whether in factories or homes,
increase dramatically.
The paper, published in Nature Machine
Intelligence, explores the issue of robots using objects. 'Grasping' is an
action perfected long ago in nature but one which represents the cutting-edge
of robotics research.
Most factory-based machines are 'dumb',
blindly picking up familiar objects that appear in pre-determined places at
just the right moment. Getting a machine to pick up unfamiliar objects, randomly
presented, requires the seamless interaction of multiple, complex technologies.
These include vision systems and advanced AI so the machine can see the target
and determine its properties (for example, is it rigid or flexible?); and
potentially, sensors in the gripper are required so the robot does not
inadvertently crush an object it has been told to pick up.
Even when all this is accomplished,
researchers in the National Centre for Nuclear Robotics highlighted a
fundamental issue: what has traditionally counted as a 'successful' grasp for a
robot might actually be a real-world failure, because the machine does not take
into account what the goal is and why it is picking an object up.
The Nature Machine Intelligence paper
cites the example of a robot in a factory picking up an object for delivery to
a customer. It successfully executes the task, holding the package securely
without causing damage. Unfortunately, the robot's gripper obscures a crucial
barcode, which means the object can't be tracked and the firm has no idea if
the item has been picked up or not; the whole delivery system breaks down
because the robot does not know the consequences of holding a box the wrong
way.
Dr. Ortenzi and his co-authors give
other examples, involving robots working alongside people.
"Imagine asking a robot to pass you
a screwdriver in a workshop. Based on current conventions the best way for a
robot to pick up the tool is by the handle. Unfortunately, that could mean that
a hugely powerful machine then thrusts a potentially lethal blade towards you,
at speed. Instead, the robot needs to know what the end goal is, i.e., to pass
the screwdriver safely to its human colleague, in order to rethink its actions.
"Another scenario envisages a robot
passing a glass of water to a resident in a care home. It must ensure that it
doesn't drop the glass but also that water doesn't spill over the recipient
during the act of passing, or that the glass is presented in such a way that
the person can take hold of it.
"What is obvious to humans has to
be programmed into a machine and this requires a profoundly different approach.
The traditional metrics used by researchers, over the past twenty years, to
assess robotic manipulation, are not sufficient. In the most practical sense,
robots need a new philosophy to get a grip."
Professor Rustam Stolkin, NCNR Director,
said, "National Centre for Nuclear Robotics is unique in working on
practical problems with industry, while simultaneously generating the highest
caliber of cutting-edge academic research -- exemplified by this landmark
paper."
The research was carried out in
collaboration with the Centre of Excellence for Robotic Vision at Queensland
University of Technology, Australia, Scuola Superiore Sant'Anna, Italy, the
German Aerospace Center (DLR), Germany, and the University of Pisa, Italy.
https://www.sciencedaily.com/releases/2019/08/190812094446.htm
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