Robotic arms: reality or fantasy?
Written by Vinay Jayaram
Any discussion on prosthetic arms must inevitably start (or end) with Luke’s hand from The Empire
Strikes Back. It was, for any prosthetics researcher or aficionado, the holy grail of the field: a hand that
looks like a human hand, moves like a human hand, and responds to stimuli like a human hand. Indeed,
in the next movie, it’s almost as if Luke has a human hand after all, no post-processing required. Unfor-
tunately, much like faster-than-light travel and light sabers, such a prosthetic exists today firmly in the
realm of fantasy.
Prosthetics is a fascinating field be-
cause it is the intersection of so many
different disciplines. Ask a dozen re-
searchers why a perfect prosthetic
hand doesn’t exist today and you’ll get
just as many answers.
The mechanical engineer would say:
Have you seen how many different mo-
tions the human hand can do? It’d be a
miracle to make one that can move like
that which doesn’t fall to pieces if you
look at it wrong.
The orthopedist would say: Comfort-
ably carrying a prosthetic manipulator
24 hours a day isn’t easy, and in-bone
implantation of prosthetics is not yet a
solved problem.
The neuroscientist would say: Repro-
ducing spinal reflexes from a prosthetic
hand? Ignoring the embodiment issues
involved, that requires an interface to the
peripheral sensory nerves that perfectly
replicates what would have existed in the
natural limb.
Then all the scientists would propose
totally conflicting ideas for the best
way forwards.
These days, however, people are look-
ing more and more towards one par-
ticular person to solve the problems
that keep prostheses frustrating and
often unusable: the machine learning
expert. If you just record enough data,
the thinking goes, and train the right
predictive model, you can learn how
to steer the prosthetic limb using the
brain. With perfect control, even im-
perfect sensory input or mechanical
ingenuity may be surmountable. And
so, in the past decade there has been
an explosion in the number of people
trying more and more complicated ap-
proaches to control a prosthesis with
the mind. While the performance met-
rics in some of these papers keep get-
ting higher and higher, however, even
state-of-the-art commercial prosthe-
ses use methods that the machine
learning community stopped looking
at decades ago.
To get into this mystery a bit more,
let’s back up and talk about how a
prosthesis works in more detail. Let’s
imagine you’ve gotten your arm (half-
way between elbow and wrist) cut off
in a freak light saber incident. Once
the stump heals, you go to your lo-
cal prosthetics company and ask for a
new lower arm. At this point you have
a few options: a passive arm, a body-
powered arm, and a myoelectrically
controlled arm. The passive arm is
just that: a realistic replica that hangs
on your forearm as a dead weight (or,
for something both more sinister and
more useful, you could choose a hook).
Body-powered means something like
a claw that you can open and shut by
shrugging your shoulders. Myoelectric
is what the machine learning commu-
nity cares about. It uses the signals
from the residual muscles to control
the prosthesis. Even though your hand
is gone, the muscles that used to con-
trol it are all packed into your forearm,
and are mostly still around – which
means that when you imagine moving
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