IEEE Robotics & Automation Magazine - December 2014 - 123

on a one-year-long study leave from
ANU, where he worked on image segmentation. While in the United States,
Jarvis also visited Bob Bolles at Stanford
Research Institute and was very inspired
by the work on laser range finders
he saw there. After going back to Australia, Jarvis succeeded in building the
first laser range finder in the southern
hemisphere around 1982, and with all
these tools at his disposal (lasers, image
segmentation, and the direction of a

computer science department) he
began his work in three-dimensional
(3-D) mapping.
Robotics as an Honest Test
for Computer Vision
Jarvis' interest in 3-D mapping led him
to work with robots, particularly robotic manipulation, as perhaps the most
honest way of testing whether your vision was operating correctly. His first
studies in robotics used computer vi-

The JP Algorithm
"We wrote a couple of papers together and I got interested in an area called clustering,
which is nonsupervised pattern recognition, and I authored an article in which I
acknowledged his support as second author, on a clustering methodology, which
turns out to be more useful than I ever imagined. The title, 'Clustering Using a
Similarity Measure Based on Shared Near Neighbors,' actually tells you the method.
So the method was just super simple. What you did is imagine all these points in, say,
Euclidean space. You found, maybe K nearest neighbors, and K may be seven or ten
for each point, and you looked at the neighborhood list of two points, and you said
these two points could be considered put together if they shared a lot of neighbors. It
is a bit like saying, tell me your friends, you tell me your friends, if many of them are
in common, you are probably friends. In addition, it is a very simplistic computation,
and it does not make any assumption about underlying probabilistics, so it is totally
nonparametric, and by changing the size of the neighborhood and the thresholds, you
could adapt to many different sorts of clusters, clusters that were long strings of points,
they are sort of very globular, and so on.
"Twenty years later, I just looked up clustering, and I found everywhere they are
referring to this Jarvis-Patrick (JP) algorithm, and I thought, what the heck is the JP
algorithm? It was the JP algorithm, so I was very pleased to find it was in great use
and by pharmaceutical and molecular people who were working in the field on
the edge of medicine and molecular chemistry. In addition, they were using this as
a standard method for clustering their different kinds of products and making this
clustering available to their customers and the like. So it was a pity I did not patent
it, but I was pleased to see it used."

Jarvis' First Robot
"I thought if you could actually show that manipulation was supported by this
[computational] analysis, you then had a way of saying, 'even if you have objections to
some of the philosophic ideas, I can demonstrate a practical outcome, and therefore,
from an engineering point of view, that is a good and solid result.' Then I thought, what
I need is a robot. I had something like US$20,000 for a robot, and in those days, the
decent robots were made by Joe Engelberger's staff, one called Puma and another
called Unimate. The small one was called Unimate 250. Those cost about US$45,000-
US$50,000, and I had US$20,000. The local agent for Unimation, I think they were called
Unimation Incorporated in those days, said, 'why don't you ring Joe Engelberger?' So
with nothing to lose, I remember trying to pick my time, so it was sort of mid-afternoon
in the United States, and I spoke to Joe Engelberger, you know, the great, the father of
robotics and all this kind of stuff, and he just said, 'oh, look, leave it with me. Maybe I
can find a refurbished unit and send it to you.' And indeed, good to his word, this box
arrived with the Puma 250 at US$20,000. So, that was my first robot, and between the
time I got it and the time I shifted to Monash, it was over a three-year period, I started to
do hand-eye coordination. I was using my laser range finder and other methods, vision
methods, to allow robots to pick up blocks. So, I scavenged a lot of the building blocks
from my kids' playpens and stuff like that, and I had all these different-colored blocks,
and I could manipulate them and pick them up off a table, and put them into different
[configurations]-and I was quite pleased with that. Again, the experimental side was
seen as critical for me, rather than the more theoretical side."

Figure 4. Jarvis with the Marsokhod robot
at Monash University (photo courtesy of
Andrew De La Rue).

sion to enable a Unimate Puma robot
to recognize and pick up blocks, described in "Jarvis' First Robot." Unlike
the popular Guzman approach that
used edge data to reason about a block's
appearance, Jarvis focused on
using the physi- The substantial
cality of the funding enabled him
robot and the
objects around to buy larger pieces
it to solve com- of equipment, and he
puter v ision
problems. He started getting involved
called this ap- in "outdoor robotics."
proach active
vision and assumed that you can learn a lot from
manipulating the object and seeing
how it changes as a result of that manipulation, so you did not need to
know everything about an object before
picking it up; "the manipulator is allowed to change the world, and therefore, the change can expose things that
you may not have known statically. "
Most of this research was carried out in
his newly founded Intelligent Robotics
Research Centre using 3-D stripe scanners. This line of research led him to
his first work with industry, in partnership with BHP Billiton, on hand-eye
coordination, mobile robotics, and 3-D
sensors. This collaboration was supported by one of the early, rather large
government industrial research development grants. One practical outcome
was a "stripe light 3-D system that was
used in the steel mill to test the

December 2014

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IEEE ROBOTICS & AUTOMATION MAGAZINE

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Table of Contents for the Digital Edition of IEEE Robotics & Automation Magazine - December 2014

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