okay so why don't we just start by
having you introduce yourself and tell
us where you were born and where you
grew up
hi uh i'm brian carlisle uh i was born
in san francisco
i grew up in both san francisco and
marin county
uh and then later live down in the
peninsula
and where did you do your undergraduate
study i went to
well both undergraduate and graduate
work at stanford university
uh so i studied in mechanical
engineering there at stanford and
my professor there was a gentleman named
bernie roth who is very well known in
the robotics community
and who is also the advisor and and
professor for
a number of other colleagues that i
subsequently work with
including bruce shimano whom i believe
you you also interviewed
how did you first become interested in
robotics uh i was always kind of
interested in mechanical things my
grandfather was a pattern maker
who made patterns for big diesel engines
and so
what he did was make the big wooden
patterns for these uh these giant
engines that uh
you know would pull freight trains
around uh and i visited him
several times in ohio and uh he took me
one time to his uh
factory where he worked and i was sort
of fascinated by the
the whole process of uh making patting
things and
and uh creating something that was you
know big and strong and powerful
mechanical so that got me interested in
mechanical
things at a very early age and then
later as i was a
teenager i got interested in model
railroading and i got a big model
railroading thing going for a while i
was
16 17 18 and did that and
then finally when i went to college i
decided i wanted to go into engineering
when did you decide you really wanted to
focus on robotics
robotics probably about my senior year i
would guess
i started generally in mechanical
engineering
i met some folks through bernie roth
my advisor my senior year
a gentleman named vic shineman who was
also at stanford uh and um and bruce
shimano
and um vic was uh working on uh
some robots there at stanford which
we'll talk about here in a little bit
um and uh so i got together with victor
and
with bruce kind of my senior year
masters year
and then when i graduated with my
master's degree
uh i went to work with vic and bruce in
a very very small company
did you do a phd also no just i did a
masters actually i did most of my
undergraduate work
uh in mechanical engineering and design
and mechanical engineering and then most
of my master's work
in electrical engineering and i was
particularly interested in
servo mechanisms and at that time
microprocessors were just coming out i
graduated in 1975 with my masters
and microprocessors were coming out in
the early mid 70s and so
servo control was transitioning from
essentially an
analog process uh to a digital process
and so i took a number of courses in uh
microprocessor based control and then
sort of servo engineering and circle
mechanisms and
all of that was you know kind of led
right into the robotics thing
what kind of microprocessors were they
using for that the very well
you know the very earlier ones that
first came out
for microprocessor motion control were
things like the um
uh uh what was it it was the 88 not the
8088 it was 6802 i think or 6502 was
that wasn't the number it was you know a
little small eight but
uh slow 10 megahertz processor um
but you know the first intel
architectures were coming out kind of in
the
mid 70s and
also deck which who's no longer with us
digital instruments
corporation digital equipment
corporation
came out with a a mini computer which
they sort of turned into a micro
computer and that was called the lsi 11
and they had taken something that had
been a 32-bit architecture and kind of
squeezed it down into
something that would fit in kind of a
bread box and
that was kind of the first more general
purpose high-level
micro computer mini slash mini computer
kind of architecture
and so we used that early on in the mid
70s uh to actually
do the computations to control the
robots and we used
things like this very small 6502
microprocessors to
control the individual motors and how
are you programming
um the microprocessors or the robots
well robots have several layers of
control
in them there's a layer of control which
is a motion control which is
basically controlling the motors and
then since you have some
number of motors in a robot ranging
typically from three motors to
six or seven motors you need to
coordinate all those motors together to
make the robot move
and you do that typically by having a
mathematic
mathematical model of the robot called
the kinematic model
and you again
tell the robot at a higher level
computer what kind of motions you want
to make in
typically straight line motions in
cartesian space
and then the computer figures out all of
the individual commands for the
individual motors
so you have at the sort of supervisory
control level a language which is
a robot programming language uh similar
to many
computer languages with its standard
control structures um
you know do loops and f loops and while
loops and case structures and so forth
but then in addition to that you have a
whole um a library of motion control
commands um so you know open the gripper
or move in a straight line or
um you know in some cases talk to a
vision system and get information from
some kind of a sensor and then make some
decisions about that um so my uh my
partner
that i've worked with when you
interviewed bruce shimano uh
really was kind of the software guy and
he was he was the guy that
was expert in the kinematics and the
language and
the mathematics uh to move the robots
and i was more the hardware guy and
mechanical guy and
electrical guy in terms of the
mechanisms themselves
so what were some of the big challenges
for the electrical mechanical design of
the first
system well early on certainly in the
early 70s
the the earliest robots were hydraulic
powered machines they were very big and
they were very powerful but they really
didn't have much of a control system in
them um
the early controllers were
rotating drums in which information
analog information was stored on a
rotating drum was like a player piano
a drum would rotate around and it would
send out you know some analog
information to some
servos and the robot would sort of lurch
around and
open and close the gripper and so that
was not a real sophisticated
controller but uh nonetheless you know
some early applications in the
60s 1960s were done that way
when the computer control of robots was
really introduced
in the mid-1970s and and stanford was
one of the
the principal locations where that was
done mit was also working in that area
at the time and
carnegie mellon a little bit
and with the so one of the big
challenges was
uh first of all um how do you
make this uh all this complicated
mathematic execute quickly enough
uh in the uh computers that were
available all the time uh secondly
um you know how do you replace these
hydraulic servos
with electric servos or electric motors
that were strong enough
to move some of these big mechanisms
around
so there was both a software design
and then kind of a mechanical design
challenge and making the early electric
robots
were there any memorable breakthroughs
in that well i think
uh probably the biggest software break
through was a
thesis uh done by don piper in 1968 he
was another one of bernie ross students
and don's uh thesis um
uh was essentially how you uh solve
a um a six axis kinematic
the inverse solution for a six axis
kinematic transformation
uh that is um uh
how you invert the the rather large and
complicated matrix that results
when you're trying to figure out uh how
to move um six degrees of freedom in a
robot arm
to get the tip to move in straight lines
uh so so don piper sort of figured that
out and
that was kind of the basis for computer
control of robotics
that was an important breakthrough and
that came from stanford also in bernie's
group
and um so the first first system you
worked on was the vicar
yeah the um uh sort of a bit of a
history slide here
we have sort of in the 1969 time frame
something called the stanford arm which
was a cartesian
sort of a polar coordinate robot rotated
and extended and had a wrist and so
forth
and uh that was developed by uh vic
shineman um at stanford
and a couple of other students there uh
bruce shimano was involved in doing some
of the controls
for that the control system for that
robot was a pdp 10 computer it was you
know the size of a volkswagen
and uh you know big very big fairly
expensive computer at the time
and um it was used uh
in just really primarily in research at
stanford for a couple of years
um then uh in 1975
um when i graduated from stanford i
joined
victor and bruce joined victor and three
of us had this small company
called vikarm or vic vick's arm
and we sold one of these
robots to the general motors research
lab
and that was i remember delivering that
on my birthday in a snowstorm my
birthday happens to be in february
uh to troy michigan and
um dragging this robot to the loading
dock there general motors
through the snow that was entertaining
and then going in and installing it with
a couple of fellows who are also known
in the robotics community a gentleman
named lothar russell another fellow
named
mitch ward and so we delivered that
robot
to them with a at that time we
had packaged the uh language
that bruce had developed at stanford
into something that would run on one of
these lsi 11 computers um
and so we delivered
the robot and a controller
actually i think i take that back i
think that was early enough that we
didn't have the language in the in the
portable computer yet we just delivered
the robot
and the repackaged and came a bit later
um
so uh that was a direct sort of transfer
of technology from the university um you
know to a big and uh
interested commercial uh prospect
general motors have been using robots
for some time but they were very these
large hydraulic very simple robots and
and gm
um was interested in the idea of
a robot that was more human scale and
they could do small part assembly and
material handling
so they did some gm did some research
then uh for a little while
uh on that uh uh early vikram
robot and about and the vikarm kind of
carried on um
doing a bit more work and and starting
to develop
the packaging of the controls into the
lsi 11 um
computer environment and then in early
19
maybe late 1976 or so
gm came out with an rfq for something
that they called the puma robot which
was
a gm acronym for a programmable
universal machine for assembly
and that whole concept was
in part based on this this vicar robot
that now that we had delivered
and so there were a number of companies
that bid on that
including some very large companies like
abb and cincinnati milicron and bendix
and
honeywell and and so forth and then
there was unimation and then there was a
vicar
animation was sort of the uh
existing um a commercially
viable robot company at that time in
1977 they were doing maybe 25 million
dollars a year
selling these large hydraulic robots
primarily to the automotive industry
vikram was a tiny company and had no
resources uh to
uh successfully bid on a proposal from
general motors no
no we couldn't have credibly uh done
that uh so we sold vikram to unimation
uh and then bid on the puma contract
and and we won that contract and because
we really had
uh relative even to these very large
companies we had technology that that
worked and
and uh we could demonstrate that
so we went on then over the next year
and a half to develop
kind of a commercial product which was
called the puma
and this was the very first prototype
that was delivered to the smithsonian in
uh
by general motors actually in in 2003
but we delivered that first prototype
about a year later uh in uh sometime in
i would guess uh early 1978 somewhere in
there
and um gm i really liked it and then we
did a more packaged version of it with
castings and and and kind of the
more industrial uh version then rather
than just a prototype
and um that grew very quickly into quite
a good business
for um unimation it grew in about a 40
million dollar business in the next
three four years and more than doubled
the size of unimation
and there were some thousands of these
puma robots that were built and sold all
over the world and there's still
a few of them hanging around university
research labs
that people still tinker with and the
puma came we so we developed that and uh
various sizes there was kind of a small
version that we did which
you can see i don't know if you can see
it in the camera over here in the
background
and that small version was done in the
in the early 1980s and there were some
bigger versions and so forth so that was
kind of a
technology transfer story really from uh
university to a very small company
uh than uh to a sort of a medium-sized
uh company
uh and uh and out into industry
so over some of the big innovations
the preceding arms were essentially all
hydraulic
unimation had made these very large
heavy hydraulic arms and with these
again
very simple controllers the almost
rotating drum
type of controllers um other companies
who were in the business and bendix
had some hydraulic robots cincinnati
milicron had some hydraulic robots
and again all of those with very simple
types of controllers there were some
other
pneumatic robots out there that were
driven by air cylinders essentially
but there was no electric robot there
was no servo controlled robot and there
was no language and there was no
robot that had the kinematic solution
you know running in a controller where
it could literally move in straight
lines and talk to sensors and
communicate with vision systems and and
uh
do repetitive structures like pick parts
you know do loops and things and pick
parts off of pallets without having
every single program every single motion
explicitly
taught so the puma was different in
both regards both in the control system
being the first computer-based
control system that was available uh and
then secondly uh
in being the you know the first small
medium light to medium payload electric
robot that was available
and about that same time another company
abb in sweden came out with a small
electric robot that they targeted
primarily at um
arc welding so just about that time the
mid 70s and the 76 77 time frame there
were these two products that came into
the market there was the
the puma from the animation and then
there was a small electric robot i think
was called the irb six
uh from abb and after that
a lot of companies then moved into
electronics yeah and so the puma was
somewhat of a watershed
uh product in the sense that uh it
became clear then that you could you
know
a that there was a market for small
electric growth that's what general
motors buying these things buying 40
million a year these things
uh and then b uh that the technology
was mature enough to
make small electric robots so then
everybody sort of piled on board you had
you know many japanese companies kind of
expand into the robot business doing
lots of electric robots
various european companies a few years
later i think by
1983 or so when time magazine had robots
on the cover of time magazine
just in the u.s there were 34 u.s
companies that i counted up at the time
that were you know in the robot business
uh there were probably
two or three times that in japan in the
robot business and
there were a good dozen or so in europe
so uh
it was believed in the early 80s that
robotics would be a really explosive
growth business
uh that uh that robots would replace
um human labor in uh things that were
heavy big and dangerous and you know
like spot welding which in fact
they largely have done in hazardous
environments
underwater sorts of things which has
also come about
but it was also felt that robots would
do a lot of work in
a small part assembly and and that has
been
i would say less uh ubiquitous than um
some of these other areas because people
can still easily do small part assembly
until you get to the point where you're
talking about very high precision when
you get down to
a thousandth of an inch or you know 10
20 30 40 microns
kinds of precision then it becomes very
difficult for people to do it and then
you find that robots or other automated
equipment using various kinds of
feedback is doing that so in the uh
but there was a sense in the early 80s
that you know robots were
were going to be everywhere and that
humanoids were you know
just a few years away and so there was
kind of this
wild boom and companies investing in
robotics
and that lasted for about five years and
then kind of tapered off
why do you think it tapered off it
tapered off because
the well for several reasons one um
a lot of the robots are not nearly even
today
as independent and sophisticated um
as a person is and so many uh
applications that were envisioned for a
robot
you just couldn't do with the technology
uh you couldn't send a robot in to clean
the bathroom or
uh you know put the dishes away or wash
the windows or
do many of these kinds of things that
were kind of imagined
in in the early days so people sort of
took uh
the hum the science fiction view of
robots and and figured it was all going
to happen in the next five or ten years
and uh the technology has taken much
longer to develop so i think that's
kind of the bottom line there are some
things that robots are quite good at
doing if they're very repetitive in a
very structured environment and you
uh order the parts and the robot can go
pick up the parts and
and that um but working in unstructured
environments is still very challenging
even today
what were some of the big companies that
were investing in robotics then
uh you had uh many of the large
companies in the united states you had
general electric
which had a big robotics program
westinghouse which
then uh not to be outdone by general
electric purchased unimation
um in the about
the 1983 time frame
um and then you had ibm
i had a big robotics uh program uh you
had
bendix uh which uh you had honeywell i
had a robotics program
um so uh quite a number of large
companies uh had uh programs and then
there were a number of smaller companies
as well sort of startup kinds of
companies
and um so so what led you to
uh to your next business
well we uh so we had a sold bike arm out
to unimation
and um the way that kind of worked out
was that um
we were out here in california uh being
california kids
and um unimation was back in connecticut
and so our
sort of working relationship with
animation evolved into we became
sort of an r d group a research lab and
they had the manufacturing and
production facilities back there and so
we would develop new products
and both controls and robots and we had
mobile robots you know
rolling around and doing on infrared
navigation and a whole variety of things
and unimation would um you know
commercialize those products
back there um and that worked out quite
well for about five or six years uh
and then uh westinghouse as i mentioned
uh acquired
unimation and westinghouse uh
had a big robot research group in
pittsburgh
they were a number of years
behind where we were in both technology
and software and in terms of
the experience and some of the
capabilities of the of the team
um but there was quite a period of
kind of uh integration planning uh that
went on
um at the same time westinghouse
reorganized just after this acquisition
they reorganized from three business
units or four business units down into
some
smaller number two or three business
units and they eliminated the business
unit that had planned and executed the
acquisition of unimation
so all the managers who had sort of done
that were all let go or
reassigned and unimation was punted into
this completely new group that had no
clue as to why they suddenly had
inherited this business
that new group and their wisdom went out
and hired mckinsey the consulting
company
uh to tell them what they ought to do
with this acquisition that they just
spent 107 million dollars on
and so we then had um six months of uh
young sort of consultants from mckinsey
who didn't know anything about robots
coming out and asking us in california
well what should we tell westinghouse
they ought to do with this business
and that looked like it really wasn't
going to have a very positive outcome
and so
um at the same time
in you know this period around 1983
there was as i mentioned
a great deal of uh brouhaha and euphoria
about
robotics being on the cover of time and
so forth
and so we were being regularly
approached by venture capital investors
and
and i knew people the venture capital
community they said gee why don't you
guys you know go start your own robot
company
so after watching westinghouse thrash
around for some
six months or so with no clear plan and
then finally they hit upon the nice idea
that uh
they would offer us all the opportunity
to relocate to pittsburgh
um which didn't go over terribly well
with this uh
a bunch of people from silicon valley we
said no thank you
and bruce and i went out and we raised
some millions of dollars in venture
capital to uh to start adept
um adept technology so that was in
1983 and
at that time we looked at the market and
we said you know this
well it's not a huge market there still
is a an
opportunity in this area of small part
material handling and assembly
and in particular it would be
nice if the robots were more reliable
than the robots have been in the past
and because they need to run very fast
and for a long period of time
so um so i've been working on a um sort
of an
with another company uh uh on a new
motor design a direct drive uh motor um
similar to a giant stepper motor it's
called a variable reluctance motor
and it was low cost but very high torque
and but it was
very challenging to control this thing
and make it run smoothly and quickly
uh so we figured out a little bit of
that while we were at unimation and i'd
given westinghouse the opportunity to
invest in it and develop it and
westinghouse said no we don't think this
is very useful
so they walked away from it so we took
that core technology and we cut some the
deal with westinghouse and we said look
we'll give you 15 of this new company
you let us you know
take this technology base that we have
we won't take any of the existing
products um
uh although you know westinghouse will
retain the puma and all the existing
products
but we'll go do something new and uh
we'll get it funded
and but we want to take the people we
want to have a license to certain bits
of technology and particularly this
motor and some other stuff
and uh we'll go off and do our own thing
and
you know if you guys are interested in
it uh maybe you can sell it or
distribute it at some point
so so we funded this thing with venture
capital and went out and hired a few
more people but we started
adept with 27 engineers 27 people
most of whom were engineers and a
facility and and
some ideas and then we went out to
develop a a direct drive
um assembly robot which we did in about
14 months
and uh and introduced it about um you
know a year year and a half later
uh and it was a you know a really
popular product
our first uh year of sales we went from
zero to 14 million dollars in revenue
um and uh which was uh much faster even
than we had grown the uh the puma
product line with unimation uh and the
the adept one as it was called um
you know was uh uh bought by uh you know
companies all over the world it was very
reliable
uh it would there's some of the machines
that we developed and delivered in 1984
are still running to factories today
you know many years later and
it was uh it was very well received and
very well recognized and so adept over
the next few years
grew to sales of
over 100 million dollars and we
developed other products and we
continued to develop we believe
then and as we believe now that
much of the key differentiating
technology
for robots is is the sophistication
power and ease of use of the control
system
the language and the controller we
invested quite a bit of money in
integrating machine vision with motion
control
and so adept um was one of the very
early
in fact we've done that even at
unimation this uh this early robot here
back in the unimation days
um was actually vision guided
demonstration this had a camera and it
was a you know six
axis robot with a machine vision uh back
in
1981 and which was years before any of
the
other robot companies we're really doing
anything you know with integrating
machine vision
um and so adept when we introduced the
adept one we introduced it uh
along with a machine vision um system at
the same time
and uh adept uh over the years and even
today probably sells half its robots
with machine vision and uh we developed
a number of technologies so some things
called flexible part feeders which
would allow you to just take a bag of
jumbled parts and drop them
kind of on this vibrating conveyor which
would spread them out
under a camera and then the robot could
pick them up once they were separated
and that made it much easier and to do
small part assembly because one of the
big challenges was how do you get the
parts out of this bag of parts
so we did work in in robotics we did
work in vision we did work in part
feeding
we did work in making all of that easier
to visualize in the program
uh in 1996 i bought a company called
silma
and somo was another stanford spin-off
started by one of the founders was a
gentleman named john craig who was
another one of
bernie roth's students and um
they had concentrated on developing um
offline
three-dimensional simulation and robot
programming technology
so they could program spot welding
machines and they could program
coordinate measuring machines and they
could program assembly robots and you
could
develop all the software in this offline
environment and you could visualize it
and move cad models around and debug it
and get the geometry sorted out and then
you know download that program to your
spot welding line or whatever you have
well i have believed for uh some time
that um
and still believe uh that uh you know as
we go forward in robotics uh
we need to add the we need to integrate
the ability uh to model and reason about
geometry
uh with the robot control system and so
i acquired soma
uh so that uh we would start to get that
technology within adapt
and um someone was about a five or six
million dollar business then at the time
and and we started then to uh integrate
um some of the uh selma technology uh
with some of the uh adept
uh robotics technology and so we could
do
i don't know if it's on here
let me see if i have the
next slide here yeah
so we could do things like this where
you could simulate a
an entire production line and you could
simulate the robots at each of their
stations and you could predict how
quickly they could do a particular task
and so and then you could do line
balancing you know along the line
so that uh if you know workstation one
took five seconds and workstation two
took three seconds you could
move some things around and try to
balance uh the
cycle time you know for each of the
workstations so that you would improve
your throughput
and typically when you did that with the
simulation tools you would improve the
throughput of the entire
uh uh assembly line by 30 or more and
when you're
spending maybe half a million dollars or
a million dollars on an assembly line
getting 30 more productivity out of it
was a big deal
we also developed technology to reason
about
the geometric stability of parts if you
dumped a bunch of parts onto a
flat surface what percentage of the time
would they lie in these very stable
states
and based on that you could predict what
the throughput of these flexible park
feeders would be how many parts per
minute you could feed so
we had some very sophisticated
mathematics and reasoning
that we were starting to build into the
systems uh in the um
you know the late 90s and sort of early
2000s you know time frame
and it was my intent to integrate the
the offline uh geometric modeling and
reasoning
capability with the online control
systems
because what you ultimately would like
to have is you'd like to be able to
have a robot roll into a room and use
its vision scanners and laser range
scanners to make up a model of the room
and know whether it can reach under the
table or over the table but it can't go
through the table and
so if it doesn't have a model already
you'd like to be able to create a model
you'd like to be able to reason about
that model you'd like to be able to
update that model with
uh sensory information um and and that's
kind of
you know what people do and what you
know higher level reasoning
entities do uh so that was kind of the
the rationale and we never completed the
integration of silma
we did a lot of work uh there but the
but the products were still sort of a
separate simulation and separate
motion control system by time we we left
and started precise
but that was uh was the vision
who is working on the vision systems
right now um
we uh well
if we go back to kind of the original
animation days we there was a vision
work that had started
at stanford also it had migrated over to
sri stanford research institute
there was a gentleman over there named
bob bowles who was well known
in the vision community and
um he had done a lot of work uh along
with a couple of other people over there
um uh you sort of commercializing some
of the um
uh research that had been done at
stanford
and then i licensed so we licensed the
uh
some of the sri technology um at
unimation
uh in the late um
well probably would have been in the
early 80s about the 1980 time frame
and we hired a phd named scott roth from
caltech
and scott worked with bruce shimano
and essentially
did our commercial version of that
vision technology and we develop
patents for various things and um
so he was kind of the uh the leader uh
both at earlier on
unimation and then subsequently at uh
adapt
uh in developing a lot of the uh the
early vision technology for us
what was it like working with bruce
um well you know the three of us are all
very different personalities um
uh have you met vic simon tomorrow
tomorrow
well you'll get vic's take on it all um
vic's very high energy um guy
with lots of ideas um and a rather short
attention span
um and so he's he was he was great at
generating ideas he's very bright he's
very capable
um um but uh in terms of
the back end of things of really turning
something into a commercial product um
you know he kind of loses interest
fairly quickly
and then in some sense that's uh why he
hired me uh originally uh
in the early days of bicarb was to kind
of um
uh make things um you know into real
products and that's a portable products
and that
bruce uh um uh
is a very very bright guy he's very
methodical he's very thorough
uh he's extremely detail-oriented uh and
never forgets anything
um and uh you know his uh
his real love is you know his software
in mathematics and so um he's a real
programmer
um uh and i'm sort of you know somewhere
in between i'm i'm
i have lots of ideas but i can also kind
of work details and and
get through the process and get to the
back end and uh so
i would say our relationship with victor
was you know when it was vikarma was
vic's company and he was coming up with
a lot of ideas and
and bruce and i were doing some of the
uh the execution
when it was our company and i actually
took over um
vic shineman was the uh original
uh concept guy on on the puma not robot
uh for because unimation had bought
vikarm and
the puma was a derivative of some of
victor's victor's work
uh after a couple of years though um at
um
unimation even unimation was kind of too
big a company for victors and victor
quit
and went off to do something else
working with another company called
automatics where he went and did some
some other new things
and so i was running kind of then the
the research group there for uh for
unimation
uh and then when bruce and i um you know
started our own company um
obviously all the ideas were essentially
our ideas at that time
bruce and i have a very good uh working
relationship
we've worked together for i don't know
30 more than 30 years
and um you know i have lots of ideas
uh bruce is very calm and and he's sort
of a low-pass filter
and uh so if i can convince bruce that
you know one of my ideas is a reasonable
idea then
uh usually it has so we we work
quite well we kind of complement each
other personality wise um
he has always preferred to live in
southern california and his
family he was born down there his family
is down there and they're
japanese-american they were very close
to their families they like to you know
spend weekends together with families
and that and i've always hated southern
california and i've always preferred to
live in northern california
i can't imagine why anybody would ever
want to live in southern california but
um
so i've like you know the mountains and
skiing and so i've lived in the bay area
now i'm up here in the
auburn sort of near lake tahoe and [ __ ]
valley area
um but um you know having said that
we've
worked together quite well for for many
many years and haven't continued to have
a very close relationship
how are you able to coordinate that
distance
well the uh starting in uh
you know the mid 70s it was also kind of
the the birth of the uh
of the arpanet and the which sort of
morphed into the internet
um and so we've always uh you know just
done lots of telecommunications and
uh emails and phone calls and web
conferences and
and all of that and also logistically
uh the work that we were doing was
fairly well segmented bruce was doing
programming and running a software group
down in l.a
and when i was running unimation or
subsequently running adapt or
you know now running precise yeah i was
running more the hardware stuff but the
software stuff you can kind of split off
and so you know the software guys were
kind of you know
locked in an office and you know every
week or two we'd slide pizza under the
door kind of thing
but we tried to isolate them from
distractions from customers dropping in
from sales guys saying you know can you
do this can you do that
and as a result they were able to be
very very productive
and they had a quiet sort of fairly
intense um
you know working environment um but that
bruce enjoyed that and that's the way he
liked to work and and the people that
you know he hired to work with him
um also were those types of people and
and so
they've always worked very independently
and it's worked quite well now there are
periods when you're doing a robot where
you have to do integration where you
have to bring software and hardware and
mechanical
stuff together and when you do that then
people have to travel and they all get
together in one place and try to make
the robot work but
a lot of the development can be done
independently
uh well in the unimation days they were
in connecticut uh in the uh adept days
uh we
they were in uh originally uh san jose
and then later
livermore but in the bay area we had
about a hundred thousand square foot
facility there in
san jose and then we expanded uh even
beyond that
um uh in sort of the 2000 you know time
frame
and then um uh subsequently
um adept is contracted after kind of
about the time we left that was part of
the reason we did leave which we'll get
to
um and so it's probably about half that
size now maybe 40 50 000 square feet
something like that
uh what we're doing with this company is
we're now doing production
um at a a company that's a friend of
mine um
down in uh los angeles again so the
production is actually done down near
anaheim
were you at chad did you interview bruce
chad so you saw chad yeah okay so chad's
very interesting
chad's where we're doing with our
manufacturing right now okay so can you
talk a bit about precise automation and
how it came together
yeah um so uh so we've grown adept
um by sort of the um
late uh 90s um to about 100 million or
so
in revenue and we had 25 million in cash
we were profitable so the company was
you know pretty successful and we
had gone public and uh oh 1996
and um and so we were sitting around
with at the board level uh and talking
about well you know we've grown up to
this point you know how do we grow this
thing to the next hundred to two hundred
million dollars
and so we started um or contracted or
assigned three marketing people to look
at
three new markets and one was the
semiconductor robot market and the
second
was the photonics fiber optics market
and the third was the life sciences and
kind of genomics
market each of those markets was
developing for robotics and
so we spent about a year or so doing
these
market studies in each of those new
markets and
at the end of the day we decided that we
couldn't do three new markets they all
looked interesting but now we selected
um semiconductor and we selected
photonics as
two that were close to home a
semiconductor it was you know we were in
the middle of silicon valley and so we
had lots of semiconductor companies
there
and photonics was exploding uh at that
time jds uniphase which also had was a
big presence in silicon valley thousands
of employees
had grown from 2000 employees to 24 000
employees
in just a few years and everybody was
you know wiring fiber all over the place
and then putting these
fiber optic transceivers which convert
electrical signals to optical signals
you know
underwater and all kinds of places and
so it appeared that semiconductor and
photonics would both be very interesting
growth markets for us and we went out
and we did
a couple of acquisitions to help us
address those markets uh and um
in semiconductor between sort of 1999
and sort of 2000 we grew a business from
basically zero to 20 million dollars so
we had quite a nice business that
developed there
in photonics we got a very big program
with
jds uniface and we were doing two
million dollar work sales for them and
they told us they were going to need
hundreds of these things and
uh that we really had to expand our
manufacturing facility to be able to
you know meet their needs and how are we
possibly going to ramp up and scale up
and so forth
and we had taken their first pass yields
on these little fiber optic transceivers
which required that you align an optical
fiber with a laser diode to a tolerance
of a few microns
and then fasten them in place and we
figured out how to do that their first
pass yields uh
had been about 30 for this product and
they were selling it for thousands of
dollars in some cases ten thousand
dollars
um and throwing 70 of them away and so
we were able to take their first pass
yields up to 97 98 percent
uh with uh with automation so they were
delighted about that and
and were just going crazy with this uh
forecast uh they even invested 25
million dollars
in adapt to support that whole
development program
so in uh sort of the 1999 early 2000 uh
time frame you know it looked like uh
depth was probably gonna grow from 100
million to 200 million in the next you
know five or ten years
so we went over and we leased a big new
facility over in livermore
we roughly doubled our space from about
a hundred thousand square feet to about
two hundred thousand square feet
uh and we did some acquisitions and we
were you know scaling up
uh and then in 2000 um sort of the whole
economy
at least the electronics economy marched
off a cliff
we had this whole y2k thing and so
everybody
uh who needed to buy a computer bought a
computer in 1999
and they didn't need to buy any more
computers in 2000 and so the computer
market
tanked the the semiconductor market as a
consequence
tanked the disk drive market as a
consequence
tanked the photonics market as soon as
the long-haul fiber was built out
that had been way overbuilt way over
extended and uh and all the telecom
communications companies tanks we had
companies like northern telecom
that almost went bankrupt uh lucent uh
that was um
uh sort of sold off um
alcatel which had you know huge problems
i mean these these big worldwide
50 60 70 billion dollar companies you
know just cratered
and jds univase uh went from these back
down to about 2
000 employees over the next you know
three four years uh
so they absolutely stopped uh building
um you know any new product and
cut off their business so the depth we
saw our
orders our revenue dropped from you know
120 million
uh down to something like less than half
that about 50 million in a period of 18
months
and it was just we had just gone out and
at least signed a 10-year lease and this
you know
great big new facility expensive
facility
and the whole business vanished on us
so that was extremely painful and
we essentially spent the three years
between 2000 and 2003 downsizing the
company and we
had multiple layoffs and we had to close
sale sales offices
all over the world we had to um
it's very difficult to to lay people off
in
europe in france and germany it takes a
year or more to
let somebody go um that's obviously it's
uh
very difficult emotionally to to take
all these people that you've hired and
that have grown with you and you've
known for
some cases 20 years or more and tell
them that you know they just
can't work with you anymore um so
uh as well as of course we were a public
company so we had tremendous pressure
from the investors and the board of
directors we were losing a lot of money
and and so forth
uh so we spent three years sort of going
through uh
huge write downs and and layoffs and
downsizing
the company which we kind of finally
sort of accomplished
by the middle of 2003
and we never ran out of money uh which
was good um
but our cash was getting low and so at
the end sort of the middle of 2003 we
went out
to uh get ready some additional working
capital for the company
and at that time we had a reasonable
story to tell we had
stopped burning cash the orders were
starting to improve a bit again
um you know so the company was stable
and and um uh not hemorrhaging
um and so i went out and raised 10
million dollars with a
a venture fund called special situations
fund in new york
uh so they put a person on the board and
and they said gee you know we're going
to
put our money in but we really want to
have somebody else come in and run this
company
because it's been so bad for the last
three years that somebody else must be
able to do better
uh so the board hired a new
ceo who came in and um
about three weeks after he arrived he
decided well
i really have no interest in trying to
work with a couple of founders here and
so he just fired
bruce shimano and i just walked in one
morning and fired us with uh you know
no warning um and actually well we won't
go
much further there so um so bruce and i
um uh were were out
and this new fella had his opportunity
to run the company he was fired five
years later
but he did last four or five years um
but finally the board
got tired of him so our only uh
sort of uh solace i suppose is that uh
he certainly he certainly didn't do any
better than we had done over the
previous
15 or 20 years
he had worked it
in the automation industry but in a very
different industry in the paper industry
so he'd done paper mill automation and
he didn't really understand robotics he
didn't understand the market he didn't
understand the technology
and he didn't understand the rationale
in history for why we had the products
we had and
and he wasn't a big listener he was one
of these uh fellows who
was going to come in and do it his way
and so a lot of the very very uh senior
people left
within six months to a year after he
arrived and he really kind of decimated
the company
um but that's kind of what happens in um
or off not always happens but sometimes
happens
boards when the business is bad have an
obligation to the shareholders to go do
something
and the one thing they can go do is they
can go change the ceo and so you know
that's what the board did and
you know certainly can be argued whether
it was the right decision or not but it
was nonetheless that's what they decided
to go do
so so bruce and i then um went out and
started a new company
this company that we're currently
running called precise automation
and we decided this time that we were
not going to give up voting control of
the company
to outside board of directors so um
we raised quite a bit less money this
time around i funded part of it and we
rate we have some outside investors we
have a couple of corporate investors
and so we've grown this company uh more
slowly but we've also
done what we hope will be kind of the
next generation of
control systems and and robotic
technology
and um that now is um starting to get
out in the marketplace and so this
company is now starting to grow we'll
grow 100
this year and probably 100 percent next
year we're still small we're sort of at
the 4 million dollar
revenue range right now but we should be
doing six or eight next year and we'll
get up into the bigger numbers at some
point
but this time we will do it in a manner
that doesn't require us to give up
control of the business
i still believe that um that robotics
will continue
to be uh that the technology uh will
will uh continue to address many uh uh
different areas
uh that uh it will get out of the
factory as it's starting to do
uh into um you know service areas uh
into
there's already some some nice models
for surgical robots which
can be argued whether they're robots or
master slave telemanipulators but um
uh certainly a couple of uh at least one
company has done very well in that
area there's uh good examples of
some military you know hazardous
environment sorts of things robots going
into caves and you know
looking for mines and and there's some
you know toys and things like that now
that are starting to come out with uh
some of the technology we can look kind
of low-end
technology um and um
and if you think about it in sort of its
most the most general sense you know a
uh
people have lots of definitions for
robots but but my definition is
essentially a uh
uh autonomous uh thinking machine that
can sense and interact on its
environment interact with its
environment
uh you know computers have essentially
been these disembodied boxes that we
kept in the closet for you know the last
30 40 years
and a robot is in some sense you know a
computer that has some
sensing uh whether it's a vision or a
sense of a touch
and the ability to act on the
environment whether it's just rolling
around with wheels or flying a plane or
you know running a cruise missile or
doing surgery
um that's a sort of a very broad
definition
and the types of technology that we have
developed and will continue to develop
i can serve if you take that broad
definition many many different
applications
um and so the uh
i think as this technolo as this company
gets a bit more mature
uh we will have a lot of opportunities
to
spin this technology into several
different directions do you think that
the
fact that robots are embodied also in
some way in a
sense limits the their ability to be
general purpose like
computers are yeah i mean in some sense
um
obviously a a human is an extremely
flexible
mechanism but even humans you know pick
up tools to do a lot of things
so we adapt the human body
whether we can jump in an automobile or
pick up a screwdriver you know by by
adding
various um tools and
and so forth and certainly you're not
going to have a general purpose robot
that's going to be more flexible or even
as flexible as a human
you know anytime soon but you will have
a robotic technology that can be
packaged into whether it's
a sentry that's rolling around or you
know a
drone that's flying around the uh the
sensing technology whether it's machine
vision or
or terrain mapping or actuation
coordinating multiple actuators um to
move in a
coordinate system uh that that core
uh technology base um can be packaged
in different kinds of mechanical
packages to address a lot of different
applications
and what are the main applications you
focus on that precise
well at precise we decided we needed to
uh build a business that wasn't terribly
risky
uh in the sense that we didn't want to
spend 10 years doing r
d and have to raise you know tens of
millions of dollars to go and do
something
dramatically new and so uh we started by
sort of packaging the technology and
we're focusing on a couple of markets
one of which is life sciences uh so
we're doing uh robots uh for life
science applications and that is a
market that requires uh some sensing it
requires a lot of material handling
um uh it requires a
motion control there's both some
dedicated machines where we're selling
controllers into that market and then
we're also making some various
mechanisms that go into that market
and it's it's also one that's growing uh
quite quickly uh with the uh
focus on health care in the united
states uh we're doing more and more
personalized medicine
uh there are companies out there trying
to figure out how they're gonna
run you know uh a million um
drugs past your particular cancer and
see which one you know
actually does something as effective as
opposed to hitting you with these broad
chemoblasts that destroy your entire
body
there's a lot of genomic work going on
so there's a lot of r d
work going on in life sciences and then
there's also
a lot of a processing of just samples
some
biological samples whether they're blood
or urine or whatever and
all that's got to be handled and
processed and so forth
so that is a it's a market it's going to
be a growing market
um there will be you know derivatives
and spin-offs
of that market um and it's not going to
go to china it's not going to go to
singapore or go overseas it's going to
stay here in the united states
so that's really where we're spending a
fair bit of our time
we're doing a little bit of work with
the semiconductor industry where we're
selling controls to people that are
building semiconductor robots
those are a lot of that is in korea
right now a lot of that is
in asia because most of that market you
know is overseas now
and we're doing a little bit of work in
disk drive
and most of that
those products are going to singapore
are again now going overseas
but i would say over half our business
right now is in this life sciences area
that was the one that you ended up not
doing out of the three yes that's right
yeah it looked good 10 years ago and it
still looks good but we didn't know
anything about it then
um and i can't say we know a great deal
about it now but we know more about it
now than we did then
and it's also you know matured a bit and
developed a bit
over that period of time i was curious
you've obviously have a lot of very
have had a lot of very successful
technologies that you've developed
were there any paths that you took that
were unsuccessful
and that you thought would be
interesting but just didn't work out for
certain reasons
well yeah i mean there's always you know
you don't always hit home runs um
and you hopefully learn as you as you go
along
um we had one product at adept
which was a real boat anchor it was
called the adept
ii uh but we decided we're gonna take
these big direct drive motors and make a
very small robot with us that would go
really really fast
because it would have these giant motors
on it but it turned out that it was just
too big and too expensive and too heavy
you know and we should have built
something much smaller so
we probably spent a year you know
developing that thing and
never went anywhere and that was also a
case of
not doing a very good job of listening
uh to uh to our customers in our market
there was a earlier at animation
we had high hopes for mobile robots
and we developed a platform that had
these
uh omnidirectional wheels on it uh back
in the uh i don't know 787
probably 1981 time frame and um
so it had these three wheels and it
could move in any direction and we put a
robot on top of that and it had a
portable
you have battery power and it could go
recharge itself and we had infrared
sensors on them that could navigate
around and they could
move around the factory and the idea was
that it could go service intermittently
um you know stations that there were
process stations that
needed to be loaded and unloaded uh you
know every few minutes uh but then
that you could move the robot around and
we thought that would be quite
interesting you know for the
semiconductor industry and and we had
talks with ti and other you know
semiconductor companies about it
and they said yeah gee that that is
interesting uh but
it wasn't interesting enough that they
really wanted to buy it um
and so we put probably
yeah a few years of research and into
that and you know made some nice videos
about it and but it was probably 30
years too early
it just there just wasn't a real
viable commercial market for it yet and
today there are lots of
automated vehicles moving both overhead
and on in to some extent on the floor of
semiconductor factories
uh but as semiconductor ultimately
evolved most of the um
transportation is done on overhead rails
uh to keep from cluttering up the floors
and a lot of the floors also are these
clean room floors and
uh it's hard to put heavy loads on them
sometimes
so you mentioned that um with the first
beat
a depth two that you weren't listening
to your
to your market right um how do you
usually
kind of integrate the voice of the
market
into your work well you know there's
there's it's always kind of a fine line
um because if you just ask the market
what they want they'll tell you sort of
what you know
they wanted for yesterday and if nobody
else is making that that's you know an
opportunity
you can go do it but usually you try to
shoot a few years ahead of you know
because it's going to take you a few
years ahead and you don't uh
to do something and then you and then
secondly
um you'd like to when you introduce
something have something that not you
know 10 other guys
are doing exactly the same thing um
so you know most of these um activities
involve some um
balance between trying to
forecast you know what's going to be a
successful uh product in a couple of
years
and doing enough homework that um
that it actually is you know when you
get it out there
we knew uh for the adept 2 that again
for small part assembly that people
wanted to go real fast
um and you know we'd had and they wanted
to get you know higher throughput and
you know do sub 1 second cycle times and
things like that
we thought that um they wouldn't mind a
great big old footprint that was that
time about 14 inches in diameter for
moving around these little tiny parts
but
that we sort of blew it on so
you know and if you take you know some
people if you listen to what
you know people like steve jobs will say
they say well you know i've
envisioned the future you know and we go
create you know the future
and if you're really really good and and
and if you time things correctly
uh you can do that uh but even companies
like apple then they had the newton
which was you know if you remember it
probably 25 years ahead of its time and
it was a horrible flop
um but seemed like a good idea at the
time and so
sometimes these things almost have to
wait for the market to come around
uh i have believed for many years in
using machine vision to do what we call
flexible part feeding
and i flogged that for probably 15 years
at adept and
it's starting finally now to to uh gain
more and broader acceptance
it's not something we're doing it
precise but there are some companies out
there
and vision-based part feeders are
becoming much more common now and much
more accepted
so sometimes you just have to kind of
wait for the market to come around
okay okay so how has the business of
robotics changed over the
years well robotics has
started off in the 60s and 70s
and even 80s and to some extent even
today
has driven uh to a large extent by the
automotive industry
um probably 50 to as much as
70 of the volume certainly in the early
years uh
with spot welding and heavy material
handling and so forth
and as such um the machines that were
developed were these are very large
very powerful machines
and the
technology that was developed was
primarily uh targeted towards just a
couple of application areas
um i think today um
and then as there was as
the 80s came around we got into these
you know robots that would do smaller
uh things such as we developed an
animation and then later to adapt and
and uh now it precise and so
the robots have gone into new markets
where the payloads were smaller and
lighter
and and so forth and secondly i think
the um evolution of computer technology
now
particularly in the area of machine
vision has let the robots
work in some robots and
and other automated machines to very
high levels of precision and almost
microscopic environments
and so uh certainly a lot of what goes
on today
in the semiconductor industry and
electronics industry would not be
possible if it weren't for the
integration of machine vision and motion
control all of your
circuit board assembly you know if you
put together a circuit board you have
these little surface mount devices on it
and
they all have to be lined up very
precisely and
things we mentioned earlier in photonics
you may need to line parts up to a few
microns well you just people just can't
do that
so i think the um we've seen
the technology spread out into a number
of areas disk drive and electronics and
similar things now what's starting to
happen is
we there's been a lot of work in the
last uh five or ten years on um
trying to deal with much less or
completely unstructured environments uh
you
are probably familiar with the darpa
grand challenge you know over the last
few years in which
it was essentially a machine vision
exercise in terms of
navigation well sensing
modeling building up a model you know
from these laser range
scanners and these vision systems
planning
based on that model um and uh and
navigation um
and so uh that was considered
an almost impossible thing to do you
know 15 20 years ago and then
uh in the last couple of years people
are driving you know
not people i should say uh completely
automated systems are
driving automobiles at 30 40 50 miles an
hour
you know through the desert and then
more recently in a city environment
so the what's happening now is we are
beginning to develop the uh technology
uh to build models uh to um begin to
reason about models
to put in you know these heuristic sort
of rule-based systems is to
tell you what to do and when various
things happen
and as a as a result of that
we will see a whole lot of new
technologies
become possible and it's it's
technically possible or will be in the
next couple of years to have your car
drive you to work
you know you can go get in the thing and
say take me to work
and uh it'll do all the navigation and
you can sit in the back seat and play
video games
or sleep um it is
possible today to have you know
drones flying around in the air doing
military applications or
underwater uh in which there are no
supervisors um
uh and they they get instructions on a
periodic update
basis and
it will be possible uh in a few more
years
to give in relatively high level
instructions
for relatively mundane jobs you know go
pick up the dishes off the table and put
them in the dishwasher that will be
possible in the next you know 10 or 15
or 20 years
so and that is all based on this
integration of
modeling reasoning uh motion control
sensing uh and so forth so that's that
that is going to uh
allow the technology to just laterally
um
uh explode into many many different
areas it won't be necessary for ups to
have human pilots flying
you know cargo planes around anymore uh
it technically wouldn't be necessary
to have human pilots flying passenger
planes around but i think they probably
will be for the
foreseeable future um there's no
rational
reason that um other uh no real rational
reason that you ought to have human uh
pilots and fighter planes anymore
um that's we spent i don't know how many
hundreds of millions of dollars for
these incredibly advanced
expensive you know fighter planes and
you could you know fly a drone missile
around
for with much higher performance um for
a fraction of the cost
so i think that uh
we will see the technology be used in
many ways um some of which will be scary
some of which will be
uh you know really enhance people's
lives and add value to the society
what do you think the reason is that
these things
aren't being applied yet or won't be
applied as quickly
as they could technically be applied um
lots of people have done studies at the
rate of
acceptance of new technologies into
society um
and the sort of general rule of thumb is
it takes about 25 years
for a brand new technology to sort of be
broadly accepted
you know in society whether it's a
personal computer
um whether that will accelerate or not
is is not clear but
um but certainly uh even things that we
view as being ubiquitous
uh or almost overnight sorts of things
like cell phones
you really look at and cell phones it's
been about 25 years from the time the
first you know
fairly big clunky cell phones were
reduced to the time that
you know over 50 percent of people
actually have a cell phone um
so there are these sort of you know
rates
uh that uh are in some sense uh driven
uh by um the ease of use of the
technology when the things are big and
clunky and heavy that you know they're
not that much fun to use
uh in some sense uh driven by uh cost
and economics uh
uh over time there's a learning curve
and they get cheaper and more affordable
uh and in some sense driven by uh
generational acceptance rates you know
your kids usually know more about
computers than the adults do
um and so you have uh kind of this um
this rate at which technology or society
is able to uh
to absorb uh these uh these new
technologies uh so i think that's
certainly one issue and then i think um
there are well
there are today and will continue to be
cultural issues
uh in which we need to decide choose and
figure out how we feel
about completely automated systems uh an
example of that at the moment is the um
debate about you know should we have a
manned mission to mars
um you know it's uh
emotionally we want to send people to go
walk around on mars
it makes very little scientific or
economic sense to do that
um you know these robot missions these
little
uh mars rover that ran around for you
know a year and a half or two years and
brought back all kind of great
information
and uh it was a terrific success um
you know as a far more efficient way uh
to go about exploring other planets
uh than trying to get people there and
back and and all the water and all of
the infrastructure and all the
psychological issues of being you know
locked in a box for two years
um you know but still emotionally we
have these things
where people should be doing certain
things whether it's exploring uh whether
it's driving your car
we want to be in control we want to uh
lead we don't want to
give that up so for certain applications
i think that will continue to be a very
strong
driver will you will you be comfortable
having the robot drive you to work are
you really going to want to have your
hands on the wheel
in the 70s and 80s was there a lot of
sort of social interest and the problem
of
robots displacing workers and how did
that relate to your business
there certainly wasn't the in the late
70s and early 80s it was there was the
perception that you know robots were
going to
displace all the workers in the factory
and that everybody's going to be laid
off and and
every time a robot came in you know
people were saying hey you know there
used to be a guy
doing that spot welding on that job over
there now now the robot's doing it
that sort of concern slowly faded um
yeah
i mean it never you know went away 100 i
don't think will i mean if you replace a
job you know with a
machine or some kind of automation
whether it's a
operator working at a switchboard i mean
there are no switchboard operators
anymore
uh or you know farmers working in the
field you know with a combine harvester
i mean
uh those things will happen to a certain
extent
um but they will only happen if they
kind of make economic sense in business
sense and social sense and if
if people feel too uncomfortable about
it they won't allow you know
those machines to come in and i think
that
the the bigger question is is whether
the economy uh
uh and the the industries um can't
adjust and remain competitive and
continue to grow
and provide opportunities for people uh
as the work changes
or whether the the the jobs simply
vanish
and there there have been for some time
arguments about people have written
books about the end of work for example
and but that has yet to come to pass um
and uh i we but we have migrated from an
agrarian command you know uh
an agrarian economy where
you know ninety percent of the people
were involved in farming you know
to a manufacturing economy and and now
more to a service economy so at the
macro economic level you see sort of
these
long-term trends and but again they tend
to have to take 25 years
50 years while these things happen so
there's almost nothing that happens
overnight
there's also curious you've done a lot
of work with
u.s companies but have you what about
other international
connections yeah well we've all you know
robotics has always been a very
international business
um you know it started the first robots
were you know developed in the united
states
but relatively quickly licensed to japan
and then japan today you could say is
probably the leader in terms of
accepting implementing innovating
in many robot areas
many other countries obviously are also
very involved in robotics certainly
europe has a huge effort going on and
then
if you actually look at the markets
today probably
um japan and asia is the largest market
followed by europe followed by the
united states and so
the united states is actually the
smallest market in the world markets for
robots in terms of acceptance um
that's driven uh uh for a couple of
reasons one
the united states has been uh relatively
quick and relatively uh
accepting of just contracting out
manufacturing so instead of trying to
keep manufacturing here in the states
and and
keeping some jobs while you put in some
robots we sent it all to asia
um europe conversely has done a lot of
investment in manufacturing technology
and has put a lot of
robots and other automation and
especially in germany and
france and to some extent uh spain they
have been
much less ready to just export their
manufacturing
um japan of course is used
in robots to gain a manufacturing
advantage and
as have uh korea and some other
economies and so there's sort of that
kind of cultural thing going on uh with
respect to us
um at uh you know i've been to japan
probably 30 45 40 times
at adept we had an office in japan we
sold maybe eight or ten million dollars
a year
of mostly controls and but some robots
over there
um at uh precise we have some customers
in japan we have a number of customers
in korea
we are just now starting to set up a
reps and distribution in
singapore and southeast asia
we have also some customers in europe
and i've been to europe many many times
and
adept actually forty percent to uh
the fifty percent of a depth of business
was in europe a debt had
we had offices in germany france italy
uk
um spain um
and so uh i think they've had four or
five uh office offices uh in europe and
very strong presence there
uh so it's always been a very
international business and and when you
go to conferences and
uh you know they're all over the world
and i certainly know people all over the
world in the industry
but not only in the industry but also in
the research community
have you had close collaborators that
were from other
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other parts of the world well in some
cases um in um
we've done um some research work with uh
folks in korea
um we had a a program that we developed
um
there that uh i can't really talk about
too much um
we've done um uh
other projects um uh with um
you know with people in europe um the
when you talk about collaborators
there's sort of you know two ways to
there's within your company and so
there's you know your your people in
europe that are working on things
uh and then there's a sort of um outside
of your company um
with other entities uh in the business
uh community uh we've done
at least a precise we've done work with
a company called shunk which is a they
make grippers
and then they've more recently been
working in some service robot things
we've done
control programs with them and
cooperative uh
develop some controllers and so forth
and so they're
using some of those in their business in
europe right now
we haven't participated in
sort of international fundamental r d
programs
we don't really have the infrastructure
to do that here at precise
at adept uh we had some discussions
about some of those sorts of things but
never really got involved and they tend
to be
long-term and complicated and difficult
to get through the political process and
so forth
you mentioned uh conferences what were
some of the conferences
that you've gone to over the years and
especially in
the earlier years uh well uh
i've been a member of you know various
uh organizations and and president of
some
um so one organization with the robotic
industries association uh the ria
and um they have also a machine vision
association
uh there or automated i guess they call
the aba the automated vision association
or something now
but we were members of that i was on the
board of directors there i was the
president of the riaa for
three years and um
then um i've also been a member of ieee
uh for
uh uh quite a long time um and uh yeah
so i'm a
fellow of ieee and um uh and then um
so i've attended uh the uh the icra uh
conferences uh
some of the other ieee conferences the
robot industry association conferences
i've chaired some of those conferences
uh osama katib who's a professor at
stanford and i co-chaired the ieee icra
conference in 2000 in san francisco and
so that was
eleven hundred attendees um and uh
all over the world and and um i was the
general program
uh general chair and assam was the
program chair so
and that was a pretty significant chunk
of time to kind of
pull all that together and uh host
everybody we had that was so
well attended had a good time with that
um
and um i've been to conferences in asia
and i've you know given talks you know
all over the world so uh
i've uh sort of participated in the um
in the research community and then and
uh and kind of the
uh conference uh professional societies
um
you know for some time and what did the
robotic industries
association form
i would say uh the late 60s i don't know
the exact date
but i would say you know 66 67 68 back
in there somewhere
you could ask you could call them up and
ask them but uh yeah so they're
already around when you're starting at
your company yes you know they they were
started um
partly by joe engelberger joe
engelberger had started you know
unimation
um in 1960 or so and
developing these big hydraulic robots so
by time we came on the scene which was
the early to mid 70s
there was this robot industry as i
mentioned but it was composed primarily
of these
are consistent primarily of these uh big
hydraulic machines
and the robot industry association was
started
uh i don't know how many members there
were in the late 60s but
it was at that time maybe half a dozen
companies that were you know
doing robots and they had a staff person
and they
started having some conferences and some
robot shows and some things like that
i'm curious um this is a slightly
different direction but
with the development of robot industry
you mentioned this
kind of uh like growing spreading out of
the industry and part of it
obviously is industrial robotics but
then now there's a lot of push for more
service oriented robotics
um how do you see that as developing in
the future
is that a reasonable direction to go in
or
well i think you will see a lot of
service robots i think the
um and they will start in
environments in which they're not
touching people very much
so you will have you know your cleaning
and we have already you know vacuum
automatic vacuums running around the
floor
um you will then you know start to see
some
cleaning and service and maintenance
kinds of machines that are
doing uh you know intermittent periodic
kinds of
maintenance um um
i think that um there are today
um you know some robots that are washing
airplanes and you know and and uh
working in in some things that are
relatively you know difficult there's
obviously underwater you know sorts of
maintenance activities and a lot of
teller operated stuff
going on there um
there have been maintenance activities
in hazardous environments
for many years including nuclear
reactors and other hazardous
environments
now starting to see robots in mines and
mining applications and
and then i think a lot of time when
people talk about service robots they
think about you know something in the
house
uh or possibly um uh helping the elderly
or um
uh or possibly working in medicine and
so
when you get into those environments um
certainly i think the the early
applications again will be ones which
don't
provide a lot of physical contact with
with people
but you may have cleaning robots they
may be more sophisticated than the
vacuuming things
you may have some outdoor uh maintenance
sorts of things um
on the gardening robot um uh and we will
start to see eventually then
um and there's a lot of research going
into
sort of inherent safety how we get
robots to interact with people in a safe
safe manner there has been
interest in and lifting you know in
hospitals you know how do you have a
nurse's aide that can come in and help
turn and lift and
reposition you know a person who's not
unable to do that themselves
um there have been uh you know certainly
work in um
elder care um both in terms of
sort of very simple things like um you
know reminding someone to take their
medicine or
doing some monitoring and and
determining if they're
healthy and then people are trying to
build these structured environments uh
in which the robot can go cook a meal or
you know go get you a beer
but when you get into the details of
elder care there's a lot of
how do you take a bath um you know how
do you go to the bathroom i mean these
very personal sorts of things
that the robot technology isn't really
quite there
uh yet and may not be for a while um
that's not to say that it won't ever be
[Music]
there are mixed schools of thought as to
whether
people would prefer to live
semi-independently with a robot aid
or go into a community where
they have other people around and maybe
get
intermittent visits you know from a
caregiver of some sort
and um different people may just
have different psychological makeups and
prefer you know uh uh different
approaches but some people may just want
to be very independent and whatever
don't ever want to go into a home and
other people you know like a sense of
community so
that may turn out at the end of the day
to be kind of a sense of
personal choice but those those sorts of
issues will become
um the interaction
the safety and the psychological aspects
of
you know do we make friendly robots do
we make robots that try to mimic humans
do we make robots we can have
conversations with
um and what does that really mean
in terms of society psychology and
social structure
those will come up in the next you know
20 to 30 to 40 50 years
there's been a lot of work more recently
and
robots trying to read and interact with
with human emotions
so mit and other set programs of
robots looking at the human face and
figuring out whether you're smiling or
frowning or happy or sad and trying to
respond to you
in an emotive manner
and that
is you can debate you know you know how
how uh good that is but but certainly uh
the more responsive if we're sitting
here interacting and you're nodding or
smiling or something
it's uh certainly more rewarding to have
an interaction with
something that's a responsive entity as
opposed to talking to a computer screen
so
uh or a microphone so i think that you
do
elicit more natural responses in people
um
whether people ultimately decide that's
uh the way they want to interact with uh
machines uh really uh is open to
test a time here so do you think that
the i mean is it
really the social cultural challenges
that are the big
problem there because in japan
particularly they've been trying to
develop different applications for these
robots
that are either more you know social
or somehow exist in the in the home or
in the general society but it's
it's not worked so far
well you know the japanese have this
fascination this historical fascination
um
there was a guy that wrote a quite a
nice book uh that uh
i have a copy of in the other room that
uh sort of dates the history of robots
um
uh he wrote this about 15 years ago but
yeah robot inside the robot kingdom yeah
i met that
fella and uh interesting guy but um you
know
he goes back and talks about kind of the
japan's cultural
uh historical fascination with puppets
and uh
automatons and then cartoons and then
anime and then how all that ties into
robots and how there's this whole
thing in japan about these sort of
animated and cultural
characters and so
that fascination has led the japanese to
try and go and develop these very
humanoid you know type of robots
even though they don't do anything
useful so you can see these things at
trade shows and
honda has asimo that sort of you know
clumps around and
and um but what they
uh and uh toyota actually built a
marching band with robots playing
musical instruments and
uh you can imagine the amount of effort
that went into into something like that
um unfortunately um
all of that anthropomorphization if i
can
get that out
has almost been counterproductive in
some sense because people see these
things they expect them
to behave like a human or to have the
capabilities of a humanoid
uh and they really don't they they none
of them have
the the sensing systems the modeling
systems the control systems
uh even the natural language
understanding that you can have a
conversation with them and tell them to
go clean up the kitchen you know
the dishes and put them in the
dishwasher so
there's this wish or this
idea that we're going to have these
humanoids wandering around
and it's to some extent interesting as a
mechanical engineer to
to watch uh companies that develop and
and try to improve mechanisms and so
forth to
be able to uh have these uh
multi-fingered hands that can pick
things up and things that can walk
around
and work in these less structured
environments but we still have a long
ways to go from a control standpoint
japan and and they're trying to get
leverage i mean i think
stanford is i don't know if they
actually have it now or not but they
talked with honda about getting some
azimo robots at stanford and um
um trying to get you know more controls
people involved in uh
in running these things so you know it
will perhaps slowly uh
evolve over time the other point that i
would make is that
from the perspective of a mechanical
engineer we're still
extremely inefficient in how we build
these
mechanisms relative to biological
systems uh
a uh if i'm sitting here sort of you
know chit chatting with you guys and
running my little brain here
i consume maybe 100 watts of energy and
if i'm you know pedaling hard my
exercise bicycle
i might you know get up to 125 watts or
150 watts of
of energy that i'm putting out uh so
maybe you know a human
running at full speed or riding your
exercise bicycle is consuming you know
200 or 250 watts of energy well
you look at a at a robot or something
like asimo and it's consuming kilowatts
of energy
and the battery you know runs out in 15
minutes and the thing falls over
um you know we're nowhere near as
efficient in our mechanical
systems as as these biological systems
are
and if you look at something like a
chimpanzee which weighs about you know
probably a little bit less than i do but
as chimpanzees its muscles are six times
stronger than my muscles the chimpanzee
can chin itself easily with one
hand and it can you know pop my fingers
off one by one i mean they can pull
thousands of pounds you know with what
they're with their arms the muscle
tissue is six times stronger than human
muscle tissue so so so sort of where we
are in terms of power weight ratios and
efficiency of
designing these complex machines
is still quite poor relative to
biological systems and so
uh we don't want to build a bunch of
stuff that's incredibly inefficient and
sucks up all kind of power
and you know runs the environment out of
resources um you know uh
either so so there's there's other
issues uh
as to you know how quickly this
technology will develop