Saturday, July 21, 2012

Crossing the Chasm: will consumer robotics ever do it in my lifetime?

When will a consumer robot cross the chasm?

 What will be the first consumer robot application to bridge the chasm between early adopter and the early majority?

This, I believe, is the central question this consumer robotics must answer. Crossing the chasm means selling a product not just to enthusiasts, but to the early majority. I submit for your consideration that consumer robotics has NOT been able to jump the chasm yet, and the whole industry is stuck, and is of no real interest to major companies like Google, MS, Apple etc... those with companies with huge stacks of money and who have to power to transform this market. While MS has shown leadership in this area, it is the sole standout in the crowd.  See the article by BG : A robot in every home.

What could that first product be that jumps the chasm? There are so many possible areas where consumer robotics can grow. Obviously any new field must be technically in reach within say, several  years, it must address a pressing need. So, will it be Telepresence? Security? Intelligent toys? Elder care? Maybe even a nannybot? Or the famous "get me a beer robot" (and is the 'get me the beer robot' an expression of complete lack of imagination in the publics mind as to what robots can do for us).

Military robotics, lead mostly by iRobot, has been  able to bridge the chasm, and now we see the widespread use of robots in the military and police forces. Lego Mindstorms, may have done it (or is in the process of doing it) with education/hobbyist robots. Intuitive surgical has transformed surgery. What is wrong with consumer robotics? Why should anyone be enthusiastic about it as a business opportunity?

Here is some background to the crossing the chasm problem: "Crossing the Chasm: Marketing and Selling Disruptive Products to Mainstream Customers", Geoffrey A. Moore

Here is what I think is standing in the way.  We are stuck with the notion that consumers will not pay more than X dollars for a robot. People are focused on that X number because there is a lot of data to support the idea that $100.00 for an entertainment robot is the max and around $300.00 for a utility robot is a maximum people will pay.

Yet, people also have cars, which cost a lot more. Why? Because they deliver something of unique value to the consumer. People will also pay a few hundred a month for cable. Why? Because there is a constantly renewing source of entertainment.  TV is part of our culture.

What we need to think about is delivering VALUE to the consumer and let the price fall where it may.  What is the pain that consumers undergo that a consumer robot can alleviate? 

The first step, I think, in performing a methodical analysis of this question is compile a pain-list of consumer activities. 

Then, the second step is to determine the technical readiness of robotic technology to address each of these pains.

It is only through a methodical analysis of this problem are we going to get anywhere. Building stuff and seeing what sticks has not gotten us very fare.  Just my 2 cents. 

Tuesday, July 17, 2012

The case for neuromorphic engineering

Neuromorphic engineering... trying to build computers that are more brain like... has become mainstream. Companies like Qualcomm are hiring neuromorphic engineers. Major companies like IBM and HP have DARPA funding to build more brain like computers.

But, the question is, is there really any benefit to using neural style computing versus good-old-fashioned CPUs which have gotten us oh so very far.

For neuromorphic engineering to advance further, this question has to be answered crisply and definitively.

Before we can answer that question, we have to ask: what do we want to use the computer for? I can buy a calculator that costs less than $10.00 at my local drug store that can add, subtract, multiply and divide far faster than I can in my head, and with much greater precision!  I feel thick witted when compared to the simplest pocket calculator.

And there are a lot of places where being able to "crunch numbers" quickly is very important. For example, financial calculation, rendering graphics, and designing complex machines. So, lets not dismiss the obvious benefits of these machines.

However, that is not all we want computer to do. As we begin to attach computer to the real world by adding sensors and actuators-- essentially making them the computational core of robot-like machines, our computational needs are changing.

Here is where our world of computation turns upside. Humans excel at interpreting large volumes of data, ignoring what is not important, and emphasizing what is import. We attend to that which is relevant. We have also come to realize from neuroscience that the world we live in is mostly in our heads, in the form of models which we can use to reason with, but that are grounded in the real world.

Now, the interesting thing is that as our algorithms begin to resemble, more brainlike computation, we find that these algorithms cannot run efficiently on CPUs designed for computing balances on checking accounts. A new kind, of find grain parallelism is needed to handle the fire-hose of data that is flowing into these systems. As we drill down, we find that with this fine grain parallelism, in order to create efficient machines, we need to colocate computation with memory. If memory and processing are kept separate we need to have lots of long connections with use power and generate heat. Adding the local ability to adapt helps things as well. A global adaptation scheme just can't send enough signals to enough processor to keep up. This leads to extreme decentralization of computing.  In the end, we end up with a collection of highly parallel, computational elements with local learning, and local data storage. We end up brainlike.

And here is the real kicker. The reason why we end up designing computers like this is to save power.  Now this gives us the idea that when we look at real brains, we should also be considering how limited power and cooling capabilities shaped the creation of brains. How efficiency may have given rise to the partitioning of the brain into distinct functional regions.

So, the case for neuromorphic engineering really comes down to not necessarily computation, but the practical issue of how to host that computation most efficiently on a physical substrate. Since both brains and silicon inhabit a world with real consequences for their organization, the strongest case for neuromorphics is going to be made on the basis of power.

Sunday, July 8, 2012

Most Biologically Accurate Model of Human Walking

The press release a couple of days ago the work that Theresa Klein and I did has really caught the world's  imagination.  It also caught me off guard. My cell phone started ringing at 7:00 am the day before the press release with people asking for interviews.  It was fun, we were covered by the AFP the Paris news agency, and BBC Radio up all night... and various other places.

Before looking at the video, which you can see here, I would recommend either reading the article. Or the summary of the results published at the National Health Services in Britain here.

The point of the work was to create a physical model of the walking system in a human being. That includes getting the biomechanics right. We had to build a system with  essential elements of the biomechanics of the lower human limb. That alone was difficult starting from scratch.

We used a 3-D printer from Dimension. It took me about 5 years for my students and I to master building robots using a 3-d printer.  There were a lot of techniques we learned on the way. One day, I hope to publish all of the tricks that allowed us to do this.

Next we had to build muscle like actuators that used tendons to pull on the limb. Easy right? well, we had to sense the force in the tendons. Theresa went through many iterations until she invented a sensor that was accurate but durable enough to be used in a robot. When I say many I mean many iteration. Robotics is nothing if not about persistence.

Theresa then had to experiment with building neural circuits. Typically, neurorobot in the past have used dynamical equations that produce oscillations... not really neurons... we used Izhikevich spiking neurons which we felt are much more like what you would see in the true spinal cord.

I have been working with building spiking neural networks for some time now. We have even built a series of ASIC chips that implement the dynamics of neurons in collaboration with our colleagues at Johns Hopkins. We have used those chips to control movement in animals.

What we needed was a platform to figure out how the entire neuro-mechanical-sensory system interacts. So, if we one day want to restore locomotion, we need to have some basic understanding of walking.

Now, why is this important to science? Biologist collect lots of data about the bits and pieces of locomotory system, but they don't know if they put all of the pieces together if the system will actually work as in a human or animal. They can never know if the elements they have uncovered are both "Necessary and Sufficient."  That is where robots can really help.

So our work does just that, we took what we knew about biology then had to fill in details where details were missing (think Jurassic park :-)) . And we were able to get walking using ONLY suggestions from biology. No gimmicks.

So, that is why I think the work is cool and it is relevant SCIENTIFICALLY.

Now, a lot of people have compared what we have done to PetMan. And one commentator implied that we where trying to play "catch up."

Well, Petman has been extremely well funded, and had practical, engineering goals. We did our work on our own dime and some funds provided by the University of Arizona (thank you!).  I am sure the ratio of spending is something like 1000:1. To be perfectly honest, initial funding for the core concepts behind this  work was provided by Tom McKenna of the Office of Naval Research years ago. He got me going in the direction of building biologically inspired humanoids. Dr. McKenna has been the most important force in legged locomotion over the past several decades in the United States PERIOD.  Now Gill Pratt at Darpa is really making a push, but I think Dr. Pratt's charter is more towards building systems with direct military importance. Perhaps I can persuade Darpa to do a neuro-prosthetics program for the lower limb like they funded for upper limb prosthesis. that would be cool!

Like I said, funding is hard to get for this kind of work. In this country engineers don't care about the biology at all. And biologist are skeptical of "engineers" encroaching on their turf competing for very limited funds in biology... which I totally understand.  Really, only a handful of places around the country have been able to make a living in this biologically inspired walking robot paradigm. I would say the oldest program was  at Case Western Reserve and that work is still being continued by Roger Quinn and Roy Ritzmann and colleagues.

But, non-the-less, Theresa and I  felt this work was important and we put our own money sweat and time into building it. She preserved for years... I am proud of her. Unfortunately, the experience left her jaded about research.  She had fantastic opportunities to continue this work when she graduated  and a possibly brilliant research career ahead of her but, she decided to go into industry, ultimately.

It is a loss to science.

It is telling that most attention to this work has come from overseas... internationally... where I think they understand interdisciplinary research a bit better.

But, I think I will continue this work somehow.  I have gotten many letters from people and seen many comments on sites that indicate that people see this robot as hope that one day we will be able to restore locomotion in people with spinal cord injury. Of course,  the robot we built is just a tool for understanding. We still need the medical experts to make it real.