How does this robot know it has arms?

This video was linked to on Slashdot:

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In the video, the robot learns how to walk. How does the robot know it has arms in the first place? Did it likely have to figure this out (perhaps prior to the video), or is it likely explicitly programmed to move its arms randomely at first and go from there?

Along the same lines, how does a baby figure out it has arms and legs? How does it figure out that they can be used for locomotion -- by moving them around randomely at first?

I'm very curious...if anyone has any input, please post.

Reply to
Chad Johnson
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Well, I think that question shows how the approach taken by this project is not general enough to explain how we learn.

Beyond the question of how does it know it has arms, are all the other general question of how does it know that it's even in a 3D environment and how does it know there is gravity holding the object to a flat surface?

All these things seem to have been programmed into the model by the programmers instead of being learned by the robot. That is, without studying the research, I'm guessing the basic parameters of the model were selected by the programmers - such as the fact that the unit was a block with arms existing in a 3D space trying to move on a flat surface. As such, only a few very specific parameters of the model were then searched, like the length of the arms, and the number of joints. So most of the details of the "model" were selected by the programmers and only a very small part seems to have been learned.

I don't think anyone knows the answer to that question. The starfish project is just one of many exploring ideas of machine learning to try and help us better understand just how humans learn these things.

I happen to lean towards a belief that we have far less knowledge about our environment built into us than the starfish model has built into it. I for example don't believe we have any knowledge built into us about the 3D nature of the world, or that gravity exists, or that we have "arms" and "legs". What is built into the brain is lots of sensory inputs and effector outputs coming from eyes, and ears, and going to arms and legs. The brain doesnt have to "learn" that it has sensory inputs and outputs, that's just built in. What it has to learn, is how different signals on the effector outputs, sent based on the context created by all the sensory inputs, is likely to lead to pain and pleasure events. So what the brain learns, is how to react to current context.

I believe all our "understanding" that we have "arms and legs" and that we live in a 3D environment with gravity holding us to the ground, is just the result of learning in complex detail, how to react to our environment in ways that maximize our long term pleasure.

What I believe the brain is actually modeling at the lowest level is expectations of rewards. So as it produces different behaviors (generates pulses in different parts of the brain), it's able to use its model of expected future rewards to test and evaluate each behavior as it happens, without having to wait for real rewards. In psychology, this modeling of future rewards is seen as secondary reinforcers. For example, a baby will learn to see it's mother as a predictor of future rewards (like when it gets milk). So even though the actual tasting of milk is the real reward, the brain learns that simply seeing the mother is a good predictor of higher future rewards (high odds of getting milk soon) and not seeing the mother is a predator of lower future rewards. As a result, any behavior which allows the baby to see the mother, acts as a secondary reward for that behavior. As a result, it learns to turn its head, and eyes, and keep them focused on the mother when the mother is around - because just looking at the mother has become a reward in the world-model the brain has created.

So the brain learns how to create output signals, in response to the current sensory context, to maximise rewards. But to do so, it must create some type of general understanding of what the sensory signals means. It must for example build signal classification circuits that allow it to recognize that different images of a dog, is the same dog, and not just different dogs. And at the same time, it must learn to recognize that different images of our right arm, is the same right arm. It must do this, because when it learns how to respond to a dog, it should respond the same way every time it sees the dog. even if the current image of the dog (the dog butt) is very different from the last image seen (the dog face).

So the other part of the problem the brain solves is building these classification circuits to lump together different sensory data patterns as being similar so that when it learns how to react to one pattern, it will correctly use that same reaction for other sensory patterns that represent the same thing. Until it can classify different sensory patterns as being "my right arm" it won't understand that it's right arm is "one thing" instead of 1000 different unrelated sensory patterns.

Again, no one knows exactly how the brain does this. But many people have been working on algorithms to classify sensory data in attempts to understand and duplicate this power (such as all work done on pattern recognition).

Some people believe a lot of this power is hard-wired into us by evolution. I suspect the brain is more general purpose and uses temporal clues to build these classification circuits. I think that the closer in time different sensory patterns tend to happen to each other the closer they become to being treated as the same in the processing circuits.

For example, if you have a simple sequence of single digit numbers as a sensory input, we might see sequences like 128457623745. In this small example, 5 always follows 4. If this trend continues over time, the processing circuit could start to classify 4 and 5 as being the same thing. It would become the single item of a "45" pattern. Once the 4 showed up, the system would classify it as the "45" pattern, even before the 5 got there, simply because 4 was such a strong temporal predictor of 5. I think this is the key to how the classification circuits work. They use temporal prediction to shape the classification circuits. If one sensory pattern (across multiple inputs) is a strong predictor of other patterns, then all those patterns get lumped together as being "the same" thing. We learn to understand that our arm exists, in terms of all the sensory patterns that are created by our arm (like what our arm looks like in different positions) because each is a strong temporal predictor of the others.

So what the brain has to start with, is its built in effector outputs, but it has to learn that there is an arm in the environment by analyzing the temporal sequences of the sensory inputs. And the reason we learn to see the arm as part of us is because that what happens to it can directly create pain and pleasure in us. If we hit our arm with a hammer it produces pain, but if we hit that log with a hammer, it doesn't produce pain - so the log is not "us" but the arm is.

People that have a disease which cause them to loose feeling in their arm have the problem of forgetting that the arm is really part of them. They are more likely to use it as a hammer to hit things with and not care if it's getting damaged because they no longer see it as being part of them - it's just a hunk of flesh attached to them that's not doing them much good.

If you could put sensors on your car body, and wire it into your brain so you could directly feel when things touched your car, and so that you could feel pain if things hit your car too hard, or if the engine overheated, your sense of "self" would probably start to expand to include your entire car.

Reply to
Curt Welch

I think the scientific evidence leans in the opposite direction. People are born with a lot of inate knowledge about the physical world. Here is an experiment you can do for yourself: next time someone lets you hold their newborn baby, let your arms drop a few inches so the baby is momentarily in freefall. It will react with fear. We don't learn to be afraid of gravity, it is instinctive. Warning: They will not let you their baby a second time.

As far as knowledge about its body, I don't think there is hard evidence that babies are born with knowledge about their limbs and joints, but many other animals clearly are. Horses, chickens, etc. are up and running around very soon after being born. They don't learn to walk and run, they are born with the knowledge.

Reply to

According to my observations (three children), they do it by observation.

When very young, a child is apt to flail sometimes when excited, and when hit in the face by one of its own arms, to look startled and perhaps cry, in a normal and otherwise-observed startle reflex. I've seen this at least twice quite clearly. They have no idea the arm belongs to them.

Later, there is a definite observable time when they start to watch their hand while concentrating on opening and closing it - they've just realized they have control of the thing. They practice this control by watching it, i.e. hand-eye coordination begins.

As far as standing and walking, my oldest was quite unusual - he free-stood off the floor (before ever climbing up on furniture to any degree) at 5 1/2 months. Within a few days, he was clearly trying to work out how to get one leg off the ground to move it without falling down - a step. IOW he wanted to walk, and knew what he needed to do to achieve it. The first step took however 2 1/2 months - his feet were just too far apart and his legs didn't have the muscles until he was 8 months old. Within 4 weeks, he was running! We spent most of the next decade running after him :-). Anyhow, this was just to point out that walking was clearly an intellectual achievement before it was a physical one, and I'm guessing that standing was the same.

Clifford Heath.

Reply to
Clifford Heath

I think this is a very wonderful bit of observation!

My thinking on the subject is similar. Children experiment to find what they can control and what they cannot. I've heard discussions about them discovering their toes, for instance.

A quick Google search shows:

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"Watching our children discover new things is one of the great joys of parenting. First, they discover their toes. Then come bubbles and butterflies, books, bicycles and banana splits."

But one of the things I think they discover first, is their "thoughts" are somehow connected to getting what they want.

Want to see a grown adult make a baby face? Tell them to cause something "tele-kenetically". "Hey Joe, try to flip that wall switch over there without touching it." Watch him close his eyes and scrunch up his face. Maybe reach out in empty space, and make a groping motion. (If you need a visual, in short, "Do the Yoda".) Basically all the same moves as a baby trying to will a meal into its mouth.

How does this fit with robotics? We're still trying to move things with our minds. Programming robotics may be a latent tendancy trying to control things with our "thoughts" back like when we thought we first figured it out.

-- Randy M. Dumse

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That's interesting. However, I don't think the experiment really supports your position.

What it shows, is that a fear response is innate (you didn't bother to explain what the response actually was, or why you feel justified to call it a fear response), and that we have some sort of innate drop sensor wired so that it triggers the innate fear response.

This is a large step away from the concept of gravity which seems to be hard wried into the starfish robot model. As far as I can surmise from the demos, the starfish internal model knows that if it's got a leg of a given size, and it moves the leg down against the flat ground, it will cause the body to raise. This entire model is innate to the starfish robot. A Baby with a drop sensor wired to a fear response has no real concept of it's body or how it's effected by gravity where as the robot has an innate concept of how gravity effects a 3D body. All the starfish seems to learn, is the shape of it's own body. That's a neat thing to test in a robot to see what it produces, but I think all the talk about this robot having some unique sense of self (as if it were the first and only robot to have a sense of self) is way off base.


That's very true.

But what I think happened in evolution of man is that the strong learning system ended up replacing almost all the hard wired knowledge. Instead of having that knowledge hard wired into our circuits, it was replaced with strong generic learning circuits with hard wired motivations. Evolution still had to make sure we would act in ways to maximize our survival, but instead of making that happen by hard-coding the behaviors, it built a system where it would hand select, and hand tune the motivations, instead of the actual behaviors. All the complexity we are born with seems to exist in our motivations, and not in our behaviors. Our pain sensors and our pleasure sensors are the things that motivate our learning system.

Humans, instead of being born with a large and complex set of instinctive behaviors, are now born with a large and complex set of motivations which drive the learning system.

The baby-drop experiment you describe indicates to me that babies are born with an innate motivation circuit to sense a drop and motivate the learning system to prevent it. In other words, an innate motivation circuit that makes dropping a negative reinforcer.

I know my wife has a very strong anti-drop motivation. It's so bad that she doesn't ride roller coasters because of how uncomfortable the drops make her feel. She also hates sudden dips in roads that create that momentary sense of dropping. She even put a dent in her sister's glove compartment during one car ride where there was a sudden drop in the road which caused her to push against the glove box as a panic response.

For an organism that spent a good bit our of evolutionary past living in trees, it's not hard to see why evolution might have given us a drop sensors and wired it to our learning brain as a negative reinforcer. Without such a sensor, we would learn not to fall out of trees only after receiving the pain caused by hitting the ground. Of course, since one drop could kill, using the physical damage sensors as the only motivation would mean a lot of young chimps would die before learning not to fall. With a drop sensor, the chimp would have an innate fear of falling and would learn how not to fall, much faster, without ever once having to experience the pain of hitting the ground.

I agree it's valid that this shows that some aspect of gravity has been hard-wired into humans. But a drop sensor as part of the learning machine's critic which is wired as a negative reinforcer is very different from a hard wired sense of how gravity effects the things around us (like how we learn to expect anything we are holding to drop to the ground when we let go of it), or how we learn to expect that pushing down on the ground with a leg will cause our body to rise, but that we will still stay in contact with the ground.

The other interesting aspect of your example I think is the "fear response". Humans seem to have hard-wired external responses connected to the internal state of their learning system. All humans smile, and laugh, and cry. This is not something you can easily explain as a learned behavior even though these behaviors are to some extent under our voluntary control. But instead, you can explain it as hard wired external indicators of the internal state of the learning system. That is, evolution seems to have found justification to make the state of the learning system visible externally - just like we used to put lights on the control panel of a computer to allow some of it's internal states to be visible externally.

The fact that a new born baby would show a fear response, I believe is just an indication that the learning system is predicting future pain. I think the fear response is just an external indication of the internal operation of the learning system. When the learning system predicts danger, it's automatically displayed externally, so that others in the group can instantly sense that there is danger in the area.

Do babies show any sign of hard-wired appropriate response to a drop? Such as reaching and grabbing in an attempt to grab a branch to stop the drop? Or do they just show fear, and still have to learn that grabbing things is a good way to stop a drop?

I believe that chimps do have an innate ability to grab and hold their weight basically at birth but I don't think humans have that ability anymore. I think this is just one more example of how evolution replaced innate behaviors with innate motivations driving a strong learning system.

Reply to
Curt Welch

It's sure a shame that we don't have better tools to monitor what a brain is doing so we could get a better understanding of what type of thoughts develop in babies and when. There are just all sorts of interesting questions we have no way to answer because of our lack of ability to perform high resolution brain monitoring.

The example of a baby understanding he needs to move his legs before he learns to do it doesn't have to be a very complex thought, but it would be interesting to know just what type of thought a baby actually has about this. Is he able to visual his leg moving as if he were walking before he has ever walked? I suspect not.

I suspect the type of thoughts the baby is having is not even as complex as visualizing himself walking. I suspect it's working at a much simpler level at that point of his development. I suspect at that point he's developed recognition circuits in his brain which is able to recognize legs, and recognize simple walking actions because he's seen it in other humans. I also suspect that other humans have become secondary reinforcers for the baby because they are predictors of "good things to come". When he stands, or moves his legs correctly, he is reward by these secondary reinforcers simply because he has created in his environment the thing which acts as a reward to him (a human walking). The reward only works when he actually sees the legs moving in the right way, so this is why he will stare at his hands, or legs, as he moves them.

In other words, I think this sort of behavior in babies is probably best explained as simply mimicking behavior driving by secondary reinforcers. The baby is attempting to reproduce the things he has learned to love (the sight of a legs moving in a walking pattern).

I don't think the baby would yet have any sort of concept that walking will allow him to get something he wants. So I don't think he's having any sort of thoughts that could be described as wanting to learn to walk so he can move around the room faster. He would by that point have developed the desire to "get things" and he would have learned to use his arms and hands to reach out and grab something, and learn to craw to get things, but the thought that walking might be better than crawling I think would be beyond what he could understand. I think the attempts to move his leg are just attempts to mimic something he likes. That's my best guess as to what level of "thoughts" the baby is dealing with at that point. Basically I would say the thoughts translate to, "oh, that was cool, it looked just like mom, lets try to make that happen again". :)

Reply to
Curt Welch

Actually, I would encourage you to research some child development texts. Turns out there are real fears associated with height built into human babies, even from visual clues with no experience (therefore unlearned). One experiment I remember is the precipice detection. They take an set a baby of crawling age on a surface of glass, continuous everywhere, so it can't fall. Then underneath there is a tile pattern, one up against the glass and another well below the glass. The baby will not cross the threshold from the close pattern to the distant pattern, even though there is no possibility of falling.

I like clever setups like that, which give us insight into the mind.

I would love to be able to create others of similar scientific test, which speak to the unconscious, even in adults. Very fascinating.

Ah ha. Found something important to this discussion. Google on Moro reflex.


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"One of the more interesting observations in a newborn is the way they seem to be scared of falling for no apparent reason. It seems there is no justification for a baby to have such a fear, especially considering that they've likely not experienced falling in the first place. But Mother Nature equips human beings with an amazing set of reflexes designed to protect us from all manner of possibilities.

"This odd fear of falling is known as the Moro reflex (also known as the 'startle reflex'). It is present in newborns but usually disappears within a few months. At birth, the pediatrician will test the baby for this reflex by laying her down on her back and removing contact with her. She is expected to throw her arms and legs out and extend her head in fear."

See also:

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supports the idea of them having many reflexes built-in.

The Rooting Reflex is another interesting one, and it's goals toward survival are essential. Again, I suggest this one is also unconsciously expressed many different ways in adult life. Freud's "Sometimes a cigar is just a cigar" is easy decoded if one suggests instead, sometimes a cigar is just an expression of rooting. (Original idea of mine, btw.)

More see:

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I agree.

I really disagree.

I don't think the baby consideres itself a human being yet. There is it, and there is everything else in the world. This division starts as soon as it discovers its toes. It figures out what it can wiggle and what it cannot. What it can wiggle or feel is it. What it cannot, is the world.

I would doubt that. I think walking is a much more personal act. I'd suggest the mental process might be how do I keep this higher viewpoint (visually rewarding) and "up", and not wind up smacking my butt/face/etc. on the floor?

Disagree as above. The visual effect is already something they want. Effort has delivered, and they wish to keep their "up" status.

Above in the thread, the automatic bias against down has been identified. I would suggest the there is an equivalent desire for up built into us. You see this displayed when babies reach up both hands and open and close their fists, the universal language for "Pick me up".

So an elevated status is also something I think is a genetic prediliction. I think this was manifest later as man's reaching toward climbing mountains, taking the skies in planes, and to the stars in spaceships.

Agreed. Speed is not the motivation. Neither is mimickery.

-- Randy M. Dumse

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Reply to

Yeah, I'm sure human babies have a lot more innate abilities than I don't know about but which are well documented in the research.

But I'm not really interested in the fact that babies are born with simplistic innate behaviors if they are not needed to understand our general powers of intelligent behavior. For example, if we could remove or disable the mechanism that creates the Moro reflex from a human baby, do you suspect it would prevent them from developing normal human intelligence? I don't believe it would. I think you could disable all these innate behaviors, and still end up with an intelligent human. The only thing you couldn't disable, is it's innate ability to learn, and you would have to leave some innate motivations to guide the learning.

In other words, what do I have to build into a robot, to make it perform any task we can train a human to perform? That's my real interest. I want to build advanced machines, I don't really care about understanding humans beyond the level of what I need to know to duplicate our high level powers in machines.

I'm sure many of these innate behaviors do have a real effect on the personality, and habits, we develop in life. But I don't believe they are foundation of why we are intelligence (why we are able to do the things we do that no machine has yet been able to do). That's why I mostly ignore them.

If we figure out how to implement strong machine learning to duplicate human learning skills, I think we will have machines that everyone will agree are as intelligent as humans. But what we probably won't have, are machines with human-like personalities. They will be more human like than anything we have now, but still not close enough to be mistaken for a human under testing. To make one of these learning systems develop a fairly human-like personality, we may have to give it many of the same odd motivational systems, and odd little innate behaviors, as well as a learning brain with structure and powers very closely matched to the human brain. I suspect getting that close enough to have human-like personalities emerge from the machine will be quite hard. Though that is work we will want to do, it's not work I care all that much about personally because I'm a lot more interested in figuring out how to build smart machines than I am in building things that act like humans.

I agree. Creating a self-image model that separates himself from the rest of the world I suspect starts even before birth because of the skin sensors which create the prime separation between us, and not-us. If you bite your thumb, you get a very different set of sensations than when you bite something that is not part of your body. Those differences are all part of what the brain starts to learn about the basic nature of the world being divided into that prime distinction. Even before learning you can wiggle your toes, this image of self vs not self is starting to form because of the skin sensors. When the eyes start to develop I'm sure it only adds more detail to the me vs not-me view of the world.

However, what I was talking about, was a huge step away from a baby thinking it's a human. Developing a simple pattern recognition circuit to recognize a leg and recognize a walking-like action is just the ability of the baby to recognize that his own leg is similar to a leg of a human. It's no different than developing the power to recognize that a finger looks more like worm, than it looks like rock. It's just the first steps of basic image classification developing in the brain.

Yeah, I think there is probably a reward in standing effect at work in there as well which motivates the baby to keep standing and not fall down.

Yeah, that can explain the learning to stand. Most babies have already learned to pull up to a standing position in a crib or with other objects before they develop the ability to stand without holding. The experience of standing after pulling up, and being able to see things, and reach things, they could not get without standing has already created motivations to want to be upright at times.

But I was talking about the idea of what type of thoughts the baby might have when we see it trying to take steps.

Is the motivation to stay upright, while trying to use the leg motions that worked for crawling which is causing it to make those first awkward attempts at a step? But does the Baby look at it's leg while trying to do this? If so, I think we need to add more complexity to the idea of what might be happening to explain why it looks at it's leg while doing this. If it's not normal for the baby to look at its leg as if it were trying to make it take a step, then maybe the more simple ideas of trying to stay upright combined with trying to craw forward is what leads to the awkward attempts at the the first steps. ???

Sounds like a stretch to me. You don't need to assume a genetic bias of "upness" to explain why we like to be picked up. When babies are picked up, they get rewards like being fed, and they get tactile sensations of warmth. You don't often get to suck on a tit when you are laying on the ground. I suspect there's all sorts of potential motivations being satisfied when a baby is held and fed but I dont think there's any need for some sort of "upness" sensor".

When babies learn to pull up, there are all sorts of potential rewards that we can assume exist to explain why they would do this without the need for height. A baby is probably more likely to be picked up when it's standing in it's crib than when it's laying down. If the baby likes being held for reasons other than height, this could condition in them a desire to be "up" simply because he likes being held and he's always up higher when he is being held.

As for why we like to climb mountains or get higher, I suspect there's plenty of rewards associated with those actions that don't require a genetic bias to explain. You can see further by being higher, which means you can sense more about your environment - this allows us to quickly find the things we want, and helps us to avoid the things we don't want. That alone is a simple answer about why we have lots of interest in "getting higher" without having to assume a special genetic motivational bias is at work.

Reply to
Curt Welch

I understand you are not interested, but I am suggesting perhaps you should be.

I know you have a bias to find a way from tabala rosa to learning. I don't think intelligence works that way. I think intelligence has to have a pretty strong base of hardwired knowledge, before any thing made of soft learning can take hold. For example, you can't write an article for Encyclepida Britania without an innate ability to use language, let alone many other fundamentals, such as an alphabet of some kind, a grammar of some kind, and so on. Some will be innate, some learned. I don't think they can all be learned and none innate.

Actually, the reason these reflexes are known/important is exactly what you doubt. Doctors check these when babies are born to see if they are present. If not it is an indication of a defective human being, who will have developmental problems.

I hear you. But. Baby - bathwater.

We have one example of advanced intelligence. I am suggesting we see how it comes to be different from other life forms and use it as a guide to acheiving intelligence.

Seems crawling is a hardwired reflex. It was listed as one babys display on birth if put on their stomachs. They may not be strong enough but held up, the legs and arms go.

Also the step reflex is innate. It can be detected just after birth. It's not a full walk, only the tendency to move one leg forward when both are touched by a flat surface.

To the best of my knowlege, babies do not look at their feet when they walk.

True, but there is such a preponderance of the bias for up-ness once you start looking for it, it shows up everywhere. Even in every day expressions. For example, take the phrase: higher power. Why not deeper power? wider power? longer power? etc. Also why do you know which is better between a lower power and a higher power. There's a bias, subfusing the word useage.

-- Randy M. Dumse

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Always a possibility. :)

There's great confusion about the concept of tabula rasa. Many would argue that learning without innate ability is impossible. That's just their inability to understand that all learning systems have an innate ability to learn. They choose to look at the system's innate ability to learn, as if it were pre-wired knowledge, and then declare it's not tabula rasa learning since the the pre-wired ability to learn means it's not a blank. It's a stupid game of semantics.

All learning systems are tabula rasa learning because they all start with the innate ability to learn, then then they add to that, the knowledge which they learn.

Tabula rasa learning is not something I need to "find". Every learning system I've built in the past 25 years was already a tabula rasa learning system.

What I need to uncover, is the type of learning system needed to duplicate human level learning ability.

Exactly. A learning system that doesn't have the innate ability to learn to use a language, will never learn to use a language. The problem here is to uncover what type of learning hardware is needed for the class of behaviors we call language use.

If I want to build a robot that can learn to navigate a maze, it has to have the innate ability to move through a maze. If it's not born with the innate ability to move, it won't be able to learn a maze. But to learn a path through a maze, it needs more than the ability to move. It needs at a minimum, the ability to sense when it's solved the maze correctly, and it's likely going to need sensors to help it perform navigation tasks. At the same time, it's going to need some systems that allow it to learn from experience so that after lots of time moving around the maze, it has to be able to use that experience to direct future actions, so that hopefully, it will be able to travel from the start, to the end, without making any wrong turns.

Even with all that pre-wired knowledge about movement, and sensing maze walls, and location in a 2D maze space, the machine is still a tabula rasa learning system because when it starts, it knows nothing about the maze it's trying to learn. When it comes to the first turn, it knows nothing about whether turning left, or turning right, is likely to be the right choice.

All learning systems work like that. They have innate ability to learn something, but when they start, they have not yet learned anything.

So, when trying to build a learning system, we must always answer the question of what type of hardware does the learning system start with - what are the basic primitives which must be built in as innate ability, and what must it learn? The maze learning robot will likely have basic primitives of moving forward, or turning, and the ability to sense walls blocking it's motion and or sense when an attempt to move, or turn, has failed, because it hit a wall, and it will have the power to learn a sequence of these sorts of basic primitives.

But what type of basic primitives do we need to build into a machine to produce human level behaviors? And what does the system learn?

You seem to be suggesting that we might need a basic primitive such as the ability to crawl (or at least some basic crawl-like motions which will later be refined through learning into a productive action). It can certainly be done that way. For example, we can build a robot with legs, and write code that makes it move the legs in some fixed pattern that makes it walk. But then, because our walking gait isn't all that great (it makes the robot move forward, but the legs are slipping and sliding and on a rough surface there is much grinding of the gears in the servos because the gate is wasting energy causing the legs to push in unproductive directions). So, on top of this innate walking gait, we could add a learning system to attempt to make small changes to the sequence to optimize the gait to be more productive. But, how do we do this? How does the learning system know when it tries some change to the gait, if the change made the gait better or worse? We have to create some system to measure success, and use that measure, to guide the learning process.

Learning after all is just a change in function. But random change is of no use if we don't have a system of selection which has the power to evaluate success. Something has to be assigning value to the changes so that the purpose of learning is to actually make an improvement in behavior. The entire concept of "improvement" implies you have the ability to assign value to different behaviors.

So, for the robot, we have to add some sort of measure of "better". For a walking gait, we could use speed as a measure. Or we could attempt to minimize current draw for a given speed by trying to maximize speed per power consumed.

None the less, once you have motivation defined (the value function that the learning system is tryig to maximize) and the primitives that define the search space of the learning function (leg motions), then what's the point of having a pre-wired gait in the system to start with? If you have a good learning algorithm, why not let it start with no pre-wired motion sequences and let it learn to move on it's own? The goal of moving forward as fast as possible is the same either way.

The only advantage of starting it with a pre-wired gait sequence, is to reduce the amount of time it takes to find the optimal gait. Now, depending on the quality of the learning system, this time can be significant because as the size of the search space grows, the amount of time to search it with a learning algorithm can grow exponentially. So where as optimizing a bad gait might take hours, learning a gait from scratch might take months or even years depending on the quality of the learning algorithm.

In the case of living organisms, learning time is an important survival factor. They might die before they learn to walk. So it's not surprising that animals are born with lots of pre-wired behaviors, or that humans are born with various amount of pre-wired behaviors.

But for the most part, the pre-wired behaviors just aren't interesting, or important. Do you really think that the fact that a baby might start with a few simplistic behaviors like a drop reflex or basic crawl motions is the key to how humans were able to invent language and invent space ships to fly us to the moon? Those low level initial behaviors are there only to help us stay alive after birth. Our AI doesn't need help staying alive. We will keep it alive as it learns. If you can learn to build space ships, and solve problems like AI the robot is surely going to also be smart enough to learn to crawl without it being pre-wire into the machine.

Some low level behaviors, which act as the starting point of the learning system, have to be there. But at the level I approach the problem from those starting behaviors are extremely low level and simple - pulse sorting decisions in a generic signal processing network.

That has nothing to do with what I doubt. If a human baby is born without a normal reflex, the odds are its got problems far worse than simply not having that one reflex - it's probably got serious defects in the CNS. They don't test for the reflex because they believe the reflex is important. They test for because they believe a lack of the reflect is a good indicator of much worse problems.

There's nothing wrong with that approach. But in my study of learning systems I figured out many things which allows me to make educated guesses way beyond the need to duplicate every little biological wart humans have in order to create intelligence.

These insignificant behaviors humans start with at birth are nothing compared the enormous set of interesting behaviors we find in an adult. We can walk, and drink, and catch a ball, and cook food, and read a text book, and put together a book shelf, and design space ships, and program computers, and tie our shoes. There are billions and billions of different behaviors a normal human can perform, and every one of them was learned, not built into our hardware at birth.

What is built into our hardware at birth, is the ability of the learning part of the brain to receive sensor data from many different sensors, and control the motion of our arms and legs though it's output signals. Everything else, between there, are circuits that get configured after birth to allow us to do all the things we do. How those circuits get configured by the learning systems in our brain is the key to how we become intelligent through a life time of interacting with a a complex environment.

The key to solving this problem is understand what type of circuits the learning part of the brain is made up of, and what type of learning algorithms are at work shaping those circuits.

Interesting. But again, not very important for solving the problem of how we learn to design and build space ships.

Again, interesting.

Yes, I agree. There's a clear bias. Also, if you notice, we only elect tall presidents. Tall people naturally get more respect. Where does that bias come from? I think it comes from the fact that kids are small and parents are tall. We spend our childhood learning that the tall people have all the power and the short people have none. I think that bias sticks with us for life. Even after growing up, we learn that tall people generally have the edge on shorter people for any physical conflict.

In addition, we live in a world of gravity. Height means higher potential energy which translates to real power. This translates to the high ground being an advantage in any battle. Just the simple act of being knocked to the ground means you are at a read disadvantage in a battle because it gives the other guy the high ground - he can hit you a lot harder by throwing a punch or swinging a club with gravity than you can hit him swing your arm against gravity. The high ground is power because of gravity.

We see this "high-ground" effect translated into all sort of human behaviors. We bow to a person who we want to show respect for - giving them the high ground. We try to stand tall to intimidate someone and show them our power. When we draw an org chart, the guy in the company with the most power is put at the top of the chart. The guy with the most power gets the highest location in the building (the top floor). And of course, the guy with the most power (God) is placed in the sky above us all.

Between the effects of gravity and the conditioning we get as kids to respect height, I don't think you need anything else to understand why there's a clear bias of height being associated with power in humans.

There could still be some built in innate feature of humans that make us associate height with power. But again, I don't see that it's needed - there are plenty of things from the environment that explain it. The only innate power we need to put into our robots, is strong learning. 99.9% of everything we see humans do that we label as intelligent, is a learned behavior - they didn't have it birth. The one innate thing they have at birth that makes humans intelligent, is the ability to learn all this complex behavior in only a few decades of training.

I'm not interested in the human "warts" because I know the number one thing that's missing in our robots right now is strong learning. It's very easy to program a little startle reaction into a robot and make it look very human-like. But doing that won't give the robot the power to figure out, on its own, how to get to the moon, like humans did. To do that, we have to figure out how to add strong, generic, real time, learning to our robots. Once we get a handle on the learning problem, then we can look at the little warts to see what might be needed to make our robots act even more like humans.

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Curt Welch

I hear your argument.

What I think I'm suggesting, however, is in order to know how to categorize and add new learning to the human-body-of-knowledge, perhaps the human-body-of-knowledge needs to start out with a few already installed items.

Again, imagine something like a Wikipedia, where it starts with no articles whatsoever. Would it be possible to boot strap from zero, or do there need to be a certain amount of articles already installed to serve as examples? Likewise, in order to learn, does the human mind need some hardwired examples to serve as a basis for adding more knowledge. I certainly don't know. But I do suspect it to be the case, based on what I have observed from nature.

-- Randy M. Dumse

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Yeah, it has to have something hard-wired at the start. The basic idea of reinforcement learning is that there has to be a behavior to reinforce. If a baby (or robot) was completely motionless, it could never learn anything. It would stay completely motionless forever.

The interesting question however is what form does that initial behavior take? What does it start with?

Obviously, wikipedia started from zero. I think your example is a poor one, but your point is valid.

Well, if instead of trying to understand how humans do it, you simply look at the generic problem of how a machine can learn from reinforcement learning (aka operant conditioning, aka trial and error learning), you can get a good grasp on the fundamental nature of the problem that evolution has solved in its design of a human. I've spent a lot of time looking at the problem from this perspective, and that experience is what causes me to look at what humans do and write off some of their innate behaviors as not significant to the harder problem of learning in general.

So to start, the machine has to have the power to do something, or else there is nothing to learn. So it has to have some ability to perform something we label as behavior (move, or blink some lights, or make a decision, etc). It has to be able to make a decision (stand still, move forward, turn right, etc).

But, when it starts, we assume it's got no knowledge about which decision is better. So it doesn't know if turning right is better or worse than standing still for example. That's the blank part of the blank slate. But in order to learn from it's mistakes, it has to actually try something. If it's initial technique was simply to pick one behavior, (turn right) and stick with it until it's proven wrong, then it may never learn anything else. If the only reward or punishment in the environment happened when the robot found some food, and constantly turning right prevented it from ever finding food, then it would never learn that there is a behavior better than turning right. It would do nothing but turn right.

Basically, whatever the low level decisions the system is making, it must try different combinations of the decisions to see which work better. Given enough time, we want to make sure the system will try all possible behaviors.

The general approach then, is to try different combinations of decisions (basically randomly), and learn from experience which combinations seem to produce better results.

When a behavior (sequential combination of low level decisions) produces better results, the system will then bias it's selection of behaviors so that the ones which seem to have worked better in the past, will get used more in the future. Notice however that I say "bias" here. This is because you never want to stop trying alternatives. Just because turning right has worked the best in the past, the system can never assume that turning right will always work better in the future. It needs to keep trying to turn left at times. But the more times it tests turning right and left, and the more times it finds that turning right is better, the less often it should try the "turn left" test in the future. But it should never stop trying the turn left test.

Those are the basic requirements of making trial and error learning work. You have to start by deciding at what level the system will be learning behaviors, and then you have create a system that will keep trying different alternatives. It will then bias it's selection of alternatives, based on statistical knowledge gained. This part of the problem is well understood. The complexity is all in the implementation details.

Now, for something like a robot, you could pre-program 100 different basic low level behaviors into it. You could have a low level behavior for "move

1 inch forward". And another behavior for "move 2 inches forward", and another one for "turn right 10 degrees", and other for "sit down". You could build 100 such behaviors into the machine, and them make the learning work at the level of selecting which of these behaviors to perform next.

Or, you could make learning work at a lower level. If the robot was the type with two wheels, then you could program only 4 basic low level behaviors into the system, which would be turn the right wheel 1 degree froward, or 1 degree backwards, or turn the left wheel one degree forward or 1 degree backwards. The learning system could then attempt to do all it's learning using only those 4 commands. So those 4 commands are built in (innate) but when they are used and in what sequence they are used, is all learned.

All learning systems must have innate built-in behaviors at the lowest level, and what it learns, is then a problem of making a decision abotu which innate behavior to use next.

Humans (and even simple robots) however don't just produce a string of behaviors. They are reaction machines. We have sensors and we can select behaviors as a function of the sensor data. So the learning problem is a bit more complex. We can produce a fixed string of behaviors mostly independent of the sensor data, or we can produce behaviors as simple direct reactions to sensor data. This creates a lot of dimensions to be learned at the same time. Finding ways to structure the learning problem to reduce the complexity of what must be learned is key to making it work well.

For making a robot act like a human, I think the learning has to take place at a very primitive level. Human behavior is fine-tuned at a very low level by learning. It has to take place at a level of learning near to the level of sending a single pulse to a muscle. In order to fine tune our motion to learn to catch a ball, or hit a running rabbit with a rock, or spear a fish, learning must be happening at a very low level of controlling the timing of individual pulses.

If you have a learning machine, which is adjusting behavior at the level of single pulses, then hard-wiring into the machine something high level like crawling (which requires the sequencing of thousands of pulses) is just redundant to what it's trying to learn. Typically, I would see it working by being a pre-coded learned knowledge. That is, instead of starting with no preference towards behavior, it would be build with a starting preference of the sequence of motions needed to create a crawling type of behavior. But because it's all part of what is learned, it would then still have the ability to learn something different - to learn to never crawl. So though starting it with pre-learned behaviors like that might be useful in reducing the amount of time it would take to learn to crawl, it's not required. What's required, is that there must be low level behaviors hard-wired which are the foundation of all behaviors. But those behaviors will always be the most primitive behaviors you want the system to use for performing all behaviors - like turning a wheel, or when using pulse type signing systems, it will be the ability to produce a single pulse. That's the type of innate behavior you have to build into the system before it can learn. Any innate knowledge you give it above the primitive behaviors are just hints to the learning system to allow it to learn important skills quicker.

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Curt Welch

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