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Comment by cptskippy | Hacker News Reader

When I was in school in the late 90s it seemed like all of the robotics and AI research were in create algorithms to emulate behavior. Boston Dynamic's work seems to be extended from that. They purpose built a robot with purpose built balancing and walking algorithms. The being pushed over and getting back up demonstration seemed to confirm that to me.

Everything I'm seeing these days with AI research seems to be about developing systems that learn for themselves. Rather than telling the robot/AI exactly how to behave in a given situation, it's about allowing the robot/AI to experience as many situations as possible and learn what the appropriate response should be so that in the future it can independently identify and react accordingly.

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I was also in AI school in the 90s and I agree with you to some extent. But when you think about nature, much of the behaviour of animals is not cognitive, but instinctive and physically dependent on their structure. In other words if you can build a robot that knows how to walk because you built it that way, why not do so and build more interesting learning at the decision making level?

A self writing behavior tree that uses behavior to write new behavior based on past behaviors. Sounds human enough to become an addict of some sorts. :-).

Well the problem with that approach is that you have to account for every possible situation that might occur and program them into the robot. When a situation arises that you didn't anticipate then bad things happen. When a learning machine, it can teach itself, adapt and understand.

The former approach works fine for a factory floor robot that is in a controlled environment but doesn't lend itself well to other situations. An example might be a biped robot walking up a mountain and it falls. Most of the examples I've seen, the Boston Dynamics one included, have the robot detect it's falling and put itself into a crash position where it remains until it comes to a rest before attempting to recover.

If your robot is rolling down a mountain it might be destroyed before it comes to a rest. Having a feedback loop, reflex reactions and the ability to access the situation and recover dynamically would be much more useful.

Having a feedback loop, reflex reactions and the ability to access the situation and recover dynamically would be much more useful.

Agreed. But none of that implies learning. So why are you talking about learning in the beginning of your comment?

This happens all too often in AI conversations. Learning gives you a special and powerful kind of flexibility, of course. But not being able to learn doesn't imply it can't cope with an infinite range of situations. A robot that's unable to learn could be programmed with enough flexibility to walk on any surface it could possibly encounter.

>A robot that's unable to learn could be programmed with >enough flexibility to walk on any surface it could possibly >encounter

How exactly?

I meant to say that a learning robot could adapt to a situation and try out possible solutions, measure success and adapt where as a preprogrammed robot would only ever try what it's been infused with. In my analogy of falling down a hill, the learning robot might not be able to stop itself on the first try but hopefully it might adopt a strategy that could allow it to regain control of the situation.

I think it does imply learning. Human babies "learn to walk", by using the feedback from lots of little experiments to improve their ability to navigate uncertain and varying terrain.

Basically, flexibility and the ability to deal with novel situations is close to synonymous with the ability to learn.

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