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Fast Navigating Guessing Robots
holy_calamity writes "A new navigation technique for robots allows them to make predictions about what's around the corner based on where they've been already. It works well in repetitive environments like office buildings. If this were a Japanese project I'd say it'd be useful for robotic secretaries new on the job, but since it's an American one I suppose it'll be used for automated SWAT teams."
but since it's an American one I suppose it'll be used for automated SWAT teams.
Ya last corner terrorist, next corner must be terrorist, come out shooting.
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...sounds like just the excuse I need to place spinning blades around random corners in the office "to fend off any attacking robot overlords".
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I could see this being applied to game technology before it gets applied to law enforcement. This is an interesting approach to an AI (or AI-like) problem. The implementation just happens to be (and is well suited for) robots.
It's not offtopic, dumbass. It's orthogonal.
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Currently robots really struggle with making good judgement calls. Behvior-based systems only go so far, perhaps this will go one step further.
Engineering is the art of compromise.
More likely, you'd have a Japanese robot who is a waitress by day and a combat cyborg by night. And she happens to be a vampire from the future. And she wears a bunny suit. And she's also a suicidal paranoid schizophrenic.
At least, that's what I've learned from watching anime. For God's sake, if you're going to troll, at least try to get your stereotypes right.
Naaaaah, it has to be for automating our SWAT teams, because we're a bunch of killcrazy cowboys looking for new ways to blow things up. Um... yee-haw?
It's called Banality Bot, and the last sentence of that post is about to get its tubes cleaned.
Sounds like the same algorithm most drivers use.
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I don't know much about AI, but is the idea of making predictions based on previous data some kind of breakthrough? I'm assuming this is just an application of some firmly established concepts in AI. When confronted with a redundant or repetative data set, make predictions based on your experiences as to the nature of new elements in that set. I mean, aren't we paying these guys to tell machines how to recognize patterns? Is it news when they teach a machine to recognize patterns?
I'd venture that the purpose of this post is to discuss Terminators, and Japanese robot secretaries, and to hail our coming robot overlords. This is just a guess based on a highly redundant data set I've been analyzing (rather than doing my work).
The more you know about the context, and the more you know about the result to a given action, the less information you need from the environment (or from the other side of a communication channel). This is the Holy Grail of information theory and data compression, and it seems as if they are applying its principles here. Higher CPU and better expert programming will likely produce some nice results in the near future.
I, for one, welcome our intelligently navigating robotic overlords.
Just because it can't be explained doesn't mean it isn't true. Science fits into reality... not the other way around.
I kinda thought the SWAT joke thing was supposed to be an ironically self-deprecating throwaway line, rather than that other sort of a line, the one with the hook in it, that everyone seems to think it is. Actually, thinking about it, after all the criticism American defense forces have come under, maybe a little prickliness from you guys is a good thing. Last thing this thread needs is someone quoting how much of the US GNP goes into ADF funding.
THUD~*
...a Japanese robot who is a waitress... I dunno, I don't think I'd be giving a welcome plate of muffins to any waiting-staff-member who turns left purely out of habit. I think they'd be better on register, or coffee-machine. I can't wait until they make robotic kitchen-hands, so I can quit washing dishes and get a decent job. Like... data-entry.THUD~*
Reduce, reuse, cycle
In my project called DATMO (Detection And Tracking of Moving Objects) i've made a tracker that followed people that could "guess" where the people would appear next, using an industrial laser scanner, check the video at http://miarn.sourceforge.net/videos/pv3d_peopletra cking_and_scene.avi
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This almost sounds like some variant of reinforcement learning.. (The bit with confidence scores). Why do they never post real algorithm details :-(
"I'd say it'd be useful for robotic secretaries new on the job"
As they get chased around the desk by their robot bosses? It's pretty much left, left, left, left... etc...
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It's called SLAM: Simultaneous Localisation and Mapping. It's not extremely new. The seminal work was due in the early 90s. Leonard, John J.; Durrant-Whyte, Hugh F. (1991). "Simultaneous map building and localization for an autonomous mobile robot". Proc. IEEE Int. Workshop on Intelligent Robots and Systems: 1442-1447. The essential concept is that localisation (where am i?) and map building are two sides of the same coin. Therefore conceptually, they can be done at teh same time.
A huge problem in SLAM (of which there are many) is loop closing. Start at Pt A, walk in a circle. How do I know that I'm now back at Pt A. Taken further, on the built map, is the path which I have jsut taken mapped as a circle as well, or some odd (non-circle) shape.
A huge direction of SLAM is built with a probabilistic framework using Bayesian techniques. As opposed to "I am now 2m from where I started", it's more like "with past information and present data, I infer that I am 2m with a 90% confidence, 2.5m with a 95% confidence, 10000m with a 99.999999% confidence." Usually, this confidence is down with a Gaussian distribution.
Having said all that, SLAM is pretty cool.
Having lived in Japan and other Asian countries. I would expect the Japanese to have the robot SWAT teams long before the US. I note that those most likely to make the inexperienced remarks about America vs the rest of the world are either Americans with limited or no experience in non American countries, cultures or languages or Europeans who have equally little actual first hand knowledge of America.
Let's throw them a curveball, make one of them white & the other black, see what happens.
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where it is at all times, it knows this because it knows where it isn't. By subtracting where it is from where it isn't or where it isn't from where it is, whichever is greater, it obtains a deviation.
Particles, stuff that matters.
FTA:
Davison and colleagues are designing endoscopic surgical instruments with SLAM abilities.
Does this sound painful to anyone else?
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"wife back seat driver" robot?
Robot 1: I'm going left.
Robot 2: No, you idiot! Go right!
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