A Compilation of Robots Falling Down at the DARPA Robotics Challenge

Moravec's paradox - stuff we thought is hard (chess, derivatives) is easy, while stuff we think is easy (face recognition, getting out of a car, opening a door) is really hard. [LINK] One of my favourite videos is this compilation of robots falling (failing) during the simplest tasks at the 2015 DARPA challenge: [VIDEO]

This [1] list of failures shows that some robots seem to "just" have hardcoded motions, for example for turning the valve. [1] [VIDEO]

Yup, there is a boatload of low level knowledge missing from any AI that we build. It is painfully visible with robots (and autonomous cars are robots as well), see DARPA challenge video which was then lead BTW by Gill Pratt: [VIDEO] A lot of stuff we take for granted such a supporting oneself by a nearby wall when loosing balance, is completely out of reach of todays "AI".

slightly off-topic, but the images in the article illustrate it beautifully: I think many people don't realise (I didn't) what an amazing skill it is to be able to read handwriting (cursive). I took it for granted to be able to read hand-written German, English, Spanish, and totally forgot that it must have taken me some time as a kid to acquire that skill (beyond reading and writing generally). However, when I studied Chinese for a bit, the (printed) characters are tricky to start with, but reading a fluent writer's handwriting ("unconstrained", as they call it in the article) is nigh impossible. I think stroke order is hugely important, in that it forms the conventions for fast abbreviated writing. For example, the "mouth" radical, a square, "口", when written quickly basically looks like a lower-case "b", because the left lower stroke comes first, then top and right in one stroke, then the bottom stroke. More complicated characters basically become a squiggle, and it's really magical, if you think about it, that you can just read it. I mean, look at figures 5, 6, 7 in that article. For me, a nice illustration of Moravec's paradox of AI [1]: what we think is difficult (chess, differentiation, ...) is easy, and what we think is easy (reading handwriting, putting an egg in the fridge, walking (!) and opening a door [2]) is hard... [1] [LINK] [2] [VIDEO] ("A Compilation of Robots Falling Down at the [2015] DARPA Robotics Challenge")

> I'd trust a machine to drive more accurately than most people I see on the highway. Humans aren't special, in fact more often than not we're sloppy, subject to fatigue, and a whole bunch of other negative things. Humans are much, much, much more capable than the absolute state-of-the-art robots when it comes to doing things in an uncontrolled environment. [VIDEO]

That reminds me of Moravec's paradox: What we thought was hard and difficult (and indicative of intelligence), such as playing Chess and computing derivatives, is actually quite easy, while a lot of things that we consider trivially easy (distinguish a dog from a cat, pick up an egg, read some squiggly text) are quite hard. [LINK] One of my favourite demonstration of that was the 2015 DARPA robot challenge, where one robot after the other failed at such difficult tasks as walking and opening a door. [VIDEO]

We've come a long way from the 2015 DARPA Challenge, where the robots succeeded only in falling down: [VIDEO] [LINK]

The progress since the 2015 DARPA challenge (where robots succeeded mostly in falling down) is impressive. Less than a decade! [VIDEO] [LINK]