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At ICRA 2022, Competitions are a core a part of the convention. We shine a highlight on influential competitions in Robotics. On this episode, Dr Liam Paull talks in regards to the Duckietown Competitors, the place robots drive round Rubber Ducky passengers in an autonomous driving observe.
Dr. Liam Paull
Liam Paull is an assistant professor at l’Université de Montréal and the pinnacle of the Montreal Robotics and Embodied AI Lab (REAL). His lab focuses on robotics issues together with constructing representations of the world (resembling for simultaneous localization and mapping), modeling of uncertainty, and constructing higher workflows to show robotic brokers new duties (resembling via simulation or demonstration). Earlier to this, Liam was a analysis scientist at CSAIL MIT the place he led the TRI funded autonomous automotive venture. He was additionally a postdoc within the marine robotics lab at MIT the place he labored on SLAM for underwater robots. He obtained his PhD from the College of New Brunswick in 2013 the place he labored on sturdy and adaptive planning for underwater automobiles. He’s a co-founder and director of the Duckietown Basis, which is devoted to creating partaking robotics studying experiences accessible to everybody. The Duckietown class was initially taught at MIT however now the platform is used at quite a few establishments worldwide.
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Abate: [00:00:00] Good day all people. That is Abate. Subsequent week is ICRA and a core a part of this 12 months’s convention goes to be robotics competitions. So we’re going to deep dive into a number of the influential robotics competitions on the market. with a few brief spotlights on a number of totally different ones this week, we’ll be speaking to Dr. Liam Paul, the co-founder of the Duckietown competitors.
Hey Liam, welcome to Robohub. Might you give us a little bit little bit of background about your self?
Dr. Liam Paull: Certain. My title’s Liam Paul. I’m a professor on the college of Montreal. I’m additionally the president of the Duckietown basis and one of many co-founders of that venture.
I did my PhD in in new Brunswick. After which I did a postdoc in MIT, which is the place this Duckietown factor began. And now I’ve been a proffer about 5 years or so.
Abate: Yeah. So at present truly we actually need to dive into the Duckietown competitors. Um, so might you give us a little bit little bit of [00:01:00] details about the way you began it, what your motivations had been?
Dr. Liam Paull: Yeah. So, I imply, the Duckietown factor is one thing that’s type of taken on a lifetime of its personal, for certain. It began as a category at the start, it was used for academic functions, however then in some unspecified time in the future alongside the best way we thought that it will have additionally worth as, as a scientific benchmark. And so we began to see if we might reformulate and repurpose the platform to host these these competitions.
And the primary one was it NeurIPS. And I need to say 2018 after which we’d performed at the very least one at ICRA and some at NeurIPS and it’s form of one thing that’s actually actually gathered the motivation, I feel actually is it’s all about making an attempt to scrupulously benchmark robotic algorithms. And this can be a fairly, it’s truly a fairly [00:02:00] arduous job.
A number of robotic analysis is finished in some particular lab with a really particular setup and is kind of arduous to breed. And so we wished to construct a really standardized however very accessible platform that folks might simply get their arms on, simply, put their algorithms on, and that we might by some means like examine all kinds of algorithms in some.
Standardized and like honest, honest method.
Abate: Yeah. So what’s the precise problem that they’re competing for and the way does it, how does it look?
Dr. Liam Paull: Yeah, so that is advanced through the years, however the fundamental premise is, is, is. Largely the identical. In order a part of the Duckietown platform, we now have the vehicles, that are these little, little vehicles that you could construct, however then there’s additionally an setting through which they function.
And the setting is [00:03:00] made up of like yoga mats and duct tape and indicators that we’ve like printed and stuff. Um, however the concept is that it’s very standardized and really reproducible. To you or me, like, it seems to be like a small metropolis. Prefer it’s a really simplified view of a metropolis, nevertheless it’s one thing that approximates by some means a small metropolis and the challenges are very in complexity, however principally concerned the robots navigating on this metropolis.
And we are able to. we are able to differ the complexity by having totally different typologies of the town intersections. We are able to have totally different obstacles, we are able to produce other automobiles. And so the complexity can actually develop. Um, however essentially the most type of like fundamental, basic, like a PR factor that an agent ought to be capable of do is like drive down the street within the metropolis, keep away from obstacles and keep of their lane type of factor.
Abate: Yeah. So what was the motivation behind the title Duckietown?
Dr. Liam Paull: That’s an [00:04:00] attention-grabbing, that’s an attention-grabbing one as effectively, truly. So just like the ducky not too many individuals know this, however the ducky branding, not solely does it, it predates the Duckietown venture, nevertheless it additionally has an ICRA connection. So the opposite co-founder of the venture his title’s Andrea Censi and now he’s at ETH Zurich.
And I feel the 12 months earlier than Duckietown began, he was… I overlook precisely what the title was, nevertheless it at present this push for everyone to submit movies they usually had been going to try to sew all of those movies collectively to make like a promo video for the, for the convention. And Andrea got here up with the concept that each video ought to have a rubber ducky in it form of for various causes.
However I feel that partially, it was like for scale and likewise for like some type of coherence between the totally different movies. So they might do like enjoyable cuts and stuff in between the movies, however by some means the branding of it similar to completely exploded. After which once we began this venture, [00:05:00] like earlier than anything, the one constraint was that it needed to have like rubber duckies concerned.
I… I don’t know… Simply form of occurred that method.
Abate: Yeah,
no, it’s nice. As a result of if you like grounded in one thing, that’s like a enjoyable idea it makes it way more partaking for individuals to, to need to do it.
Dr. Liam Paull: Yeah. And there’s additionally a side of I imply, my view is that some, some robotics particularly is type of portrayed in a sure method.
And I feel that like Hollywood has one thing to do with this. Scary, not like both it’s like Terminator are going to come back and kill you, or it’s scary within the sense that it’s going to take your jobs or no matter. And I feel, yeah, in the long run a part of, a part of the motivation behind this like type of enjoyable, playful type of factor was that we might break this mildew a little bit little bit of making an attempt to make one thing that’s tremendous quick and tremendous scary and tremendous huge or no matter that perhaps this might enchantment to.
Completely different people who find themselves perhaps not [00:06:00] drawn to the, like, let’s construct a giant, quick, scary factor, however as an alternative, you realize, additionally need to have the ability to like specific themselves by some means via like via their work. And I feel yeah, I feel that’s additionally been, been a part of it and has been type of, type of profitable.
Yeah.
Abate: And so the competitors now it’s been operating for, is it a decade or two?
Dr. Liam Paull: It’s not, no, it’s not that lengthy. I feel it’s, I feel the primary iteration was in 2018. So I feel we’re at like, across the five-year mark. Um, however the five-year time. Yeah. The primary iteration of the category at MIT would have been one thing round 2016.
I feel. So the venture itself has in all probability been round for six or seven years, however the, the, the competitors itself perhaps solely 4. Hm. Yeah.
Abate: So what have been a number of the, the real-world advantages that that you simply’ve seen out of the competitors?
Dr. Liam Paull: Yeah, that’s an excellent query. I imply, I feel with Roberta [00:07:00] robotics, I imply, a part of our you realize, philosophy is that robotics ought to contain a robotic.
And I feel particularly in newer previous, there’s been this enormous development in direction of like machine studying and deep studying. Sort of algorithms. And I feel these algorithms definitely have enormous potential, however if you try to put a few of these algorithms on robots, you see a number of the, a number of the type of nitty-gritty particulars that you simply perhaps didn’t take into consideration actually have a big effect, you realize, like how the latency of your system you realize, the way it’s coping with.
asynchronous singles versus synchronous alerts, like treating time, you realize, non-model defects and issues like friction and slippage and issues like this. And so for lots of the oldsters, I feel like the actual, like the actual world profit has been that, wow, they actually have gotten an appreciation for simply how, how powerful it’s [00:08:00] to, to construct these methods.
After which if you take a look at like what, though we’re not all the best way to having, you realize business, autonomous automobiles. I feel that you could get some type of an appreciation for simply how outstanding, what has already been achieved. , it truly is when you think about all of the totally different items that need to work collectively and the way sturdy all of them need to be.
Yeah.
Abate: And I can think about through the years, you realize, totally different applied sciences have taken extra curiosity within the eyes of roboticists and that the strategy that the totally different individuals competing has modified fairly a bit as effectively.
Dr. Liam Paull: Oh, for certain. Yeah. At the start, I imply, we very a lot noticed fairly conventional what I’d name like classical.
Not as a result of they’re outdated, however simply because it’s like the best way that issues was once performed, type of like stacked that had the very commonplace abstractions of like, you realize, notion and state estimation and planning and management, and now way more we’re seeing opponents [00:09:00] try to clear up this. And to finish machine studying kind of strategies, whether or not they’re based mostly on extra like imitation studying paradigm leveraging knowledge that we make out there, or whether or not they’re utilizing the simulator primarily.
And simply making an attempt to do like reinforcement studying stuff. Fashion strategy after which switch their brokers that the actual, the actual robotic, these, I, I nonetheless assume it’s like stays to be seen at this level at this juncture, like which one is definitely higher at fixing the duty. However one factor that’s positively true is that the scholars within the opponents appear to be way more they discover the, like, I feel the machine studying type of strategy is extra interesting at this level.
It’s type of like this sizzling, sizzling subject, I assume.
Abate: Oh, that’s attention-grabbing. So it’s perhaps it’s extra interesting, however perhaps it’s not essentially as of proper now leading to a extra success for the opponents.
Dr. Liam Paull: Yeah. I imply, the best way that I view it, particularly like from a say a scientific standpoint is that [00:10:00] particularly on this setting, every thing’s very well specified a very well engineered answer with little or no studying goes to be very arduous to be.
you realize, the potential advantages of extra studying based mostly methods or that they need to be capable of be extra sturdy to various circumstances, be capable of generalize in form of a extra, a easy, extra S less difficult option to totally different environments. And so, yeah, it’s, it’s not, it’s not at all times simple. It’s not at all times simple to love we now have we now have to think twice about even simply what the metrics we’re going to make use of.
to match, you realize, these totally different algorithms, like, is that simply the one which, you realize, drives the quickest? I’m undecided that’s the perfect, you realize, that’s the perfect metric. Um, there’s all these different elements about like robustness and skill to generalize, to totally different like situations and issues like that.
And in these circumstances, the [00:11:00] machine studying options perhaps do a bit.
Abate: Yeah, no, it’s an attention-grabbing level about overfitting your answer to particularly the competitors setting, apart from like whether or not or not that’s one thing that you simply actually need to do as a choose to say whether or not or not this can be a higher answer, it is likely to be higher on this competitors as a result of it was sooner… however ought to the impediment course change a bit, the topology change, now, perhaps it’s not so sturdy.
Dr. Liam Paull: I feel that is truly the central problem in constructing robotic competitions. It’s very troublesome to construct a robotic competitors. That’s like not hackable in some sense that you could’t win by simply actually overfitting to the specifics of that individual of that individual setup.
And so, yeah, I imply, I feel. You hit the nail on the pinnacle there it’s that is the massive problem for certain. And [00:12:00] making an attempt to construct like actually good robotic benchmarks.
Abate: Yeah. In order you, as you consider subsequent 12 months’s competitions have you ever guys ever thought of perhaps doing a not releasing the map and having or not it’s a bit extra of a shock and have a little bit extra randomness related?
Dr. Liam Paull: Yeah. So we, we now have, we now have usually performed that. Like, we now have a form of a, like a, a validation set that folks get the outcomes they usually can see every thing. After which what they’re truly evaluated on as like a held out check set that they don’t see. However what we’re interested by doing this 12 months, So usually what we’ve performed is we’ve had form of like perhaps two or three major challenges, just like the lane following problem, the lane following with obstacles, problem, and the lane following with intersections problem or no matter.
And every one in all these challenges is, has its personal outlined metrics. Like how lengthy you survive for, or how far you’re touring in a sure period of time, form of like commonplace stuff. What we’re going to do that [00:13:00] 12 months is we’re going to. Have a sequence of ranges successfully which might be simply more and more advanced and more and more troublesome.
And every one in all them perhaps has like some, some degree when it comes to the metrics that you must obtain to ensure that it to be handed. However what we’re making an attempt to do is definitely alleviate the overfitting to any particular type of like particular job and stage. You’re going to have an excessive amount of extra. B constructing a normal goal agent that’s in a position to do moderately effectively in a, like a very like various like environments of various complexity and rising complexity.
And so I, that is our, that is our subsequent try, truly at type of making an attempt to alleviate this, like over-fitting to the specifics of the, of the the particular like problem or no matter.
Abate: Thanks.
transcripttags: Algorithm AI-Cognition, Algorithm Controls, Competitors-Problem, podcast
Abate De Mey
Robotics and Go-To-Market Knowledgeable
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