Transcript
0:05 hi everyone okay let's start to 21 the second lecture or the second week of this quarter so yeah hi i'm dorsa you saw me last time i'm co-teaching this class with percy and today our plan is to talk a little bit about robotics so this is this is going to be kind of an informal introduction to robotics a little bit of a history a little bit of state of the art some cool videos and and a bit of
0:35 chat so i don't need to finish my slides i have a lot of slides i'm probably not going to finish my slides so feel free to interrupt me any point in time if you have questions about anything we can make this an informal discussion again at any point in time i will probably not cover like all the things that are in the slides anyways all right so let's get into some quick logistics all right so this was
1:01 our plan for the class i'm sure you've seen this from the last lecture so the plan was to start with reflex based models we've kind of already started that so percy basically went over the machine learning components last week and we also have another week of machine learning a little bit of deep learning and then starting next week we are going to talk about state-based models so i'm going to cover a lot of that so we're going to do search mdps games i'm
1:30 going to reuse a lot of videos from last year just giving you a heads up we'll probably add and remove some but last year's videos there in nvidia we have a whiteboard everything is great so i'll probably reuse a lot of that but we basically will plan to break them into modules and then percy will cover variable based models and i will finish logic and finish the class so so that was just a quick overview of what the plan is if
1:55 you remember monday lectures are not modules right monday lectures are going to be guest talks and chats and having fun so just to give you an idea of the monday lectures so this is a tentative schedule you've already done the introduction to ai percy did that today i will be doing this talk on state of robotics talking a little bit about what that is why you should care about it and then next week we have next monday
2:21 we have a guest speaker and the guest speaker is mariano florentino spoiler he is a faculty in the law school he's also on the california supreme court so it should be a fun talk to attend he does a lot of work around ai and law he does teach a class actually on regulating ai and he has a lot of interesting opinions on that so totally recommend showing up for that i think that would be a lot of fun
2:46 then the week after we have tatsu hashimato so tatsu is a new faculty in the cs department he does a lot of work around robust machine learning so he'll probably be talking about that followed by percy talking about state of natural language processing in week five by the way this is tentative so do not quote me on that some of these dates might change i think the speakers are accurate the dates might move around and then finally in week six we have we
3:11 have emma pearson talking about ai and equality and healthcare she she's got she'll be a faculty at cornell tech joining next year so it would be interesting to hear hear from her and then week seven is kind of like a fun chat just with percy and i will show up and you can ask us anything basically we plan to think about like grad school and talk about like those topics probably so if you have any questions
3:34 about that research what to do after 221 i think that's a good week to attend week eight we have you offshore so he was faculty at stanford and he has done a lot of work like from back in the days in ai and his you know kind of like all the revolution of ai so really fun to show up for his talk and then week nine we have drago and golovk so drago is the head of
3:57 autonomous driving at waymo so if you're interested in learning about autonomous driving to the extent that he can talk about it that would be weakness and then week 10 i will do a conclusion so that is and i'll wrap up the class so so that is kind of our plan from one day lectures i just want to advertise it so and you kind of like have a plan of what will show up next next couple weeks okay
4:24 all right any questions if you have any questions by the way just put it on chat or interrupt all right so today we want to talk about robotics and i just wanted to start it off i have a lot of videos today so it'll be fun videos i just wanted to start off by some video showing robots can dance just to advertise this you guys can hear this right
4:54 yeah okay anyways i wanted to start with this video just to motivate why we care about
5:24 robotics this is spot from boston dynamics boston dynamics is this company that does a lot of really cool robotics type of work we'll see more of the robots later in this lecture but robots can dance they're cool and let's just start the the conversation with this question of well what is a robot and when is it that we call it a robot so question that i have and i think maybe like it would be a good starter that you
5:48 can just go into breakout drones for two to three minutes just chat about this and then the question that i guess i have is think about a hammer if is is a hammer a robot what do you think and think about like google or google home or like siri is that a robot so what defines a robot and i think that's just like just the starter to think about like what is a robot why should we
6:12 care about it and go to breakout rooms two to three minutes we'll come back put your answers introduce yourself talk to talk to the people in the breakout room and then put your answers when you come back on chat and we'll continue yes i believe everyone's back all right yeah so i hope you just met your friends and other people in the class and yeah if you have thoughts put it in the chat we'll look at it later what is a robot
6:40 but let's actually continue with our talk today because again i have a lot of videos so my plan for today is to do a bunch of things i'm gonna start with a quick history of robotics where did it come from why do we care about it then i'm gonna spend a bit of time talking about why you should care about robots and why should this class care about robot how are robots related to ai what is their relationship there
7:05 and then i wanted to spend a little bit of time talking about robotics at stanford so what are the research that's done at stanford who are the faculty who do robotics at stanford just so you know the faces and you know what classes to take and kind of like what type of research is done and how you can get in touch with them this is probably to the extent that i can get but if i have time i'm gonna
7:27 talk a little bit about some exciting robotics applications like all the awesome things that are happening also all the not so awesome things that are happening the fact that robots are far from perfect or like not there yet and then if i have time which is very likely to not be the case i will talk a little bit about my own research around interactive robotics so again the rules of it is at any point in time interrupt just ask questions
7:54 raise your hand like or we'll do that and we'll go from there and let's just jump into this quick history of robotics where does it come from where does the word robot even comes from so the word robot actually is kind of old it comes from this display from carl catholic in 1921 the play is called the rosen's universal robots and it's about basically this mechanical men that are built in factory and are supposed to do
8:20 work for humans and then they rise against humans so that's kind of like the part of it and and basically it has a little bit of a dystopian view of that and and the word these mechanical men basically are are called robota which basically means slave or kind of like labor type work in czech i don't know if anyone knows czech and i don't know how accurate that is but that's basically what i read on wikipedia so i
8:46 assume that is that is accurate so that was the word robot but then the word robotic was also first introduced by this guy isaac asimov who is a science fiction writer and then he came around 1950s and he wrote a bunch of books about robots and and the view of it was a little bit nicer it was a little bit friendlier than this dystopian view and and he talked about these different rules of robotics the robots were there to help humans and
9:13 they were supposed to like follow these different rules so you might you guys might have heard of these three rules of robotics by azek asimov so the first one is that a robot may not injure a human being or through inaction allow a human being to come to harm the second one is a robot must obey the orders given given it by human beings except for each or where such orders with conflict with the first law
9:38 and then the last one is a robot must protect its own existence as long as such protection does not conflict with the first and second one okay so kind of like this is this is obvious right like you don't want the robots to to go against humans and you don't want the robot to kill itself and and these are kind of the three rules and and the reason i'm mentioning this is that people are coming back to these rules like even
10:00 these days like when you're people are thinking about robots they feel like oh a robot should try to satisfy these three laws of robotics and these are kind of like the core laws that need to be satisfied and and the thing about these laws is sure it is nice but they're kind of obvious right and actually satisfying them is the most difficult part and it doesn't really like go go through that like like if i'm if i have my robot
10:23 running gradient descent on the loss function how do i define that loss function accurately so i satisfy these these rules like that is not a very obvious thing and then i guess get it in go go and talk about that let me give you an example so let's say i have rosie the robot that's supposed to like clean my house or i have a roomba that roomba is supposed to clean my house and let's say i have built this super
10:46 nice intricate house of cards okay any human was helping me clean my house would know that you shouldn't touch this house of cards because they spend so much time and this is so valuable to me and then you shouldn't touch it you shouldn't go and clean it but a robot wouldn't know that why would it like why would it know what a house of cards is or like how much energy i've put in in creating it so so that is kind of
11:09 like the objective function and sure it's not about harm it might be harming me it's harming humans but but in general thinking about what should be the objective that the robot should satisfy what should the reward function be we'll be talking about mdps in a couple of weeks and we'll be talking about reward functions what should the reward function be is actually a very difficult problem this is an active area of research trying to understand what
11:33 are the human preferences what are the things that humans actually want robotic agents to do around them and then at the same time what does the robot think those preferences are there's a mismatch between that there's always going to be a mismatch between that and that mismatch how harmful is that going to be how unsafe is it that the robot doesn't know everything and and you might ask well why don't you just like write that as an objective
11:58 that hey robot don't go and destroy doris's house apart oh that's perfectly fine but the thing is like it is really hard to write out all these specifications all these properties that we would want to satisfy in the world because there's just so much context so much information in the world and humans just know that like as they grow up like they learn and they know that and robots wouldn't necessarily know that and you might say
12:22 that's just data so let's just learn through that a lot of people agree with that a lot of people disagree with that it's still like a point of debate that is that just data like i just need to like show more things so the robot knows house of cards are important to yourself like that's also an another being but in general the point of this slide is hey these rules of asymmetry these rules that are put
12:45 out there they're not that obvious to satisfy sure i can say don't kill humans but it's not like really obvious how i write out like what does it mean not harming humans and the second point i want to make here is that even that is still under question then not harming humans it's actually not obvious to everyone that we shouldn't use robots not harm humans which is a little bit silly in my opinion but i'm just talking about
13:10 everyone's opinion here so like if you think about if you think about like our defense or other other countries defense right like people use these things called autonomous weapon systems which are basically like you can have drones that can detect targets and shoot at them you can have detail autonomous weapon systems these are commonly referred to laws by laws and lethal autonomous weapon system basically and there it's a big question should we use these should we
13:39 not use this when can we use like we thought autonomous weapon systems and and yeah it's not like it is here it's not a thing that's science fiction it's actually a thing that our like basically u.s defense has it like like the whole like all over different countries have some version of this and and the question is like yeah if we don't use it like other countries might use it and how do we think about like
14:02 using or not using these systems like how do we put a ban event on it what does a ban on it mean and there's a lot of debate around this start russell who's a faculty at uc berkeley and he also features a either he is he's basically a proponent of banning or fully banning and with autonomous weapon systems and he has a lot of interesting talks around this we will talk about this a little bit more
14:24 toward like in in the conclusion lecture but this is also something i wanted to mention because it could be an interesting topic to talk to tino about next week like when you think about laws and regulating these things how does how does the regulation how do the revelations like work and make sense so yeah so even like not harming humans which is what isaac said like that is still like even under question here like it's not clear if
14:47 that is what we want to do but okay let's go back to the history of robotics like why do we have robotics when did it start so around like 50s and 60s we had a lot of excitement around ai right so person was talking about ai last week talking about the history of ai and that was the time that there was a ton of excitement and even touring turing has this paper where in the paper he writes the best thing we can do is to
15:12 build a robot with tv cameras for its eyes and motors for its legs and have it run around the countryside and learn from the world so this is from back in the day like touring time and this is what he was thinking and like even in the same like in in the same paper later in that paper turing says well this is too difficult i don't want to deal with like this physical interaction with the countryside so instead maybe we should
15:37 focus on the problem of intelligence maybe we should focus on ai and that is how like the next six 50 years was all about ai and building good ai systems and there was a lot around robotics too i like sure like robotics also has seen a lot of advances since 50s but a lot more has happened in the ai side of things just because the robotics side was so difficult an example of that is thinking about
16:02 a big blue which basically won its first game against the world champion for chess in 1996 and it was doing like amazing intelligence right they had an intelligence being able to like win at this game of chess but the thing that was happening was that the chess pieces were moved by humans because that was so difficult like grasping is still so difficult when you think about like a robot trying to actually like move these pieces and and that was not solved like
16:27 in 1996 in any race at all yeah all right so when did the first robot comes i've been talking about this history and and like the question is okay so what was the first robot out there the first intelligent robot so the first intelligent robot was shaky i have a video of it here it's kind of like a five minute video so it's a little bit of a long video but let's just watch it i think it has a lot of interesting
16:50 history in it shaky was the world's first mobile intelligent robot embodying numerous breakthroughs in artificial intelligence robotics computer vision navigation and other research areas the robot was developed from 1966 to 1972 by sri international then called stanford research institute and its legacy and impact are still very much alive today shaky is really the great grandfather of things like self-driving cars and even military
17:21 drones the hardware was really pretty primitive but the software architecture and the software algorithms are what changed the world i think we all thought we were doing really interesting stuff so it didn't really dawn on us that we were doing anything special shaky established a position about what we should be thinking about as possible as feasible to understand why shaky is so important we have to go back to 1966 and we have
17:52 to understand where ai research was at that time well you have to remember that it was pretty much of a a green field which nikki started all over the country and even outside of the united states people were beginning to build the components to artificial intelligence nobody had tried at the time that shaky was launched to integrate all the components of ai and robotics into a single moving vehicle that could reason about the world could sense the world around
18:21 it and could take actions prior to 1966 there were no robots or at least non-intelligent ones the concept of an intelligent robot was limited to the realm of fiction you will meet a charming character in the robot always at your service when you read the title of the original proposal it was something like a mobile automaton for reconnaissance and the reason we called it an automaton
18:52 was because until shaking you couldn't go to a funding agency and say i want money to make a science fiction kind of device so we needed name and finally charlie in his inimitable fashion said it shakes like hell when it moves let's just call it shaky key components of shaky's hardware were a tv camera to observe its environment an antenna radio link bump detectors and a push bar to move objects my role was mainly get the images and
19:24 get whatever coordinates they needed to determine where they were and you know extract the information from the image i remember when i first saw it gee that looks like a dishwasher on wheels while charming shaky wasn't impressive for its looks it was the ai and programming advancements that made it famous we structured shaky software in four distinct layers and that was the first time the layered architecture was used for robots
19:54 chicky's pioneering software architecture paved the way to a new era of ai and robotics the sri team later developed flaky a research robot that demonstrated fuzzy logic and goal-oriented behavior then came centabots one of the earliest projects in swarm robotics where 100 autonomous robots demonstrated the ability to map a complex area collaboratively i like how it's code that isn't just turning numbers into other numbers you get to see the thing come to life right
20:25 next to you shaky also inspired research in natural language-based interactions leading to the popular speech-based technologies that we use today shakey's breakthrough in computer vision is now used to help drivers stay in their lanes and every time you get driving directions on your phone or navigation system you are benefiting from the a star navigation algorithm first invented for shaky even nasa's mars exploration rovers use
20:57 navigational techniques that were first launched with shaky the future is things like potentially having teams of autonomous aircraft that could go out for example and do firefighting and doing this either fully autonomously or potentially in tandem with human piloted aircraft that can go out and work with them collaboratively shaky now resides in the computer history museum visible to hundreds of thousands of visitors annually and in 2017 shaky was honored with an ieee milestone achievement award
21:27 the shaken milestone's important because first of all shaky is the world's first mobile intelligent robot in addition this is the first ieee milestone in areas of either robotics or artificial intelligence looking back more than 50 years after the shaky project began it's inspiring to see how one small team can make such an impact how one ambitious idea continues to benefit our lives how one robot changed the world
21:59 we didn't realize i think any of us what the significance of this was we knew we were the first but nobody knew where it was going and i don't think any of us would have predicted what happened shaky planted the flag way out there it's a model of the kind of ambitious projects that we should be looking at in the future all right so that was shaky's video this is actually the yeah and the computer history museum down
22:27 the road so when things open up like i suggest going there and seeing shaky there cool so that was my quick history of robotics any questions thumbs up just feel free to ask for later and so in the next part what i'd like to do is i'd like to talk a little bit about how is this related to some of the topics that we are we're learning through this class so how are robots in general like using ideas around ai and i
22:56 want to spend a little bit of time on that so if you think about robotics there's this common architecture that is usually used for robots it's not it's more under question these days but back in the day actually like this was the architecture that a lot of robots tend to use which is the sensing planning and acting architecture and then looping that right so so you sense the world you watch the world you perceive the world you do perception
23:23 and then from that you try to plan on what to do next that is where the intelligence lies and then after that you just act you execute that plan and then once you've acted like you can go back and sense plan and act again and that's a very common architecture that most robots do use and then these days people are thinking about a more intertwined relationship between sense plan and act for example you shouldn't just sense the world for the
23:50 sake of sensing right like sensing needs to be active so there's this area called active perception which is about the fact that i only sense the parts i care about and i need to act on and and we should have like this intertwined relationship between acting and sensing or there's another paradigm these days that basically tries to go from images to to actuation and and kind of like skip that planet part of it by by replacing by
24:15 replacing it by neural networks right so if i have a machine learning system and i start from images can i get directly a control on my robot that's another paradigm i'll talk about that a little bit actually in this section but let's just consider this this type of paradigm of sensing planning and acting and in this class starting next week we're going to first talk about search and actually as you heard in the video we're going to talk about algorithms
24:40 like ai star that's actually something that we will we will discuss next week and ai start was introduced for shaky right like the point of it it's actually like an extension of dijkstra's algorithm and it does have a bunch of heuristics and it's fairly fast and it was introduced for for things like robots moving around and navigating around today we do use a lot of sampling based type techniques so the algorithm that you see here running is this
25:04 algorithm called rrt star which is similar to a star to some extent but it's more of a sampling based algorithm creating this tree this dense tree and navigating along along the right lines of tree so so the type of things we will be talking about next week search actually falls a bunch like in planning for robots right like when you're planning for robots you should think about searching in that space how do you get from one location space to another
25:28 location or how do we get from one robot configuration to another robot configuration following search the week after we're going to talk about mvps and games right mvps or markup decision processes basically the idea there is the world has uncertainty and we should actually model those probabilities and uncertainties and that commonly shows up when you think about robots interacting with each other or with the world in general right like when you have dynamic environments around you when you have a
25:56 self-driving car driving right next to other cars right that that you can model that as an mvp and similarly if you think about this interaction with another intelligent agent you can model that as a game and then we will be discussing that in a couple of weeks and these ideas show up a lot in robotics so here the video on the left basically shows two robots that are trying to coordinate with each other and what they're trying to do is they're trying
26:22 to move this rod together but the interesting thing is that they're decentralized they don't talk to each other and they have different observabilities so the robot in the front can see the the books here and the robot in the back can only see the boxes and just because of the forces and the feel of the forces they can understand what the other agent is doing they can learn what the other agent's policy is and coordinate with
26:45 that agent to do this collaborative type of maneuver here's another example this is also from my lab so here what we're looking at is we're looking at two robots playing your hockey and and here we are again like having this paradigm this game theoretic paradigm of two robots trying to coordinate with each other there's a bit of more learning happening here so the robots are trying to again learn the policy of the other agent or representation of the policy of the
27:13 other robot and based on that like kind of trick the other agent or influence the other agent and win this air hockey game so okay so mvps and games pretty much show up for any type of interactive systems and as robots are leaving factory floors they have more and more interactions with people or with other agents and they're super useful again for for the planning part of robotics we will see bayesian networks immediately after that asian networks
27:41 again are super useful when it comes to things like mapping and estimation so here there's this algorithm called simultaneous localization and mapping slam and basically the idea of it is when you go to this new environment and you don't know anything about this new environment you're going to sample points and based on that you're going to create a map and navigate yourself around it and estimate where you are so a lot of ideas around bayesian networks shows up here
28:08 actually in one of the homeworks we're going to look at things like particle filters and things like state estimation and and based on that how how do we use ideas from bayesian networks to do better estimation of where we are where other agents are and again that is super useful for any robotic system that tries to like do anything in the world basically okay all right so a lot of that was around planning logic is also another topic
28:36 we will discuss in this class it is not as much used in robotics but it does show up at various places so here this is actually a work by chris gazitz group and this is called ltl mop which is basically a tool and the idea here is they try to get this robot navigate the space and go to various squares here and while doing that it tries to satisfy some logical formula so instead of giving it an objective a loss function
29:07 and then doing gradient descent let's say on that and try to come up with a policy what this robot does is it gets that logic formula and based on that it creates a plan on how to navigate this space and why would anyone want to do that well the reason why would anyone want to do that is if you have that logic formula you can actually prove things about this robot you can actually prove that if it would satisfy the
29:29 specification or if it wouldn't satisfy the specification and again that is very useful when you think about safety of let's say autonomous cars right if you want to prove that your autonomous cars are safe you would need to add a little bit of logic there you would need to think about how that can be used in planning and also it also helps with transparency because there is a smaller gap between let's say natural language and temporal
29:53 logic which is this logical language that they're using here and that smaller gap can help us like have a more transparent and clear idea of what the robot is doing here okay all right so that was all planning and all the topics that we are discussing in this class we are currently talking about machine learning a common place that machine learning shows up in robotics is on the sensing side of things so you sense the world and based on that you you perceive
30:22 the role so perception and vision is is a big part of robotics and a lot of that is done using machine learning these days right so so you have a machine learning network that basically tries to do object recognition and activity prediction of what other objects around you are doing or basically like who's the owner of the car in this case i think and what are the other objects around us and things of those forms so that's a
30:49 very common place that machine learning shows up in robotics and the sensing and perception side of things and you might ask about this acting part well what goes in the acting part so it's not just ai that shows up in robotics a bunch of other fields also show up in robotics specifically control theory and optimization is kind of like the core of this acting component of this architecture of sense plan and act and and basically the idea is you
31:14 might again have an objective like following a trajectory and you actually want to put the right control the right accelerations and fraud on the steering angle on your autonomous car in this case and get your autonomous car navigators here and a lot of that a core of that is actually done using ideas around control theory more recently people have been using ideas around machine learning here too so like adding ideas around deep learning for actually planning and
31:43 getting the car to to navigate in this space so those are some of the core ideas as i mentioned earlier there are some other paradigms of the sense planning act one specific paradigm which i think is pretty interesting is to use machine learning to do kind of what is all of the sense plan and act by by trying to learn from humans so so this is commonly referred to as learning from demonstrations or imitation learning and the idea of it is
32:13 if i just watch how humans do things then from that i can just directly figure out what their objective was or what their policy is and then the idea has been around since like 2000s in the area of robotics the work on the left that i want to show is actually like this idea directly being being applied to robotics this is work that peter rubio and andrew ing did in 2004 at stanford and basically the idea is
32:41 there are basically these helicopters that before then it was really hard to fly them by just using control like by just using like ai and control it was actually just really hard to to to fly them and then there are these pilots that can fly these helicopters just much more easily much more simply and and basically the idea is could we use that here to get this horizontal fly in this space so this is
33:16 and by watching a human pilot learn to fly it then learns to fly by itself and so what it does is watch the person fly and then they'll try to fly the same stunt movements by itself and maybe try a few times until instead of nails and what you're seeing is the end result of this machine learning process called apprenticeship so that was this idea of apprenticeship learning where kind of like for the first time like people were able to fly
33:45 these quadcopters autonomously but by learning from human pilots by learning from human experts and that idea has been around in research and people in general have been thinking about how we can learn from humans how can we get robots to act in the world by directly learning from humans but not just from demonstrations but also from things like asking their preferences or language instructions so in my lab actually we're doing a lot of work in this domain where we're
34:13 looking at preference-based learning and actively basically querying people for what their preference trajectory is and in order to basically get a robot to play some version of mini golf here like targeting for one of these balls and get the robot to actually hit the ball correctly so it gets in the right goal and that's kind of exciting because you can learn from all sorts of human feedback right you can learn from demonstrations comparisons language and from that you are able to basically
34:42 get a robot to an interesting type of maneuver another place that machine learning shows up is again like by combining this sensing plan and act into one giant box right like starting from visual data and once you start from visual data can you try to directly get actions on your robots and and this idea of robot learning has been very popular in recent years this is work from berkeley from 2015 where basically the idea is to get the
35:09 robot to just try out things and then from that from these images and and kind of joint angles of what the robot tries from that it learns how to achieve the task and how to do the test it's a very data intensive type of process but there's a lot of excitement because you can achieve things that you weren't able to achieve before and we have seen a lot of advances in machine learning when it's applied to
35:32 let's say nlp and vision places where we have a lot of data we don't have that much data in robotics but if that is the bottleneck then maybe we can create an arm farm kind of like this this image this video here and and create an arm form and basically just collect lots of lots of data of robots basically like navigating in this space moving randomly inside of this box and from that learn how to just grasp
35:58 any object that we see this is actually worked by google robotics so google x has a sub group google robotics that does a lot of work on robot learning a lot of interesting work and yeah like very intensive you need to very compute intensive but lots of excitement around this idea of directly learning actions from sensing data okay all right so let me move to oh okay so so those were all the things i wanted to say on how ai
36:29 is used in robotics but ai is not the only thing that is used in robotics there are a bunch of other things like as you can as you've probably noticed robotics spans a bunch of different different departments for example you see robotics in mechanical engineering and that has a very different view of robotics and and that view is usually focused on design and co-design which is a super important problem right so if you're thinking about building an arm or building a hand
36:55 that needs to do very precise manipulation what type of sensors you're using how are you building this system like those are all really good questions like how do we make sure that you build a prosthetic that is not too heavy and it's also comfortable and is also very safe for the person to use for walking like these are all really great questions that are usually like design questions that are super important in robotics another reason that this is important
37:19 is that there's a whole new area around co-design which basically says well for whatever hardware we pick there's going to be some ai algorithm but if i change that hardware my ai algorithm is going to change and if i change my ai algorithm like that could run on a different hardware very differently so can we design both of these at the same time we design what our robot should look like and what algorithm should run should run on them kind of at the same
37:45 time or can we have reconfigurable robots and a lot of excitement around this area in general when you think about design and co-design of these systems i just want to show a few other cool designs that are out there that are very impressive one design that i want to show is this robot on the left this robot on the left is is a dynamic integrity structure so what it is is it basically has a bunch of rigid rigid
38:12 links like these guys and then these links are connected by things that are kind of like ropes and it's kind of like a funny structure but the interesting thing about it is it is shock resistant so like nasa really cares about this robot because landing robots on the surface of mars is really difficult but if i just drop this robot like nothing happens to it and it can just roll around and continue moving forward which is again a very interesting design
38:41 for something that you don't want to to break because robots in general are pretty rigid and and this robot is very flexible so there's a lot of interest in basically building robots that are softer that are less frigid that are flexible and this is an example of that another robot that i think is kind of fun i'll show this a little bit later a video of it there's this robot from stanford this is from mark kotkowski's
39:04 lab and it's called stickybot it's it's basically a robot that has kind of gekko-like hands so it's hands are inspired by like hands of gecko and having like these suction cups and because of that it can climb up like walls and really like slippery like slopes which is again a very interesting design another design that i find super interesting is this inflatable snake robot this is from allison nakamura's lab again in the mechanical engineering at stanford and the idea here is that
39:35 this robot can inflate itself as it goes through different different parts of the environment so it might be really difficult for example to go through go through this hole but like as the robot is inflating it's actually making its way through different like kind of narrow spaces and getting to various areas of the space this can also be used inside of our body for example when we are doing like more intelligence type of endoscopy like we
39:59 can actually send some of these robots inside of the body and navigate a little bit better inside of our body for robotic surgery for things like endoscopies and so on i can take any questions now actually about any of this a quick question so you mentioned that robotic counseling stuff like computer revision machine learning working by the system so what do you recommend for people who want to go into the field do you study all control of their novels or is there
40:32 was a role yeah that's a very good question yeah so it kind of seems like a giant thing right like because it incorporates like everything and and the field of robotics in general when you go to the conferences it is interesting because you see people from all these different fields but but they're coming together for the same problem but not the same technique which is which is a very interesting kind of fields to be in but
40:55 now at the end of the day like everyone just like focuses on their expertise and then brings it together as part of a team so for example robotics and cs like at stanford i'll be talking about that actually a little bit there's a lot focused on developing ai algorithms developing algorithmic side of this but they don't really like to as much on on the design side but robotics in the mechanical engineering is very focused on building new designs new structures
41:20 and yeah we do have a lot of joint projects where we use new designs and try to develop new algorithms for them and lots of collaboration in these different fields so it is a very interdisciplinary field but yeah as i said it might seem too large it's not that large at the end of the day everyone focuses on the thing that they're actually like very interested in or the same goal of building robots i have a question
41:46 what subfields of robotics would be really great to go into a startup for right now so to start it right is that what you yeah so that's a good question to ask yeah so i think there's a lot of excitement around autonomous driving right so and autonomous driving these days is very focused on like vision and machine learning and control theory so like those three kind of like backgrounds are commonly used in autonomous driving but beyond autonomous
42:11 driving i think again a lot of big companies are using them so it's not necessarily startups but beyond that yeah so people are very interested in domestic robots these days right actually like getting robots inside people's houses which wasn't the case like a few years back even right like we have robots functioning very well on factory floors but having robots in our homes is a big problem and there were some startups that weren't very successful so it's kind of like a
42:36 kind of like an edgy area to to be in but i think there's a lot of excitement there like home automation like things like kind of next generation chromebooks or other things that we can have in in homes and again for that i think like a lot of these systems are using machine learning ai in general so that is again like a huge like background that you would want to have but the design is pretty important there
42:58 so like with the type of like hardware design that you use there is actually super important yeah and healthcare like like kind of like thinking about robotics being used in healthcare in hospitals and things of those form i think that is also a very exciting area thank you wanna ask a question yeah so he's kind of like follow up so he's about like a robotics like research because right now like you know like after you said previously
43:25 be like there's a lot of things like happening in ai stuff and also like there's also like something happening control theory so is there any research kind of like moving these two things together because for my undergrad i study also certainly something like control theory and also ai because i find out like in control theory they have like some really interesting concepts actually like stability like observability like all of this have you so there are any research like for example
43:55 using this concept for example like a reinforcement learning center in like learning site apps which is missing like a stability or like some concepts like this is there any research about that yeah there's a lot of excitement actually around that and i totally agree with that right there is a lot of interesting topics in control theory there are a lot of interesting topics in ai and machine learning that are just making their ways into robotics and the two communities at first
44:21 there was a little bit of like clashes between them i would say but now i think there's a lot of like coming together and trying to combine those ideas there's a new conference called learning for dynamics and control of what we see and the whole point of that is actually bring in learning people and control people together to try to use the same ideas yeah lots of research trying to bring learning and control together and i think that is actually
44:44 the right direction because as you said lots of interesting ideas in dynamics and control and i think a lot of those ideas could be used as prior structures that could be put on learning based systems so when you're let's say training a neural network you can bring in structure that you know about the system that comes from control theory let's say i had a question sure sure i was wondering you know you talked about armed forms and
45:12 you know collecting lots of data do you feel like the field is more data limited or more algorithm and learning limited i think about when i learned to drive it wasn't that way it should have been more data but it wasn't it was just maybe a couple weeks of practice and then i was ready that's a very good point so i think it's a combination i do think the field is very data limited and it's interesting because yeah when you learn
45:39 to drive right you spend a couple of weeks but you have seen cars drive right next to you like the learning by observation which is like a very interesting type of learning you're not learning by doing you're learning by like observing other people like driving right next to you and that has so much information in it it's kind of the same problem that i mentioned earlier with the house of cards example right like your autonomous car doesn't know that it's important but
46:02 like you would know that you have so much context like in the world and because of that like i think like the issue is specifically for autonomous cars is some of these corner cases so like driving on a highway like that's like solved right the issue is like some of these corner cases that it hasn't seen yet in the data and maybe more data will solve that so i think more data is definitely a problem i think we can still do better
46:25 on our algorithms too but data is still like i would say that's the higher kind of issue at least in autonomous driving i would say on the data side do you feel that synthetic data could be something that would be useful for machine learning applications or is that something that's kind of like gonna always be a fantasy i think it's super useful right like if you can create near accident driving scenarios and driving and then kind of
46:48 train your car in those settings and then just generate that automatically that would be super useful so then you don't need to wait forever to see like in your accident driving scenario on the vehicle i think one issue there is like this simulation to reality gap which is which is a big problem specifically for robotics but i do think generation of data is important yeah hi so i have a question regarding the deterministic of robotics for example in terms of
47:18 machine regulations my government usually requires that actions of a machine to be state and manufacturers of the machine sometimes responsible for these actions there is a machine learning base or deep learning based algorithms they are statistical by by definition so how could how do we define the responsibility of the manufacturer of the machines and if there's artists that accidentally comes by autonomous driver who should be responsible in this case yeah that's very good that's a very good question
47:49 for tino next week actually so very good question yeah so the first point that you made that yeah like like all the laws are about about deterministic systems that's actually not like always the case so for example michael kochenderfer in the aeronastro department he has done a lot of work around palm vps that actually run on like aircraft systems so there's this acas excuse system which is basically unmanned like aircraft like like all of the all the motions
48:16 landing and taking off and all of that is done in kind of like an online setting but the system is small it's upon vp which with like a few states and all that and you can verify everything about it so there's a lot of interesting rules around verification and validation in this space and even if it is not deterministic you can't still like verify it so the small palm bp for example partially observable markup decision process you won't see that in
48:39 the class if you're interested in that topic take michael's class but when it comes to neural networks yeah we don't really have that much guarantees around it and there's a lot of again discussion here so some people are taking the route of trying to prove things and trying to verify neural networks clark barrett is someone in the cs department who does a lot of work on verification of neural networks again we are limited in the size there so we can't have giant neural
49:03 networks there another kind of perspective on this is giving these statistical guarantees right like if my autonomous car is safer than humans statistically maybe that is good enough and we are okay with some number of accidents some number of times maybe you would be okay with that and some of it is yeah like acceptance issues too right like the first like aircraft that were out there probably not safe probably people were okay with that and the number of deaths
49:28 were like higher right and and i think there's a little bit of that acceptance issue for how this is going to pan out but it is actually a very good question of how are we going to regulate and who is going to be at plane one can take tesla's approach and be like a human is always in control so if anything happens it was it was a human's fault which is kind of like a weird type of type of
49:49 approach i would say it's not necessarily the safest way to go but yeah i don't honestly have good answers for this something to ask tino next week i have a question with regards to the co-design so you mentioned about the hardware and the ai algorithm needs to go hand in hand so for example the self-driving car has to make you know the algorithms have to
50:20 make a quick decisions with the real time change in the environment and if the algorithms take a long time to run and i know the hardware wise there are a variety of ways the algorithm can be put on let's say on a gpu or on other aspects so would you please are there any pointers towards how these two go hand in hand and what is the
50:52 best so i was more thinking about it in an offline kind of fashion right so in an offline kind of fashion you can have like a fancy algorithm that does like everything like that has that takes a lot of compute let's say for a hardware that is very simple or you can kind of like increase complexity of your hardware and on the other hand have a really simple algorithm that runs on it one place that so i wasn't really
51:13 thinking about the online aspect of it which you're right like yeah they're like running at different frequencies so how could they work together one example of that is an assistive robotics assistive teleoperation when you're using a joystick to control a robot arm this is something that you work on commonly and you can make the hardware very interesting very intricate like use haptic devices and then be able to control things like much easier on the other hand you can have a hardware
51:39 that's really simple so for example there are these sip and puff devices that a lot of patients with disabilities use and it's a very simple device you can only like sip and puff like that's the only thing you can do but then the algorithm that's underneath needs to be like much more complicated to be able to capture what that what that zip-and-puff means so so that's kind of like one place that this interplay between hardware and soft hardware and algorithm
52:02 really shows up all right thank you guys yeah let me okay so let me continue a little bit and then i'll stop at the end of this section and i can take more questions too after that i kind of want to show some of these applications too at some point this section is small so the robotics at stanford one yeah so okay so we talked about all of these we talked about the history robotics said stanford had an
52:27 interesting history too so it does have like an old history here's a video that i kind of just wanted to show for fun this is a video from usama kitube slab and sam always has the best videos he has these two robots juliet and romeo the other fun thing in this video for me is lots of these people are like faculty now or like very famous in the field so it's kind of fun to see them as
52:51 grad students at stanford so yeah this is like oliver brock this is gates if you like look at it closely it is first floor updates it hasn't changed much this robot is still on the first floor of gates so if you get a chance to go there it is still like sombra and yeah like this is basically getting this robot helper to help you do various sort of things like move objects for you they have basically two of
53:19 these let me move forward a little bit so it helps you like carry objects and things of those forms it's very old robot and some of these concepts that you study like now even like thinking about interaction between humans like they were actually like they were thinking about this back in the day this is collaborative transport these robots are not decentralized they do actually have a centralized control but they're compliant so meaning that they're not rigid right like if you move it like
53:44 they also get moved yeah so i think and then later on there's this video of dancing with romeo so again it's compliant and it kind of moves around you and all that so this is some old videos from stanford robotics some more recent video of videos of stanford robotics and successes i guess is about the star for grand challenge so it's not that recent it's from 2005. so darpa grand challenge was this
54:16 competition that darpa put out and basically it was a competition that was trying to get researchers to work on autonomous driving and this was this was basically the competition in 2005 where sebastian thrawn was heading the stanford team and stanley was the vehicle and actually won the competition
54:49 in case you don't recognize it that is a volkswagen tourette into the finish line configuration ladies and gentlemen boys and girls it's been done so that is stanley passing the finishing line after this actually sebastian throne left stanford and joined google and started
55:21 the google self-driving car groupter now waymo and yeah lots of advances in autonomous driving since then but this was kind of one of the big successes of stanford robotics winning basically this darpa grand challenge which is very exciting but in general robotics at stanford kind of falls into a bunch of different departments so in computer science here are some of the faculty i just wanted to show their faces so you know who they are and you can
55:47 take classes from them later on and things of those form so usama kiteb i've already showed a lot of videos from his lab i have one more from his lab that i'll show later and someone does a lot of work around field robotics meaning that i'm gonna send robots to places that humans haven't seen before and see what happens which is like really exciting then we have ken salisbury who does a lot of work around like helper robots
56:10 and and building systems that can actually help people sylvia does a lot of work around vision and robotics so he's primarily a vision faculty but he is thinking about that intersection of vision robotics and some of the more recent people have joined including myself jeanette and chelsea so does a lot of work around manipulation i am personally very interested in interaction so when you think about multi-agent interaction or interaction with humans and chelsea does a lot of
56:35 work around robot learning metal learning and things of those form in addition to some of these faculty there are some other folks in the cs department who do a lot of work that is related to robotics so again fei-fei does quite a bit of work and vision but also is interested in robotics that intersection and then karen liu and jojen wu both of them recently joined stanford and they do a ton of work around physical simulation graphics
56:59 things of those form and that has a lot of relations to to building robots that can work with like deformable objects and things of those form and some folks who used to do robotics i guess are andrew and sebastian so i showed a video of andrew's learning from demonstration work earlier the the flying video of the helicopter and then sebastian has done a lot of work in autonomous driving they're both kind of around so andrew andrew does a lot of work in healthcare
57:29 these days lesten comes in he's an adjunct faculty now outside of computer science you still have a lot of robotics faculty so in the aero astro department we have grayscale max schrager and marco pavone and michael kochendurfer i mentioned michael's work around aircraft systems earlier so building these acasu systems and and trying to prove properties around them they all do a lot of work around drones and quadcopters and helicopters things of those form multi-agent systems and being able to
57:58 get guarantees and talk about risk and finally in mechanical engineering we have a good number of faculty allison nakamura sean former mark otkowski steve collins and monroe kennedy a lot of the like al most of almost all of them do quite quite interesting work here on design too so building systems that are actually like interesting and useful the the sticky bot that i showed earlier was from mark slap the snake robot was from from allison's lab sean does a lot of interesting work
58:25 at the intersection of robotics and hci so if that is something you're interested in you should check you should check out like what these faculty teach and and all that so that was kind of my very quick robotics at stanford type of overview let me spend another five minutes showing some of these applications and maybe after that i'll take questions for the last five minutes and i have a seven minute video that i'll just leave after the class for
58:51 you guys to watch it's like a 50-year like history of robotics which is kind of fun all right so i wanted to show you basically generally some exciting applications of robotics and i actually had a hard time classifying it because i think classifying them are can they can be classified with different axes and and it was hard to classify them but i ended up classifying them into three main groups the first group is bio-inspired robots which is basically let's look at biology
59:20 and try to build robots that are useful so it's a lot of interesting design goes there another interesting direction is soft robotics meaning that hey i'm going to build systems that that use soft material so they're the integrity structure was an example of that so they're flexible they're soft they're not rigid they're not going to break i'm not going to actually talk much about soft robots but i'm going to talk about manipulating soft objects which is
59:45 a very difficult algorithmic question and then finally if i have time i will talk a little bit about domestic and interactive robots which is something that i think is really exciting like this interaction with humans is something that you should really care about as robots are basically starting to interact with us all right so bioinspired robots this is more of an like interesting design question so from kind of early on everyone was interested in humanoids because you want robots to look like
60:14 humans for some reason so there's a lot of work around humanoids and building robots that look like humans that is they have two arms and two hands and two legs and a face but at some point people realize robots don't need to look like humans and they started looking at the nature in general and and then started thinking about generally bioinspired robots right there are a lot of a lot of animals that can get to places that humans can't and can
60:41 be built robots that are similar to them and and another interesting topic that shows up here specifically under humanoids is this idea of walking so people have been obsessed with walking for years now and it's an interesting problem like if you want to build a robot that walks kind of like humans that is still very difficult like all walking robots have like weird gates and they don't really like walk like human like and when they do they're
61:08 just super inefficient like humans are just amazing at walking and that's in general very active area of research trying to get robots to walk and why do we care about that well first off it's an interesting question second like building exoskeletons building systems that can help people watch is always an interest in this field so let's look at a few bioinspired robots i only just want to show a lot of videos of these systems so one type of bio-inspired robots is
61:36 looking at insects like cockroaches and try to build robots that kind of act like cockroaches because they're amazing at getting through like obstacles and there's a team at uc berkeley ron fearings group that basically designs robots that are similar to cockroaches and they go through places like cockroaches and the nice thing about it is yeah they're very agile they go through things the other thing is they're small and they can be super fast so you can
62:04 have a swarm of these robots get to places super fast and another interesting thing about cockroaches is when they navigate they use their antennas so that is actually how they figure out where the ball is like using the antenna they figure out they kind of like follow the ball and and basically people in ron's group have have been using similar type of ideas to be able to sense the world and actuate in this world and they even build
62:29 these robots using origami which is kind of interesting but it makes sure that they're small and they're not they don't take as much battery power and energy and and they're kind of like white let me actually move to this one the other robot that i showed you a little bit earlier is the bioinspired robot is the sticky bot from mark slap so basically if you look at gecko feet it has these suction cups that it's very very tiny suction cups that
62:59 gets attached to glass and because of that like this robot is using a similar type of paradigm and it can just like walk up like really slippery slopes like polish granite and that is super impressive thing so lots of cool design going on here similarly i think snake robots and eel robots are very popular there are a lot of like links connected to each other and they can navigate easily this is an elite robot it's an underwater
63:30 robot it does have a camera in the front and based on that it navigates which is kind of cool all right and then this is a this is a hopper so this is basically a robot again from ron's group salto which is kind of like jumps around like a bush baby it's kind of cool if if you guys have used things like mujoku which is a simulation environment you might see these random animals in
64:01 murdocal part of it is roboticists care really about like different types of animals like swimming type of motion hopping and and that's why those show up in this mutuco type environment which is a simulation wise physics simulator that allows you to train things basically in simulation all right so those were those were generally bioinspired type robots as i mentioned earlier we've been obsessed with humanoids so lots of energy and money goes into building humanoids honda actually like spent a lot of
64:32 money on building this this robot called azima it's walking is kind of weird i am happy to be here with you today thank you i'm excited to be here in washington d.c all right i'm gonna cut it there
65:03 but yeah so humanoids have been really exciting and another place that actually humanoids show up is basically when you have one other example i want to show basically is sending humanoids type robots to spaces that you wouldn't be able to send before so i want to show a video of this robot from usama's group ocean one you some of you might have seen this video this is basically not full humanoid it does have two arms so you can
65:33 teleoperate it and get the robot do various things but it goes underwater to places that people have not been able to go before so let's just quickly watch this video it's kind of like a nice video it's almost work ocean one is aimed at bringing a new capability for underwater exploration
66:07 the intent here is to have a diver diving virtually creating a robot that can be the physical representation of the human if you want a robotic diver that can have bi-manual capabilities so it has two hands it has a stereo vision and the most amazing thing about it is that you can feel what the robot is doing while it's sitting up on the boat
66:37 and this is combining the technology of haptics that is the idea that we can reflect the contact forces it's almost like you are there with the sense of touch you create a new dimension of perception this robot is oil filled it allows us to take the robot very deep this robot can go to thousands is this stanford truly a human-like machine that is also human-friendly
67:09 essentially shipwrecked located about 20 miles off the coast of toulon in france at 100 meters in the last year we have been working and getting our robots ready to take on that expedition we are going to land on the moon more than 70 percent of the surface of the planet is water we have a lot of structures a lot of polar potteries to
67:42 monitor we need to reach down there you can think about it as a solution that much time i'm going to kind of like move forward because probably the last thing i want to show is the walking video and then i'll close it and ask for questions but yeah like in general if you're interested in underwater robots kusama does a lot of work around that he's at stanford he teaches classes talk to him i think that's a very interesting
68:10 direction and then finally i guess the last video that i can show under this category is this idea of walking and jumping in things of those forms so so there's a lot of again excitement around that and boston dynamics the first video i showed you guys for the dancing robot was also from boston dynamics does a lot of work around building very dynamic robots so they have really good controllers for these robots and this is atlas
68:38 from austin dynamics jumps flips even that is super impressive
69:11 and usually roboticists show that one video of it working and don't talk about videos of it not working but more recently people are showing more videos of things not exactly working so i think this video it actually it's super impressive how it recovers because that is really hard to do in real time that was a failure so yeah so okay so lots of excitement around these areas i can start taking questions now i'm not
69:42 gonna show more videos i have more stuff to show but let me just like answer a couple of questions and then i'll just at the end i took 220 i'll leave this video up 50 years of robotics that osama put together and has fun music so any questions i had a question i was super impressed by the last video you showed of the robot doing flips i was wondering it looked really heavy like it had a lot of
70:07 materials and i was wondering if like why they chose to equip it with such materials i i thought you know maybe using lighter materials so it could be easier to jump i was just curious if you knew the reason behind how they designed that robot that's a very good question yeah so i don't know the details of it because i don't personally like work too much on like walking and like the design side of things but yeah i actually don't know like what
70:31 material they're using they definitely do consider different types of material and making sure there's lightweight and all that but i think there's just a lot of like joints and a lot of stuff going on with that robot if you're interested to learn more about that yeah boston check out like boston dynamics basically website they have all their other cool robots there i have a question so i was wondering what's some examples of state-of-the-art research
71:02 involved both robotics and nlp or language based ai for example voice activated systems or related work yeah there's a lot of excitement around actually nlp and robotics i actually have a student joined between me and percy which is very exciting this is like first time doing nlp robotics together there's a lot of work around instruction following so basically just making an interactive teaching so when you have a person and a robot at home like how would the robot learn that you
71:32 care about house of cards or you don't care about house of cards type of type of a thing so thinking about human robot interaction a little bit more carefully than you actually have access to nlp so so that is one place that it shows up another place that is a little bit harder to think about but i think it has a lot of value is thinking about the large data set of natural language data that we have
71:55 generally like we have a lot of like text data and from that if you can from that learn something about context learn something about how a robot should like cook an egg like i think that is that is very interesting too and i haven't seen that much work around it but again lots of lots of excitement in that particular intersection of nlp and robotics hi i had a question as well maybe this goes more into the scope of
72:23 visual recognition but robots will be playing a part in this too so the world unfortunately will always consist of good factors and evil factors and for international security purposes there will be a role if there already is a rule for robots and autonomous systems but those same methods can unfortunately also be used for human rights violations too how do you build it so that any technology will always be neutral is its use that determines what the outcome is
72:47 but in the case of human rights violations how can you build systems so that an authoritarian regime or what would be the way to using technology to evade an authoritarian regime that will have the most best technology i've seen some works around how to full facial recognitions how can technology work against technology when it's needed and also serve its purpose i think it's a tough question it is a very tough question i refer that to tino next week specifically but in
73:17 the case of vision and in the case of like using ml like in general like machine learning i think it is much like much tougher in the last lecture i will talk about this a little bit like this idea of like fooling like neural networks there's some recent work around basically showing that you can always find adversarial examples so this idea of trying to like safeguard your system so it doesn't get affected by adversarial examples is just not going
73:40 to work like there's proof by auditioning here and others who have actually shown that you can always find like basically an adversarial example in some settings under some distances in the case of robotics i've been part of some discussions around this idea of autonomous weapon systems as i mentioned earlier for those discussions there are discussions on the number of drones that for example can be purchased at the same time and things of those forms a lot of concerns there is
74:10 basically autonomous weapon systems becoming weapon of mass destruction which is kind of scary like as i talk about it but yeah like discussions around that is what sort of limitations can be put what sort of regulations can be put there so like people don't buy too many drones at the same time and weaponize it let's say like things of those forms but i'm definitely not an expert in this i refer you to tina hoyler next week
74:36 on more details on this thank you thanks so much i have a question regarding among all these since robotic is such an integrated subject like it integrates like mechanical engineering artificial intelligence and also power management and also regulations what is that the biggest limiting factor that prevents robotics from like affecting everyone's life from being like widely adopted and by being widely adopted they're
75:07 still like dealing with uncertainty is still like so difficult right so like you have robots in factory floors confined spaces they can move around so much easily putting it in a world where humans are just like walking around it there's so many reasons that the human could walk around it and like figuring out what those reasons are can be really difficult so in general dealing with uncertainty dealing with in the case of autonomous driving or dealing with things like near accident
75:31 scenarios that it hasn't seen before all of those like that uncertainty is like a big factor that's not allowing us to have robots like out there like widely used in our everyday lives so given the current ai technology mostly are based on learning algorithms but if you keep doing the learning algorithm that means you can only like learn existing behaviors so in order to deal with these uncertainties are there any efforts to deal with uncertainties in life or do something
76:00 like a self-generated motion or self-motivated actions from the robots itself yeah so yeah definitely yeah so there's a lot of work around like actively generating these scenarios active learning in this domain so the robot had but the robot still has some sort of hypothesis space that it can search in right so like you have a hypothesis space of things that can happen and within that you can search and yeah so so like there are these things that
76:28 are called known unknowns and unknown unknowns you can't really do much around unknown unknowns better than like other than just randomly experiencing them but for known unknowns yeah definitely like there's a lot of work on actively looking for the most informative data i guess another reason that we don't have robots widely used is it's such an integrated system and it's such an interconnected system so you have like the best ai algorithm and all of a sudden your camera fails
76:55 like the hardware failure can affect like there's so many things that can fail in that pipeline that makes it just such a difficult system to debug it's like everything coming together so all right so i'm going to just leave this video on and then at the end of it i'm going to sign off because it's so fun it has fun music osama again made this osama is awesome in making music but if you have more questions about these things just come
77:25 to the office hour and i'll be happy to answer any questions let's just watch 50 years history of robotics so if you guys want to sign off sign up to i'll talk to you later this is seven minutes all right so
78:02 so
78:40 wow is
79:51 yes thank you
81:12 oh
81:48 foreign
84:35 all right that was kind of long but kind of fun so this is for repress 2020 okay do they still have 30 people oh my god okay all right good seeing you all that's it i'll see you at office hours in our next lecture later bye