Vijay Kumar: Flying Robots | Lex Fridman Podcast #37
the following is a conversation with Vijay Kumar he's one of the top roboticist in the world a professor at the University of Pennsylvania a Dean Afeni engineering former director of grasp lab or the general robotics automation sensing in perception laboratory a pen that was established back in 1979 that's 40 years ago Vijay is perhaps best known for his work in multi robot systems robot swarms and micro aerial vehicles robots that elegantly cooperate in flight under all the uncertainty and challenges that the real-world conditions present this is the artificial intelligence podcast if you enjoy it subscribe on YouTube give it five stars and iTunes supported on patreon simply connect with me on Twitter at lex friedman spelled fri d ma an and now here's my conversation with Vijay Kumar what is the first robot you've ever built over a part of building way back when I was in graduate school I was part of a fairly big project that involved building a very large hexapod suede close to 7,000 pounds and it was powered by hydraulic actuation or was actuated by hydraulics with 18 motors hydraulic motors controlled by an Intel 80-85 processor and an internal 8086 coprocessor and so imagine this huge monster that had 18 joints each controlled by an independent computer and there was a 19th computer that actually did the coordination between these 18 joints so as part of this project and my thesis work was how do you coordinate the 18 legs and in particular the the pressures and the hydraulic cylinders to get efficient locomotion it sounds like a giant mess so how difficult is it to make all the motors communicate presumably you have to send signals hundreds of times a second or at least this was not my work but the folks who worked on this wrote what I believe to be the first multiprocessor operating system this was in the 80s and you have to make sure that obviously messages got across from one joint to another you have to remember the the clock speeds on those computers were about half a megahertz right the eighties so not to romanticize the notion but how did it make you feel to make to see that robot move it was amazing in hindsight it looks like well we built this thing which really should have been much smaller and of course today's robots are much smaller you look at you know Boston Dynamics or ghost robotics has been off from from Penn but back then you're stuck with the substrate you had the compute you had so things were unnecessarily big but at the same time and this is just human psychology somehow bigger means grander you know people never had the same appreciation for nanotechnology or nano devices as they do for the space shuttle or the Boeing 747 yeah you've actually done quite a good job at illustrating that small is beautiful in terms of robotics so what is on that topic is the most beautiful or elegant robe in motion that you've ever seen not to pick favorites or whatever but something that just inspires you that you remember well I think thing that I'm I'm most proud of that my students have done is really think about small UAVs that can maneuver and constrain spaces and in particular their ability to coordinate with each other and form three-dimensional patterns so once you can do that you can essentially create 3d objects in the sky and you can deform these objects on the fly so in some sense your toolbox of what you can create has suddenly got enhanced and before that we did the two-dimensional version of this so we had ground robots forming patterns and and so on so that that was not as impressive it was not as beautiful but if you do it in 3d suspended in midair and you've got to go back to 2011 when we did this now it's actually pretty standard to do these things eight years later but back then it was a big accomplishment so the distributed cooperation is where is what Beauty emerges in your eyes I think beauty to an engineer is very different from from Beauty to you know someone who's looking at robots from the outside if you will yeah but what I meant there so before we said that grand is associated with size and another way of thinking about this is just the physical shape and the idea that you can get physical shapes in midair and have them deform that's beautiful but the individual components the agility is beautiful too right so then how quickly can you actually manipulate these three-dimensional shapes and the individual components yes right oh by the way said UAV unmanned aerial vehicle what was a good term for drones UAVs quad copters is there a term that's then being standardized I don't know if that is everybody wants to use the word drones and I often said there's drones to me is a pejorative word it signifies something that's that's dumb the pre program that does one little thing and to anything but drones so I actually don't like that word but that's what everybody uses you could call it unpiloted and paladin ah but even unpiloted could be radio control could be remotely controls in many different ways and I think the right word is thinking about it is an aerial robot you also say agile autonomous aerial robot right yeah so agility is an attribute but they don't have to be so what biological system because you've also drawn a lot of inspiration of those I've seen bees and ants that you've talked about what living creatures have you found to be most inspiring as an engineer instructive in your work in robotics to me so ants are really quite incredible creatures right so you I mean the individuals arguably are very simple and how they're they're built and yet they're incredibly resilient as a population and as individuals they're incredibly robust so you know if you take an ant at six legs you remove one like it still works just fine and it moves along and I don't know that even realizes it's lost alike so that's the robustness at the individual ant level but then you you look about this instinct for self-preservation of the colonies and they adapt in so many amazing ways you know transcending transcending gaps and and by just chaining themselves together when you have a flood being able to recruit other team mates to carry big morsels of food and then going out in different directions looking for food and then being able to demonstrate consensus even though they don't communicate directly with each other the way we communicate with each other in some sense they also know how to do democracy probably better than what we do yeah somehow the even democracy is emergent it seems like all the phenomena that we see is all emergent it seems like there's no centralized communicator there is so that I think a lot is made about that word emergen and means lots of things of different people but you're absolutely right I think as an engineer you think about what element elemental behaviors were primitives you could synthesize so that the whole looks incredibly powerful incredibly synergistic the whole definitely being greater than the sum of the parts and ants are living proof of that so when you see these beautiful swarms where there's biological systems of a robots do you sometimes think of them as a single individual living intelligent organism so it's the same as thinking of our human civilization as one organism or do you still as an engineer think about the individual components and all the engineering that went into the individual components oh that's very interesting so again philosophically as engineers what we want to do is to go beyond the individual components the individual units and think about it as a unit as a cohesive unit without worrying about the individual components if you start obsessing about the individual building blocks and what they do you inevitably will find it hard to scale up and just mathematically just think about individuals things you want a model and if you want to have ten of those then you essentially are taking cartesian products of ten things that makes it really complicated than to do any kind of synthesis or design and that high dimensional space is really hard so the right way to do this is to think about the individuals in a clever way so that at the higher level when you look at lots and lots of them abstractly you can think of them in some low dimensional space so what does that involve for the individual you have to try to make the way they see the world as local as possible and the other thing do you just have to make them robust to collisions like you said with the ants if something fails that the whole swarm doesn't fail right I think as engineers we do this I mean you know think about we build planes will rebuild iPhones and we know that by taking individual components well engineered components with well specified interfaces that behave in a predictable way you can build complex systems so that's ingrained I would I would claim and most engineers thinking and it's true for computer scientists as well I think what's different here is that you want the individuals to be robust in some sense as we do in these other settings but you also want some degree of resiliency for the population and so you really want them to be able to re-establish communication with their neighbors you want them to rethink their strategy for group behavior you want them to reorganize and that's where I think a lot of the challenges lie so just at a high level what does it take for a bunch of which we call them flying robots to create a formation just for people when I familiar with robotics in general how much information is needed how do you how do you even make it happen without a centralized controller so I mean there are a couple of different ways of looking at this if you are a purist you think of it as a as a way of recreating what nature does so nature forms groups for several reasons but mostly it's because of this instinct that organisms have of preserving their colonies their population which means what you need shelter you need food you need to procreate and that's basically it so the kinds of interactions you see are all organic they're all local and the only information that they share and mostly it's indirectly is to again preserve the herd of the flock or the swarm in and either by looking for new sources of food are looking for new shelters right as engineers when we build swarms we have a mission and when you think of a mission and it involves mobility most often it's described in some kind of a global coordinate system as a human as an operator as a commander or as a collaborator I have my coordinate system and I want the robots to be consistent with that so I might think of it slightly differently I might want the robots to recognize that coordinate system which means not only do they have to think locally in terms of who their immediate neighbors are but they have to be cognizant of of what the global environment looks like so if I go if I say surround this building and protect this from intruders well they're immediately in a building centered coordinate system and I have to tell them where the building is and they're globally collaborating on the map of that building there they're maintaining some kind of global not just in the frame of the building but there's information that's ultimately being built up explicitly as opposed to kind of implicitly like nature might correct correct so in some sense nature is very very sophisticated but the tasks that nature solves or needs to solve are very different from the kind of engineered tasks artificial paths that we are Forrester address and again there's nothing preventing us from solving these other problems but ultimately through our impact you want these forms to do something useful and so you're kind of driven into this very unnatural if you will unnatural meaning not like how nature does setting and it's a little probably a little bit more expensive to do it the way nature does because nature is less sensitive to the loss of the individual and cost wise in robotics I think you're more sensitive to losing individuals I I think that's true although if you look at the price to performance ratio of robotic components it's it's coming down dramatically right it continues to come down so I think we're asymptotically approaching the major where we would get yeah the cost of individuals will really become insignificant yeah so let's step back at a high low of you the impossible question of what kind of as an overview what kind of autonomous flying vehicles are there in general I think the ones that receive a lot of notoriety are obviously the military vehicles military vehicles are controlled by a base station but have a lot of human supervision but I have limited autonomy which is the ability to go from point A to point B and even the more sophisticated now sophisticated vehicles can do autonomous takeoff and landing and those usually have wings and they're heavy usually their wings but then there's nothing preventing us from doing this for helicopters as well so I mean there are many military organizations that have autonomous helicopters in the same vein and by the way you look at auto pilots and airplanes and it's it's actually very similar in fact I can one interesting question we can ask is if you look at all the air safety violations all the crashes that occurred yeah would there happen if the plane were truly autonomous and I think you'll find that any other cases you know because of pilot error we made silly decisions and so in some sense even an air-traffic commercial air traffic there's a lot of applications although we only see autonomy being enabled at very high altitudes when when the pilot to the the plane is on autopilot there's still a role for the human and that kind of autonomy is you're kind of implying I don't know what the right word is but it's a little dumb dumber and it could be right so so in the lab right course we could we can we can afford to be a lot more aggressive and the question we try to ask is can we make robots that will be able to make decisions without any kind of external infrastructure right so what does that mean so the most common piece of infrastructure that airplanes use today is GPS GPS is also the most brutal form of information if you have driven in a city tried to use GPS navigation you know in tall buildings you immediately lose GPS and so that's not a very sophisticated way of building autonomy I think the second piece of infrastructure they rely on is communications again it's very easy to jam communications in fact if you use Wi-Fi you know that Wi-Fi signals drop out cell signals drop out so to rely on something like that is not is not good the third form of infrastructure we we use and I hate to call it infrastructure but but it is that in the sense of robots it's people so you could rely on somebody to pilot you right and so the question you want to ask is if there are no pilots there's no communications of any base station if there's no knowledge of position and if there's no a priori map a priori knowledge of what the environment looks like a priori model of what might happen in the future can robots navigate so that is true autonomy right so that's that's true autonomous and we're talking about you may like military applications and drones okay so what else is there you talk about agile autonomous flying robots aerial robots so that's a different kind of it's not winged it's not big at least its small so I used the word agility mostly or at least we're motivated to do agile robots mostly because robots can operate and should be operating in constrained environments and if you want to operate the way a Global Hawk operates I mean the kinds of conditions in which you operate are and very very restrictive if you go want to go inside a building for example for search and rescue or to locate an active shooter or you want to navigate under the canopy in an orchard to look at health of plants or to look for to count to count fruits to measure the tree the tree trunks these are things we do by the way as cool agriculture stuff you've shown in the past is really alright so in those kinds of settings you do need that agility agility and does not necessarily mean you break records for the hundred meters - what it really means is you see the unexpected and you're able to maneuver in a safe way and in a way that that gets you the most information about the thing you're trying to do by the way you may be the only person who in a TED talk has used a math equation which is amazing people should go see what actually it's very interesting because the Ted curator Chris Anderson told me you can't show math and you know I thought about it but but that's who I am and that's that's what that's our work and so I felt compelled to give the audience a taste for at least some math so on that point simply what does it take to make a thing with four motors fly a quadcopter one of these little flying robots you know how hard is it to make it fly how do you coordinate them four motors what's how do you convert there's those motors into actual movement so this is an interesting question we've been trying to do this since 2000 it is a commentary on the sensors that were available back then the computers that were available back then and a number of things happened between 2000 and 2007 one is the advances in computing which is and so we all know about Moore's law but I think 2007 was a tipping point the year of the iPhone the year of the cloud lots of things happen in 2007 but going back even further inertial measurement units as a sensor really matured again lots of reasons for that certainly there's a lot of federal funding particularly DARPA in the US but they didn't anticipate this boom in I amuse but if you look subsequently what happened is it every year every car manufacturer had to put an airbag in which meant you had to have an accelerometer onboard and so that drove down the price to performance ratio oliver's so I should know this that's very interesting yeah it's very interesting the connection there and that's why research is very it's very hard to predict the outcomes and again the federal government spent a ton of money on things that they thought were useful for resonators but it ended up enabling these small UAVs yeah which is great because I could have never raised that much money and told you no soul this project hey we want to build these small UAVs can you can you actually fund the development of low-cost dire news so why do you need an IMU and so so so I was I'll come back to that but but so in 2007 2008 we were able to build these and then the question you're asking was a good one how do you coordinate the motors to develop this but over the last 10 years everything is commoditized a high school kid today can pick up a Raspberry Pi kit and build us all the low levels functionality is all automated but basically at some level you have to drive the motors at the right rpms the right velocity in order to generate the right amount of thrust in order to position it and orient it in a way that you need to in order to fly the feedback that you get is from onboard sensors and the IMU is an important part of it the IMU tells you what the acceleration is as well as what the angular velocity is and those are important pieces of information in addition to that you need some kind of local position or velocity information for example when we walk we implicitly have this information because we kind of know how how would our stride length is we also are looking at images fly past our retina if you will and so we can estimate velocity we also have accelerometers in our head and we're able to integrate all these pieces of information to determine where we are as we walk and so robots have to do something very similar you need an IMU you need some kind of a camera or other sensor that's measuring velocity and then you need some kind of a global reference frame if you really want to think about doing something in a world coordinate system and so how do you estimate your position with respect to that global reference frame that's important as well so coordinating the RPMs of the four motors is what allows you to first of all fly and hover and then you can change the orientation and the velocity of the and so on exactly exactly bunch of degrees of freedom six degrees of freedom but you only have four inputs the four motors and and it turns out to be a remarkably versatile configuration you think at first well I only have four motors how do I go sideways but it's not too hard to say well if I tell myself I can go sideways and then you have four motors pointing up how do i how do I rotate in place about a vertical axis well you rotate them at different speeds and that generation reaction moments in that allows you to turn so it's actually a pretty it's an optimal configuration from from engineer standpoint it's it's very simple very cleverly done and and very versatile so if you could step back to a time so I've always known flying robots as the to me it was natural that the quadcopter should fly but when you first started working with it like how surprised are you that you can make do so much with the four motors how surprising is e this thing fly first of all you can make it hover then you can add control to it firstly this is not the four motor configuration is not ours you could it has at least a hundred year history and with various people various people try to get quad rotors to fly without much success as I said we've been working on this since 2000 our first designs were well this is way too complicated why not we try to get an omnidirectional flying robots or so our early designs we had eight folders and so these eight rotors were arranged uniformly on a sphere if you will so you can imagine a symmetric configuration and so you should be able to fly anywhere but the real challenge we had is the strength to weight ratio is not enough and of course we didn't have the sensors and so on so everybody knew or at least the people who work with rotor crafts knew four rotors we get it done so that was not our idea but it took a while before we could actually do the onboard sensing and the computation that was needed for the kinds of agile maneuvering that we wanted to do in our little aerial robots and that only happened between 2007 and 2009 in our life yeah and you have to send the signal may hundred times a second so the compute there is everything has to come down in price and what are the steps of getting from point A to point B so you just talked about like local control but if all the kind of cool dancing in the air that I've seen you show how do you make it happen it would have trajector make a trajectory first of all okay figure out a trajectory so planet trajectory and then how do you make that trajectory happen I think planning is a very fundamental problem in robotics I think you know 10 years ago it was an esoteric thing but today with self-driving cars you know everybody can understand this basic idea that a car sees a whole bunch of things and it has to keep a lane or maybe make a right turn or switch lanes it has to plan a trajectory it has to be safe it has to be efficient so everybody's familiar with that that's kind of the first step that that you have to think about when you when you when you when you say autonomy and so for us it's about finding smooth motions motions that are safe so we think about these two things one is optimality we want a safety clearly you don't you cannot compromise safety so you're looking for safe optimal motions the other thing you have to think about is can you actually compute a reasonable trajectory in a fast manner in a small amount of time because you have a time budget so the optimal becomes suboptimal but in our lab we we focus on synthesizing smooth trajectory that satisfy all the constraints in other words don't violate any safety constraints and is as efficient as possible and when I say efficient it could mean I want to get from point A to point B as quickly as possible or I want to get to it as gracefully as possible or I want to consume as little energy as possible but always staying within the safety constraints but yes always finding a safe trajectory so there's a lot of excitement and progress in the field of machine learning yes and reinforcement learning and the neural network variant of that with deep reinforcement learning DS do you see a role of machine learning in so a lot of the successful flying robots did not rely on machine learning except for maybe a little bit of the perception the computer vision side on the control side and the planning do you see there's a role in the future for machine learning so let me disagree a little bit with you I think we never perhaps called out and my work called out learning but even this very simple idea of being able to fly through a constrained space the first time you try it you'll invariably you might get it wrong even if the task is challenging and the reason is to get it perfectly right you have to model everything in the environment and flying is notoriously hard to model there are aerodynamic effects that we constantly discover even just before I was talking to you I was starting to a student about how blades flap when they fly well and that ends up changing how a rotor craft is accelerated in the angular direction this is like micro flaps or something is smooth it's not microfiber you assume that each blade is is rigid but actually it flaps a little bit oh it bends interesting yeah and so the models rely on the fact on the on an assumption that they're they're actually rigid but that's not true if you're flying really quickly these effects become significant if you're flying close to the ground you get pushed off by the ground right something which every pilot knows when he tries to land or she tries to land this is called a ground effect something very few pilots think about is what happens when you go close to a ceiling well you get sucked into a ceiling there are very few aircrafts that fly close to any kind of ceiling likewise when you go close to close to a wall there are these wall effects and if you've gone on a train and you pass another train that's traveling in the opposite direction you feel the buffeting and so these kinds of microclimates effect our UAVs significantly so impossible to model essentially if I wouldn't say they're impossible to model but the level of sophistication you would need in the model and the software would be tremendous plus to get everything right would be awfully tedious so the way we do this is over time we figure out how to adapt to these conditions so we've early on we use the form of learning that we call iterative learning so this idea if you want to perform a task there are a few things that you need to change and iterate over few parameters that over time you can you can you can figure out so I could call it policy gradient reinforcement learning but actually this is iterative learning learning and so this was their way back I think what's interesting is if you look at autonomous vehicles today learning occurs could occur in two pieces one is perception understanding the world second is action taking actions everything that I've seen that is successful is on the perception side of things so in computer vision we've made amazing strides in the last ten years so recognizing objects actually detecting objects classifying them and and tagging them in some sense annotating them this is all done through machine learning on the action side on the other hand I don't know if any examples where there are fielded systems where we actually learn the right behavior outside a single demonstration of successfully you know in the laboratory this is the holy grail can you do end-to-end learning can you go from pixels to motor block mode occurrence this is really really hard and I think if you look go forward the right way to think about these things is data driven approaches learning based approaches in concert with model-based approaches which is the traditional way of doing things by so I think there's a piece there's a role for each of these methodologies so what do you think just jumping out in topic since you mention autonomous vehicles what do you think are the limits and the perception sighs so I've talked to Elon Musk and they're on the perception side they're using primarily computer vision to see the environment in your work with because you work with a real world a lot in the physical world what are the limits of computer vision do you think you can solve autonomous vehicles focus on the perception side focusing on vision alone and machine learning so you know we also have a spin-off company X and technologies that that works underground in mines you go into mines there they're dark they're dirty you fly in a dirty area there's stuff you kick up from by the propellers the downwash kicks up dust I challenge you to get a computer vision algorithm to work there yeah so we used lighters in that setting indoors and even outdoors when we fly through fields I think there's a lot of potential for just solving the problem using computer vision alone but I think the bigger question is can you actually solve or can you actually identify all the corner cases using a sense single sensing modality and using learning alone so what's your intuition there so look if you have a corner case and your algorithm doesn't work your instinct is to go get data about the corner case and patch it up learn how to deal with that corner case but at some point this is going to saturate this approach is not viable so today computer vision algorithms can detect 90% of the objects or can detect objects 90% of the time classify them 90% of the time cats on the Internet I probably can do 95 percent on here but to get from 90% to 99% you need a lot more data and then I tell you well that's not enough because I have a safety critical application I want to go from 99% to 99.9 percent that's even more data so I think if you look at wanting accuracy on the x-axis and look at the amount of data on the y-axis I believe that curve is an exponential curve Wow okay it's even hard if it's linear it's hard if it's linear totally but I think it's exponential and the other thing you have to think about is that this process is a very very power hungry process to run data farms or solar power you mean literally power literally power literally power so in 2014 five years ago and I don't have more recent data two percent of US electricity consumption was from data forms so we think about this as an information science and information processing problem actually it is an energy processing problem and so unless we figured out better ways of doing this I don't think this is viable so talking about driving which is a safety critical application and some aspect the flight is safety critical maybe philosophical question maybe an engineering one what problem do you think is harder to solve autonomous driving or autonomous flight that's a really interesting question I think autonomous flight has several advantages that autonomous driving doesn't have so look if I want to go from point A to point B I have a very very safe trajectory go vertically up to a maximum altitude fly horizontally to just about the destination and then come down vertically this is pre-programmed the equivalent of that is very hard to find in a self-driving car car world because you're on the ground you're in a two-dimensional surface and the trajectories in the two-dimensional surface are more likely to encounter obstacles I mean this in an intuitive sense but mathematically true that's mathematically as well that's true there's another option on the 2g space of platooning or because there's so many obstacles you can connect with those obstacles and all these those exist in the three-dimensional space is wrong so they do so the question also implies how difficult are obstacles in the three-dimensional space in flight so so that's the downside I think in three-dimensional space you're modeling three-dimensional world not just just because you want to avoid it but you want a reason about it you want to work in the three-dimensional environment and that's significantly harder so that's one disadvantage I think the second disadvantage is of course anytime you fly you have to put up with the peculiarities of aerodynamics and they're complicated environments how do you negotiate that so that's always a problem if you see a time in the future where there is you mentioned them there's an agriculture application so there's a lot of applications of flying robots but do you see a time in the future where there is tens of thousands or maybe hundreds of thousands of delivery drones they fill the sky a delivery flying robots I think there's a lot of potential for the last mile delivery and so in crowded cities I don't know if you go if you go to a place like Hong Kong just crossing the river can take half an hour and while a drone can just do it in in five minutes at most I think you look at a delivery of supplies to remote villages I work with a nonprofit called weave robotics they work in the Peruvian Amazon where the only highways are rivers and to get from point A to point B may take five hours while with a drone you can get there in 30 minutes so just delivering drugs retrieving samples for for testing vaccines I think there's huge potential here so I think if the challenges are not technological that the challenge is economical the one thing I'll tell you that nobody thinks about is the fact that we've not made huge strides in battery technology yes it's true batteries are becoming less expensive because we have these mega factories that are coming up but they're all based on lithium based technologies and if you look at the energy density and the power density those are two fundamentally limiting numbers so power density is important because for a UAV to take off vertically into the air which most drones do they're not they don't have a runway you consume roughly two and watts per kilo at the small size that's a lot right in contrast the human brain consumes less than 80 watts the whole of the human brain so just imagine just lifting yourself into the air is like two or three lightbulbs which makes no sense to me yes so you're going to have to at scale solve the energy problem then charging the batteries storing the the energy and so on and then the storage is the second problem but storage limits the range but you know you you you have to remember that you you have to you have to burn a lot of it for a given time so the turning which is the pop which is a power question yes and do you think just your intuition there there are breakthroughs in batteries on the horizon how hard is that problem look there are a lot of companies that are promising flying cars but there are autonomous and that are clean I think there over-promising the autonomy piece is durable the clean piece I don't think so there's another company that I work with called jet otra they make small jet engines hmm and they can get up to 50 miles an hour very easily and left 50 kilos but their jet engines they're efficient there are little louder than electric vehicles but they can bail flying cars so your sense is that there's a lot of pieces that have come together so on this crazy question if you look at companies like Kitty Hawk working on electrics of clean I'm talking to Sebastian Thrun right it's a it's a crazy dream you know but you work with flight a lot you've mentioned before that manned flights are carrying of the human body is very difficult to do so how crazy is flying cars do you think there will be a day when we have vertical takeoff and landing vehicles that are sufficiently affordable that we're going to see a huge amount of them and they would look like something like we dream of when we think about flying cars yeah like the Jetsons The Jetsons yeah so look there are a lot of smart people working on this and you never say something is not possible when you're people like Sebastian Thrun working on it so I totally think it's viable I question again the electric piece they let you pee on it and again for short distances you can do it and there's no reason to suggest that these are all just have to be rotorcraft you take off vertically but the new morph into a forward flight I think there are a lot of interesting designs the question to me is is are these economically viable and if you agree to do this with fossil fuels that instinct immediately becomes viable that's a real challenge do you think it's possible for robots and humans to collaborate successfully on tasks so a lot of robotics folks that I talk to and work with I mean humans just add a giant mess to the picture so it's best to remove them from consideration when solving specific tasks it's very difficult to model there's just a source of uncertainty in your work with these agile flying robots do you think there's a role for collaboration with humans or is it best to model tasks in a way that that doesn't have a human in the picture well I I don't think we should ever think about robots without human in the picture ultimately robots are there because we want them to solve problems for humans right but there is no general solution to this problem I think if you look at human interaction and how humans interact with robots you know we think of these in three different ways one is the human commanding the robot the second is the human collaborating with the robot so for example we work on how a robot can actually pick up things with a human will carry things that's like true collaboration and third we think about humans as by standards so driving cars what's the human's role and how does how do self-driving cars acknowledge the presence of humans so I think all of these things are different scenarios it depends on what kind of humans were kind of tasks and I think it's very difficult to say that there's a general theory that we all have for this but at the same time it's also silly to say that we we should think about robots independent of humans so to me human robot interaction is almost a mandatory aspect of everything we do yes so but the Jewish to agree so with your thoughts if you jump to autonomous vehicles for example there's a there's a big debate between what's called level 2 and level 4 so semi autonomous and autonomous vehicles instead of the Tesla approach currently at least has a lot of collaboration between human and machine so the human is supposed to actively supervise the operation of the robot the part of the safety a definition of how safe a robot is in that case is how effective is the human and monitoring it do you think that's ultimately not a good approach in sort of having a human in the picture not as a bystander or part of the infrastructure but really as part of what's required to make the system safe this is harder than it sounds yes I think you know if you if you if I mean I'm sure you've driven the driven before and highways and so on it's it's really very hard to have to relinquish controls to a machine and then take over when needed so I think Tesla's approach is interesting because it allows you to periodically establish some kind of contact with the car Toyota on the other hand is thinking about shared autonomy as a collaborative autonomy as a paradigm if I may argue these are very very simple ways of human-robot collaboration because the task is pretty boring you sit in a vehicle you go from point A to point B I think the more interesting thing to me is for example search-and-rescue I've got a human first responder robot first responders I got to do something it's important I have to do it in two minutes the building is burning there's been an explosion it's collapsed how do I do it I think to me those are the interesting things where it's very very unstructured and what's the role of the human was the role robot clearly there's lots of interesting challenges and as a field I think we're gonna make a lot of progress in this area yeah it's an exciting form of collaboration you're right in the town was driving the main enemy is just boredom of the human yes as opposed to in rescue operations it's literally life and death and the collaboration enables the effective completion of the mission so exciting in some sense you know we're also doing this you think about the human driving a car and almost invariably the humans trying to estimate the state of the cars estimate the state of the environment so on but what if the car were to estimate the state of the human so for example I'm sure you have a smartphone the smartphone tries to figure out what you're doing and send you reminders and often times telling you to drive to a certain place although you have no intention of going there because it thinks that that's why you should be a cause of some gmail calendar entry or something like that and and you know it's trying to constantly figure out who you are what you're doing if a car were to do that maybe that would make the driver safer because the car's trying to figure out is the driver paying attention looking at his or her eyes looking at saccadic movements so I think the potential is there but from the reverse side it's not robot modeling but it's human modeling it's more in the human right and I think the robots can do a very good job of modeling humans if you if you really think about the framework that you have a human sitting in a in a cockpit surrounded by sensors all staring at him in addition to be staring out staring outside but also staring at him I think there's a real synergy there yeah I love that problem because it's a new 21st century form of psychology actually AI enabled psychology a lot of people have sci-fi inspired fears of walking robots like those from Boston Dynamics if you just look at shows on Netflix and so on or flying robots like those you work with how would you how do you think about those fears how would you alleviate those fears do you have Inklings echos of those same concerns you know anytime we develop a technology meaning to have positive impact in the world there's always a worry that you know somebody could subvert those technologies and use it in an adversarial setting and robotics is no exception right so I think it's very easy to weaponize robots I think we talked about swarms one thing I worry a lot about is so you know for us to get swamps to work and do something reliably it's really hard but suppose I have there's this challenge of trying to destroy something and I have a swarm of robots well only one out of the swarm needs to get to its destination so that suddenly becomes a lot more doable worry about you know this gentle idea of using autonomy with lots and lots of agents I mean having said that looked a lot of this technology is not very mature my favorite saying is that if somebody had to develop this technology wouldn't you rather the good guys do it so the good guys have a good understanding of the technology so they can figure out how this technology is being used in a bad way or could be used in a bad way and try to defend against it so we think a lot about that so we have a were doing research on how to defend against swarms for example that's a there's in fact a report by the National Academies on counter UAS technologies this is a real threat but we're also thinking about how to defend against this and and knowing how swarms work knowing how autonomy works is I think very important so it's not just politicians you think engineers have a role in this discussion absolutely I think the days where politicians can be agnostic to technology are gone III think every tech politician needs to be literate in technology and I often say technology is the new liberal art understanding how technology will change your life I think is important and every human being needs to understand that and maybe we can elect some engineers to office as well on the other side what are the biggest open problems in robotics and you said we're in the early days in some sense what are the problems we would like to solve in robotics I think there are lots of problems right but I would phrase it in the following way if you look at the robots or a building they're still very much tailored towards doing specific tasks and specific settings I think the question of how do you get them to operate in much broader settings with where things can change you know unstructured environments is up in the air so you know think of a self-driving cars today we can build a self-driving car in a parking lot we can do level fire autonomy in a parking lot but can you do level five autonomy in the streets of Napoli in Italy or Mumbai in India no no so in some sense when we think about robotics we have to think about where they're functioning what kind of environment what kind of a task we have no understanding of how to put both those things together so we're in the very early days of applying it to the physical world and I was just enables actually and that's there's levels of difficulty in complexity depending on which area you're applying it to I think so we don't have a systematic way of understanding that you know everybody says just because computer can now beat a human at any board game we suddenly know something about intelligence that's not true a computer board game is very very structured it is the equivalent of working in a Henry Ford factory where things parts come you assemble move on it's a very very very structured setting that's the easiest thing and we know how to do that so you've done a lot of incredible work at the University of Pennsylvania grasp ah you know Dean of engineering at UPenn what advice do you have for a new bright-eyed undergrad interested in robotics or AI or engineering well I think there's really three things one is one is you have to get used to the idea that the world will not be the same in five years or four years whenever you graduate right which is really hard to do so this this thing about predicting the future every one of us needs to be trying to predict the future always not because you'll be any good at it but by thinking about it I think you sharpen your senses and you become smarter so that's number one number two it's a corollary of the first piece which is you really don't know what's going to be important so this idea that I'm going to specialize in something which will allow me to go in a particular direction it may be interesting but it's important also to have this breadth so you have this jumping-off point I think the third thing and this is where I think Penn excels I mean we teach engineering but it's always in the context of the liberal arts it's always in the context of society as engineers we cannot afford to lose sight of that so I think that's important but I think one thing that people underestimate when they do robotics is the important of mathematical foundations important of represent importance of representations not everything can just be solved by looking for Ross packages on the internet or to find a deep neural network that works I think the representation question is key even to machine learning where if you hope ever hope to achieve or get to explainable AI somehow there need to be representations that you can understand so if you want to do robotics you should also do mathematics and you said liberal arts a little literature if you want to build it all I should be reading Dostoyevsky I agree with that very good the v-j thank you so much for talking today was an honor thank you it's just exciting conversation thank you you