An Interview with Manuela Veloso: AI and Autonomous Agents

As part of our AI in Your Community series, I sat down with Manuela Veloso, a renowned expert in artificial intelligence and robotics. Manuela Veloso is the Herbert A. Simon University Professor in the School of Computer Science at Carnegie Mellon University and a former President of the Association for the Advancement of Artificial Intelligence (AAAI). She is also the co-founder and a Past President of the RoboCup Federation. We discussed her work, how she started working with robots, and what advice she has for students about identifying good problems to solve.

Manuela Veloso

Manuela Veloso

Tara Chklovski: Maybe you can start by telling us a little bit about what problem you are excited about and what you’re working on.

Manuela Veloso: I’ve been working in the field of AI for many, many years. In particular I look at AI research as the challenge to integrate what I call perception, which is the ability to interpret data, or assess the world through sensors – and then I combine it with reasoning, which is cognition, or the part of thinking to plan actions that allows an AI system to achieve some kind of goal. And then the third component of this kind of AI story is the action, where these robots, these intelligent agents, do take action in the world by changing the state of the world.

So, I have been working on this problem of integrating perception, cognition, and action. I can also usually think about these as autonomy – so, agents or AI systems exhibit autonomy in their integrations of assessing the world through their perceptions, eventually making decisions through their cognition, and finally actuating in the world. So, within that goal of autonomous agents, I’ve been working a lot with autonomous robots. But I also work with software agents.

TC: And have you created robots to try to accomplish a particular task?

MV: Yeah – basically, these autonomous robots can be thought of as designed to achieve tasks. I’ve also worked on robots that have been part of the robot soccer team. These soccer robots address multiple problems of working on a team – problems of coordination and trying to work as teammates, and in very uncertain environments like in the presence of an opponent. I’ve done robot soccer research since the mid-90s, always trying to make these teams of robots capable of addressing the complications of playing an adversary. Soccer captures all sorts of problems of teamwork at the physical-space level.

So, I’ve done that, and then I’ve also worked on autonomous robots called “service robots” which are capable of performing tasks for humans like driving people in a particular kind of building, taking them to particular locations, picking up and delivering objects. And so that involves navigation, but instead of being on roads it’s inside of buildings. I’ve been working on these mobile indoor service robots – not just service robots that will wash your dishes, but service robots that actually navigate our environments.And then I’ve done many autonomous robots for lots of education purposes, like NAO robots, humanized robots, all sorts and shapes of robots, but basically they are all machines or artificial creatures with a way to perceive the world, like cameras or sensors and then potentially some computing that enables them to think about what they should be doing, which they then actuate with their wheels or their legs or their arms or their gestures.

TC: That’s totally fascinating. And I love the robot soccer! I’m curious, how did you get interested in thinking about making robots play soccer?

MV: I actually was not interested in the soccer problem per se. I was working on planning and execution for robots when one of my students, Peter Stone, was exposed to this idea through a little demonstration of small robots playing soccer. And then we started doing this research on autonomous robots that would be able to play on a team. I don’t have an interest in soccer really, but it gave us a tremendously challenging and exciting infrastructure to think about the problems of autonomy.It’s hard to do autonomy – for instance, autonomous cars are expensive. Plus it’s complicated. And the soccer problem in the lab became a very good framework to actually study the problem of autonomy. And it’s still very important. It’s still very challenging as we add to the research challenges and scale up (by making the teams larger, for example). The soccer was always kind of an excuse to study these very difficult autonomy problems.

TC: It constrains it, right? What advice would you give children as they try to find a problem in their communities that they can solve using technology?

MV: I think that’s a very interesting question. I can’t really advise children to follow what they like to do, since that didn’t apply to me. I never thought about doing robotics. I never loved robots, although I liked math. I liked the rigor of mathematics very much. And then as time went on I became an electrical engineer, and then I started to be very interested in computers.But for children I think the important thing is to think currently. Many of the jobs that are exciting now, be it a medical doctor, an economist, a banker, a teacher, a computer scientist – there is a lot of data involved. So, these computers that we have now, the Fitbits that we wear, the little Alexas we talk to, everything is about accumulating data, accumulating information. Computers now have access to everywhere you go because you are carrying a cell phone that has GPS. So, children need to understand the beautiful concept of trying to make use of all this data. There is a kind of data skill-set that maybe kids could be exposed to. For example, count how many steps it takes to go from here all the way to the kitchen. How many times did you see a specific person this month? Let’s do a little counting here and then they can start understanding probability, they can start understanding distributions, they can start understanding that a lot of the information to be processed is of this data nature. It’s beautiful to try to understand that these children are going to more and more be in a digital world in which a lot of the information is data. This concept of space, this concept of data, this concept of being aware that many things are numbers that become parts of a computer, I think it’s an interesting way of thinking.In addition to reading and writing and math there are these new skills that in some sense are data skills. They talk with Alexa, they can count things, they have this understanding of how these things get in a computer.

TC: One last question before we go: in your research I’m sure you encounter a lot of obstacles. So, what are some strategies that have worked for you that help you to stay motivated and keep pursuing the end goal?

MV: Very good question. I think that the way to think about this given that there are so many difficulties in research – there’s a lot of bottlenecks and very few breakthroughs – it’s the ability to actually collaborate with other people. When you are stuck, there is a component of collaboration, to engage in thinking together about difficult problems rather than facing the difficult problem by one’s self. That always helped me.At the research level I’ve worked very closely with my students; it’s a joint pursuit of difficult problems. And in children, we need to also grow that skill of collaborating with other people. It helps to interact with others, and of course having depth and understanding and persistence and not giving up.Problems that are hard are the most interesting ones, but are the ones that require more dedication, more persistence, more thoroughness. You become aware that when you are stuck in a difficult problem it’s both an opportunity to make a great discovery at the same time as it is frustration of being stuck. So, if life would not have any difficult problems then you would solve all these simple things that then would not have an impact. So, it’s good to have challenges, but you also know that you might have the potential to make a big difference if you persist on solving challenging problems.

TC: Yeah. And I think as a child, if you have not had that much experience being successful then it’s even harder because then it’s scary, right? Like do I have what I need to succeed? And so, you need to have the mentors and the parents all part of it.

MV: Yeah. So, Tara, one final thought then: it’s true that it’s difficult, but on the other hand somehow when you collaborate with other people, older people or just other people, by magic someone helps you break this difficult problem into steps and it becomes, “let’s see if we can do this thing, which is much simpler, not the actual big problem.” But it becomes an ability to let the child make incremental progress towards solving problems of a staggering nature. So, there’s no problem that can’t be solved without this kind of breaking them into pieces.And so, I think that’s what people should think and tell children. Even if it seems very hard maybe there is a much simpler problem that’s on the way to solve the hard problem that they can address now. That’s basically what we do in the research world, and that’s basically what we do in our education. We build an upon the difficult concepts. We don’t just present them. They are broken down. It’s a confidence‑building process, but it’s also about showing you are on the path so solve the bigger problem.That’s a skill that we develop as we become teachers, and as we become researchers and parents. The problems are difficult but it’s good that they are difficult. It’s just that you can make incremental progress as you go.

TC: Totally. All right, Manuela, thank you so, so much.