Robots that care with Maja Mataric

Guest:

Maja Mataric is an American computer scientist, roboticist and researcher, a Distinguished Professor of Computer Science, Neuroscience, and Pediatrics at the University of Southern California. She is also the founding Director of the Robotics and Autonomous Systems Center at the USC, and well known for her work in human-robot interaction for socially assistive robotics.

Transcript:

Maja Mataric:Thank you for having me. I'm excited to talk about socially assistive robotics because somehow or other, I ended up founding that field with some collaborators and it's really wonderful to see how it can make a difference in the world. I look forward to talking about it.

I was always really interested in human behavior and what makes us behave the way we behave. And so I like the brain, but I really am more interested in observable behavior.

Cause it's hard to know what's inside here in the brain, but observe behavior: You can observe and study. But then my uncle told me when I was in high school that computers were important. So I listened to him and I studied computers and after that psychology. And then I went to grad school and I did grad school in robotics and MIT. And I ended up being a roboticist, which is about the behavior of machines. And it was really only later that I started thinking about why I do what I do? And I realized that what I really wanted was to help people, which was really about understanding what people needed, understanding human need and human behavior.

And that led me to sort of, you know, starting this new field with a set of collaborators, which is the field of socially assistive robotics.

 Elisa Muñoz: Now that you mentioned socially assistive robotics…I don't know guys, if you knew that professor Mataric actually gave a TedX conference back in the days and, in there she mentioned the following phrase “robots that care” and how a machine can actually take care for someone, could you talk about that professor? 

Maja Mataric: I really wanted the robots to help someone. I didn't want them to just be interesting and you write papers about it, etcetera. So I, the way I figured out how to help people was by looking at, you know, outside of the lab, outside of papers, outside of outside of books, going out there and talking to people who needed help. So who needs help? Well, everybody needs help at some point, but I focused on populations like stroke survivors, children on the autism spectrum and individuals on the autism spectrum.

I looked at the elderly elderly with Alzheimer's. So I talked to a lot of individuals who had challenges to overcome in their lives. And then I talked to caregivers for those individuals and, you know, in business that's called customer discovery, but I didn't know that. And I wasn't trying to start a company back then. I really wanted to understand the problem in order to figure out how robots could help. So I really went at it in what's called the human centered way and it served me very well. I think one of the reasons that a lot of companies fail is that they have startups that are like, oh, I'm going to do this thing. And it's going to be great.

It's like, well, did you try and figure out what people actually really need? Because understanding human need is a hard question, but if you study it enough to understand it and you create something that's trying to help that need, then you're much more likely to succeed. And I think that's why the field of social assistive robotics has grown as well as it's grown, because we really care about what people need.

When I talk to stroke patients, for example, I found out that with stroke patients, it wasn't that people didn't know how to do their exercises. It's that they needed someone or something there to support them many hours a day when they had to fail, like drop something or make it really hard, who is going to support them. And that support was emotional and social.

It wasn't physical. And that's how I understood. Oh, so there is a niche where robotics can help and he could do it right now. It doesn't require us to solve the really hard problem of robots, physically helping people. Because we don't know how to do that safely yet. And so really it boils down to studying the problem, finding out what it really takes. And you can't learn that from papers. You have to go talk to people. So go talk to real people and you will see, you know, a world of problems.

Elisa Muñoz: Definitely. And so at the end of the day, these robots that you guys are building or that you build at some point, they are not only for motivation, but you say also for companionship, right?

Maja Mataric: Yes, exactly. I like to call it a nexus that intersection of what people need is really a combination of it's this social support. We're very so as humans, right? We're very sensitive to social influences. So when someone cares about us, we tend to do what that person says and tend to want to make them happy because they make us happy.  

So the role of the robot is to be that companion that is available to you, that cares about you, that you feel wants to listen to you. You know, we'll give you positive feedback, kind of a friend, right? But unlike human friends who have their own lives to live that are complicated and might have a bad day, it might be grumpy, you know, and so on and so forth, the robot doesn't have those issues, right?

It's never grumpy. It's never having a bad day. As long as the battery is charged and probably the light is on so they can hear and see you. But the idea is that, you know, it feels that niche where it's a buddy, a companion, a friend, and at the same time, it has goals to help you. 

Elisa Muñoz:  And I mean, I think this totally relates to entrepreneurship. Like how do you think robots within the industry  are related to the topic? 

Maja Mataric: Well, I think that's a little bit trickier because basically one, you have to actually care about helping people versus making a profit. So when someone can align helping and making a profit that's ideal. But usually what happens is that there's a conflict. So let's say you want to build a robot that helps children with autism, but the investors think that it would be a bigger market if you made an entertainment robot. And so they really say, well, you know, don't work on autism. Cause that's not all kids let's work on entertainment. Right. And that happens all the time. So that's just one example, but that's what happens, right? So I think when young people with great talent and skill decide they want to do something, they should not, they should not lose sight of their ideals and their goals. Because if they, if they give that up, I'm sorry to say, they sell out.

Then they will not get back. The sad thing is, you know, then you end up doing something entirely different than someone else thinks is gonna make more money. And then it may or may not, if it does, you're still not happy because it wasn't what you wanted to do. And if it doesn't, then you know, it fails anyway, wouldn't it have been better to focus on the thing you actually care about. So unfortunately, when it comes to entrepreneurship, to the extent that it supports funding, you know, it has to come from some place that believes in it. We're in a world right now where like venture capital, for example, investors, they don't care about this enough yet.

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But I think as young people are starting to get into the field and also they're getting some of the wealth, young people are getting older, right. They're starting to care more. So I'm always optimistic. Absolutely. I'm optimistic. But what I would say is I don't want young people to be so naive that they have to hit their head against the wall to learn a lesson. If you think that you're going to go out there and you have a beautiful story about helping kids with autism and that investors are just going to come running to you, that's not going to happen. So you have to, and that doesn't mean you should give it up and go work on a burger flipping robot. No, but you have to think hard. How do you, how do you sell this thing?

 You know, when, when we were creating the field, we had to find a need and we had to work on it. And then it emerged. And similarly for generating a financial interest, you really have to think about, in a sense, you are meeting the needs of the investors. It's always meeting someone's needs. So you have to kind of figure out how you do that without compromising what you're trying to actually develop for the world. It's not easy, but it's always interesting and never boring.

Elisa Muñoz: Do you have any advice for future engineers who are listening to us and that want to start in this industry?

Maja Mataric: Absolutely. I am full of advice because it's been such an interesting journey for me and not over anytime soon, one hopes. So I would, first of all, say it doesn't matter at all, what you have done so far and whether you have the experience in the background. So that's one of the things I see as a barrier. A lot of people will say, oh, well, I haven't studied this or I haven't learned this and how could I possibly do it? Don't let that stop you. It doesn't matter what you've done until now. If there's something that you want to do in, you know, in this field, you can pick up the information, you can pick up the knowledge you can learn.

 So that's the first thing. The second thing I want to say is stop being obsessed with math and thinking that you have to love math and be the world's greatest mathematician in order to do engineering. And robotics is just not true.

I don't love math. I just do enough math to get to the stage of that. And do I do any math right now? Zero. Exactly. Zilch. Right? So the point is, of course, yes, you have to take some classes and you have to gain skills, but that's not the end, all just do it, work hard, get through it so that you can do the thing that you really want to do. And I think it's unfortunate that there is a myth out there. And I want to bust that myth, that there's a myth that you have to adore math to be an engineer and to be in robotics. And that is just not true. It's not true. Practically, there are certain parts of the field that are very mathematical, but they're just one of many things that we do. So anyway, don't, don't believe me, go talk to real people.

If you see someone who's doing the kind of work you're inspired to learn more about what they do, what did they do as opposed to, you know, believing a stereotype. So don't worry about what you've done so far. You can still do it too. You don't have to love math. And then three do what really inspires you. And I mean that in the context of engineering in particular. So I love my job because I love working with young people. And I also love developing technologies that make me feel like I'm helping. So I had to, you know, work to create a field that does that. So I can be happy all the time. So, because I couldn't find one at the time.

So I think that's a really important lesson for everybody, which is, if you're passionate about something and you'll know either if you're passionate about it or you disliked something, well, if you really dislike something, that's also a hint that maybe, you know, you shouldn't be doing. 

And so I just feel like, you know, I, I really had to think about what I liked and it was not about engineering because what you like is not going to be an engineering thing. It's not about, oh, I like machine learning. That's just a tool. It's not a thing you like, what is your purpose in life? Your purpose in life can not be machine learning. That's not the purpose.

So what is your purpose in life? Right. And I realized for me, my personal purpose is I want to feel like I'm developing something that helps people. That's really important to me. That's what makes me happy. And so then I was like, okay, so how can I get to that? And there are many, many ways to get to that, you know, and I found this one through my field, but it's so important to do that. You see young people taking jobs and getting majors and studying things just because they're currently popular or they think they have to, you know, and then you're just basically signing up for not being happy. So find a way to find the things that give you joy to weave them into your profession one way or another, because you're going to be working for a long time in your life.

And it would be good if you could be working on something that you feel good about.

Elisa Muñoz: Thank you so much for taking the time and for being in our podcast. This was an amazing experience  professor, glad to have you here.

Maja Mataric: Thank you so much.

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