Eric Rosen may have graduated from Brown University in the spring, but his tenure with the Ivy League school is not over. Rosen, who was president of Brown Ignite CS Initiative, the school’s chapter of the Google Ignite CS Initiative, will remain on campus as a postdoctoral student in the Brown Robotics Lab. His research focuses on applying computer science to real-life scenarios and encouraging young people to pursue the field.
PBN: When were you first drawn to the robotics industry and what was it that sparked your interest?
ROSEN: I became interested in robotics during my freshman year at Brown University. I first learned to program in high school and realized early on, because I liked studying how the brain works and the idea of making programs do things on their own and solve problems, I had an interest in artificial intelligence.
When I started studying at Brown, I took a class on robotics taught by my Ph.D. adviser, Stefanie Tellex. During the class, Tellex brought to my attention the idea of intelligent robots, where machines interact with the world and learn from their observations. I found it very cool that we can put sensors on a robot, let it make decisions using external data and have it learn new things based on what happens. Rather than have intelligent programs be confined to a computer in isolation, we can let them explore for themselves, just like humans.
Through her class, I started to get more and more interested in learning how to make robots that could interact with the real world, which snowballed into me studying social robotics today.
PBN: During your four years as an undergraduate student at Brown, in what ways did you find computer science could be applied to daily life?
ROSEN: One of the biggest ways computer science has shaped my daily life is in how I think about and approach problem-solving. For example, when you write a computer program for the first time, it’s never going to work perfectly on the first run. However, that doesn’t mean you failed; it just means you have an opportunity to learn and understand why things happened that way.
It’s very important in computer science to constantly prototype different designs, evaluate their strengths and weakness, think about what you can change with those considerations in mind and then try something new. Learning to not be afraid of failure, and to constantly try new things and keep an open mind about what solutions are available, has helped me a lot. Rather than being afraid of the unknown, I feel computer science has made me excited, and prepared, to learn about what I don’t understand.
PBN: What types of coding and computer science awareness-building were you responsible for as president of Brown’s Google Ignite CS Initiative?
ROSEN: Ignite CS’ main goal is to expose every student in the greater-Providence area to computer science. Since I became president, we have participated in a wide range of outreach opportunities in order to accommodate a variety of student demographics in Providence.
During CS education week in December, we have organized Hour of Code sessions led by Brown students at six schools in Providence. Here, students are introduced to basic programming concepts in one hour. We also have volunteers go to elementary schools and talk about CS in career days or [science, technology, engineering and math] talks to teach students what it means to do CS for a professional career.
We also do long-term initiatives that run throughout the school year, such as after-school clubs at middle and high schools focusing on building iPhone apps and learning programing skills. In these clubs, we also bring in robots from the research lab I am a part of to let students get hands-on experience.
If there is anyone who is interested in having Ignite CS provide mentorship for them, they can learn more about our group and reach out to us from our website [at] brownignitecs.wordpress.com.
PBN: What types of research will you continue as a postdoctoral student working in Brown’s Robotics Lab?
ROSEN: The research lab I’m in deals with social robotics, which questions how humans and robots should collaborate and interact. Right now, the only people who interact and control robots are roboticists, because in order to safely work with robots you need to understand how they completely work, which is typically through programming. However, not everyone knows how to program, so we’d ideally want interfaces that allow anyone to be able to program and control robots, such as using natural language or body gestures.
My main research topic deals with making robots with which anyone can collaborate and interact – no matter how much they know about programming or computer science. I would like to create ways to allow every person to use robots, not just experts in computer science. It’s important to have immersive, intuitive, easy-to-use interfaces with robots so that it’s safe to work with them as tools.
I really like studying this field of research because I like being able to help people unlock their inner potential, and I feel that making human-robot interfaces easier for anyone to use helps let anyone learn how to program, interact and use robots for their own benefit. Making robots that the average person can control and understand will be important if we want to have a future where robots are helping everyone out in all parts of our daily lives.
PBN: What do you wish more people knew about current discoveries and ongoing research into robotics?
ROSEN: Most people’s perception of robots come from movies and books, where robots are often personified and have very human-like aspects. Because of this, a lot of people have misconceptions about the current state of real robots, such as their capabilities. Although many sci-fi movies make it seem like robots can do everything better than humans – in reality, robots are extremely far from being able to do most basic tasks.
In robot research, there is a saying: “What is hard for a human is easy for a robot, and what is easy for a human is hard for a robot.” Things [such as] playing chess and multiplying large numbers are really hard for humans because our brains aren’t built for solving those kinds of problems, but computers are really good at them because it’s logical. On the other hand, humans are really good at balancing themselves, picking up items, understanding language, because it comes very natural to us. These tasks are really hard for robots for the foreseeable future because there’s no easy way to write out the rules.