This computer scientist is making virtual reality safer

Niall Williams creates algorithms that lowers the odds of motion sickness and bumping into obstacles while using virtual reality headsets.

This computer scientist is making virtual reality safer

Virtual reality (VR) headsets aren’t without their risks. Users can bump into walls, furniture or even other people. Niall Williams is looking to fix that. This computer scientist makes programs that keep people safer while using VR. He works at the University of Maryland in College Park. 

Williams works with redirected walking. This technique nudges users along a path in the real world by subtly changing their virtual display. Developers can tweak how VR programs represent traveling distance, says Williams. For example, algorithms can make two feet of walking in the real world cover more virtual ground. This lets users walk naturally while exploring large digital spaces. 

Fast or large adjustments can leave users disoriented and queasy, though. Redirected walking also works best in areas with fewer obstacles. To help, Williams designed a program that calculates a safer path for users. It avoids both physical and virtual obstacles. “We kind of play tricks on people to get them to walk around safely,” says Williams. 

His algorithms stopped more collisions than other redirected walking programs. By using slower changes, they also lower the odds of motion sickness. Williams is exploring other ways to use natural walking in virtual spaces, he says. In this interview, Williams shares his experiences and advice with Science News Explores. (This interview has been edited for content and readability.) 

What inspired you to pursue your career? 

I wanted to study biology, but I wasn’t good at chemistry. I liked programming and got some experience in high school. So I decided to do a computer science degree. After a few years, I learned about computer graphics. It’s a combination of all of my interests. I also really enjoy video games, animations and art.  

I’m doing this research because working on new problems is fun for me. I like learning about how the human visual system works, and why images evoke certain responses from people. When you see a cartoon person, it’s clearly not a realistic image. But you can still tell that it’s a person in some way, even though the proportions are totally incorrect.  

How did you get to where you are today? 

There are a lot of PhD students in my lab that work on different things. In our lab meetings, the students studying robotics would discuss problems they were working on. I saw this interesting intersection between robotic navigation and VR locomotion. Robot navigation is getting from one point in the environment to another point without getting stuck. Sort of like how your Roomba knows where to go in the room to figure out where it needs to clean. That has a lot of similarities with locomotion in virtual reality. 

A big problem in VR locomotion is that you’re seeing a virtual environment through the headset, but you’re physically located in a different environment. If you want to reach some destination in the virtual world, your path to that destination is likely blocked by some physical objects.  

I realized that I could probably sort of combine the two fields after talking with my lab mates. I could apply techniques for robot navigation to this VR locomotion problem. This might help people avoid objects when they’re in VR. It worked out, so I continued on that path. 

What would you say is your biggest success? 

Probably my first published paper that goes toward my dissertation. I had the idea of applying motion-planning techniques from robot navigation to this VR navigation problem. But nobody had done it before, and it was [during] the pandemic. I was stuck in my house and had to figure it out, largely on my own. During the first two months of that summer, it wasn’t working out. I met with my PhD advisors to discuss the technical details and then took a step back.  

I came up with some algorithms that led to better performance in certain situations. I implemented the research idea, and it worked. We then turned that into a paper that got published. The paper was very well received in my community of scientists. 

Two computer generated images show calculated VR pathways. Each is in a room with an office chair and desk. An orange dotted line shows the path. Two photos accompany each image. One photo shows a user wearing a VR headset standing in a room with wood floors. A beige dotted line curves in front of them. In the other photo, a user wearing a VR headset stands in front of assorted boxes. A beige dotted line curves around the pile of boxes.
Niall Williams tested his algorithm in different physical and virtual scenarios. In one test, the virtual environment was larger than the physical space available. The program guided the user along a curved route in the physical world to compensate (left). In another test, the program had to navigate a straight virtual path while avoiding real-life objects placed in front of the user (right). N. Williams

What was one of your biggest challenges and how did you get past that? 

I did an internship at the company Meta (the parent company of Facebook and Instagram). It was more focused on researching human perception in virtual reality, which I don’t have formal training in. Instead of working with computer scientists, I had to learn how to work with people who study human perception, such as psychologists. Figuring out how to bridge that gap and learn how to do science in the way they do was a challenge for sure.  

How do you get your best ideas? 

My best ideas come from talking to other people and reading papers from different scientific disciplines. This world of extended reality is a very interdisciplinary field. Computer science is one component of it. We develop these systems and devices that you can interface with to explore a virtual world. But it comes with a lot of other questions, especially about human perception.  

Bridging the gap between two communities can also be where the best ideas come from. I believe that a lot of interesting research comes from learning about other kinds of science and seeing how those might be applied to your discipline. As a computer scientist, I may try looking at my problems from the perspective of a different type of scientist, like a human vision scientist. If you’re facing challenges, you’re probably on the right track.  

What piece of advice do you wish you’d been given when you were younger? 

I wish someone told me earlier on that a PhD can be fun. Becoming a scientist should be fun, and it often is. Sure, it’ll be difficult and you’ll have to work hard, but you get paid to study whatever you think is interesting. Your only real responsibility is to think deeply about that problem or topic and try to contribute some new piece of knowledge. It’s a unique experience that is not the same as just doing more school. It’s very independent. You get to think for yourself and maybe get to know yourself better.  

I also wish someone told me early on that being a scientist is a real career path. Scientists are not just fictional characters in movies. We’re real. 

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