As the newest robotics researcher at the University of Toronto Institute for Aerospace Studies (UTIAS), Professor Nick Rhinehart and his team are developing autonomous systems that can operate safely and effectively in complex, unpredictable environments, such as roads and public spaces.
Rhinehart joined the university in 2024 from Waymo, one of the leading autonomous ride-hailing services, where he was a member of the research team working on data-driven simulation and optimal driving.
With the start of a new academic year, we sat down with Rhinehart to learn more about his research, what he’s most excited about and advice for students considering graduate studies or a career in robotics.
Please tell us a little bit about yourself.
I’m from a small town in Pennsylvania. I first became interested in computer science as an undergrad at Swarthmore College, because it felt like the study of problem-solving itself. Over time, that curiosity evolved into a deeper interest in machine learning and robotics: how to build systems that perceive, learn and make decisions in the real world.
Since then, I’ve worked as a researcher in both academic and industry settings, including as a grad student at Carnegie Mellon University, a postdoctoral researcher at UC Berkeley’s AI Research Lab and a senior research scientist at Waymo Research. Now I’m delighted to be at the University of Toronto as an assistant professor, where I lead the LEAF Lab (Learning, Embodied Autonomy, and Forecasting Lab).
Why did you choose U of T?
U of T is world-class when it comes to robotics and AI, and I was excited by its strong interdisciplinary culture. The chance to collaborate with brilliant researchers across engineering and computer science through the University of Toronto Robotics Institute was a big draw. Toronto itself was another draw because it’s vibrant, multicultural and full of the energy of people pursuing many different lifestyles and careers. Plus, I can easily visit my family back in Pennsylvania, and I genuinely enjoy the weather here (usually).
What is the main research goal of your lab?
At the LEAF Lab, we aim to develop broadly useful autonomous systems that efficiently and safely operate in complex environments by advancing the algorithmic foundations of robot learning. Our work combines learning, forecasting and control by teaching systems to anticipate changes in the world, adapt their behaviour and act intelligently.
One of our specific interests is on learned model-based control methods, which learn to forecast the future from examples and can be used to plan robot behaviour. Some of my prior work on model-based methods is in the context of autonomous driving, where explicitly forecasting what might happen next is crucial to being safe and effective; it’d be very difficult to drive if people on the road weren’t at least somewhat predictable.
Even if robots were perfect at forecasting and planning, it’s still a challenge to communicate to robots precisely how we want them to behave. This is another of my group’s interests — reward learning — which is about using information from people to model the essence of what we want robots to do. Broadly, we want robots to combine forecasting and reward learning so they can plan ahead, do what people actually want and improve with experience.
What excites you most about being a faculty member at U of T?
I’m most excited about building a thoughtful and collaborative lab of excellent researchers who want to solve tough problems and become leaders in robotics and machine learning. Being able to do that at a place like U of T, surrounded by talented students and colleagues, is an amazing opportunity.
What advice do you have for students who are interested in pursuing a career or graduate studies in robotics?
Read widely, build things, break them, and figure out why they broke. Get into research early if you can. Familiarize yourself both with recent research trends and foundations from the past. Always be on the lookout for hidden assumptions and insights. Ask a lot of “why?” questions (and try to answer them!) Develop opinions that you can back up with evidence, but keep an open mind. Dream big!
What is something most people might not know about you?
One of my favorite Wikipedia articles is “Timeline of the Far Future.” I like tracing the predictions back to their scientific roots, as well as thinking about which are inevitable, which seem unlikely, and which might mainly depend on choices people make.