Research Projects

Our lab is currently focused on four main projects: Hardware Acceleration for Robotics, Tiny Robots, Accessible STEM Education, and Robust Nonlinear Trajectory Optimization.

Hardware Acceleration for Robotics

The next generation of robotics applications are facing a computational crisis. Facing thermal dissipation limits, CPUs have hit a single-threaded performance wall. At the same time the real-time computational demands for emerging robotics algorithms are only growing. To alleviate this bottleneck, we are developing software libraries that make it easy for other robotics researchers and practitioners to use alternative computing platforms (e.g., GPUs and FPGAs), as well as developing automated tools to enable the efficient design and use of custom robotics accelerator chips. All of our open-source code can be found on our Robot Acceleration GitHub.

Tiny Robots

Many emerging robotics use cases will require small, cheap robots that use embedded devices for computation. When compressing robotics algorithms to fit on these resource constrained computational devices, new challenges and opportunities emerge. We are working to unlock the full potential of these tiny robots by leverging insights from both computer architecture / embedded systems and robotics to custom tailor algorithmic solutions through hardware-software co-design.

Accessible STEM Education

In order to improve the accessibility of STEM education we are undertaking research to understand and improve diversity, equity, inclusion, and belonging in STEM education globally and to design new interdisciplinary, project-based, open-access courses that lower the barrier to entry of cutting edge topics like robotics and embedded machine learning. As a part of this effort we help lead the Tiny Machine Learning Open Education Initiative (TinyMLedu).

Robust Nonlinear Trajectory Optimization

It is often challenging to get nonlinear trajectory optimization to converge reliably without getting stuck in spurious local minima. Better methods for handling constraints in the optimization problem and improved design of objective (cost) functions can help alleviate this problem. Leveraging new methods of data-efficient machine learning and mathematical insights from optimization theory, we are working to design robust trajectory optimization methods that can be used for real-time nonlinear model predictive control.