While advancing robots towards full autonomy, it is important to minimize deleterious effects on human and infrastructure. To achieve this, I have been developing data-efficient robotic mapping techniques that capture uncertainty in dynamic environments. By modeling the nonlinear spatiotemporal relationships, these techniques can characterize the uncertainty in long-term and short-term patterns of occupancy, speed, and directions. Since these maps represent uncertainty, they can then be used for robust decision-making.
As part of the SAIL-Toyota Center For AI Research lead by Mykel Kochenderfer, Mac Schwager, and Marco Pavone, I work on safe interactions of autonomous systems.