Existential Robotics Lab
We use principles from machine learning, control theory, optimization, and computer vision to develop intelligent, reliable, efficient robot systems operating in dynamic environments under uncertainty.
We use principles from machine learning, control theory, optimization, and computer vision to develop intelligent, reliable, efficient robot systems operating in dynamic environments under uncertainty.
Our research lies at the intersection of simultaneous localization and mapping (SLAM), motion planning, and reinforcement learning applied to robotics with emphasis on online execution, safety, and robustness.
We develop environment models that unify geometric, semantic, and temporal reasoning to achieve understanding of space, objects, and dynamics from onboard robot sensing.
We develop algorithms for robot task planning and autonomous robot navigation that enable adaptive and safe behaviors in novel operational conditions.
We develop principles for collaborative inference and decision making in heterogeneous robot teams to achieve distributed intelligence.
We are grateful to the generous sponsors who make our research possible!
