Decision Making and Learning for Robotics

The Robotic Decision Making Laboratory develops planning, coordination, and learning techniques for systems of mobile robotic sensors. At the core of robotics is the optimization of physical plans for tasks such as grasping and navigation. In contrast, a pervasive notion in engineering and computer science is the idea of information optimization. In our research, we seek to unify information optimization and physical motion planning to bridge the gap between the near-optimal performance possible in the digital world and the limited performance currently possible in the physical world.

Our goal is to develop robotic decision making techniques grounded in principled theoretical analysis capable of operating in changing and unstructured environments with imperfect information. We employ formal methods from machine learning, combinatorial optimization, approximation algorithms, and information theory to analyze the hardness of problems and derive techniques with guarantees on performance. Such techniques must be scalable, distributed, and adaptive for operation in the physical world.

For an updated list of publications, please check here:
RDML publications page

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Marine Autonomy Center