Research

Increasingly capable algorithms are required to take dynamic mobile robots out of the lab and into the field. Operating under challenging conditions with real-world constraints, these robots face situations that are uncertain, unknown, or unstructured with limitations in perception, real-time demands on compute, and often with complex or stochastic dynamics.

LASER develops methods for adaptive decision-making under uncertainty for these systems, drawing from control theory, reinforcement learning, and stochastic modeling, with an emphasis on a “theory to practice” philosophy including field hardware validation: our work has flown on the International Space Station and, in the coming months, to the Moon. Our overarching goal is to make autonomous robotic operations safer and more efficient when human-in-the-loop operation becomes infeasible, risky, or wasteful.

Active Topic Areas

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Learning-Augmented Planning and Control

Infusing learning-based tools into planning and control to improve efficiency and safety under imperfect knowledge.

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Safe and Robust Planning

Providing or enhancing safety guarantees for uncertain dynamical systems, including when new information is revealed online.

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Information- and Perception-Aware Autonomy

Considering perception, localization, and information gain explicitly in robotic planning.

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Extreme Environment and Space Systems

Autonomy frameworks incorporating the above topic areas for exploration, space, and other extreme environment robotics applications. We are always looking for new, dynamically interesting robotic systems that invite creative algorithmic solutions.