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Information Theoretical Regret Bounds for Online Nonlinear Control

Wen Sun, Cornell University


Location: Zoom

Time: 2:55p.m.

Abstract: This work studies the problem of sequential control in an unknown, nonlinear dynamical system, where we model the underlying system dynamics as an unknown function in a known Reproducing Kernel Hilbert Space. This framework yields a general setting that permits discrete and continuous control inputs as well as non-smooth, non-differentiable dynamics. Our main result, the Lower Confidence-based Continuous Control algorithm, enjoys a near-optimal O(\sqrt{T}) regret bound against the optimal controller in episodic settings, where T is the number of episodes. The bound has no explicit dependence on dimension of the system dynamics, which could be infinite, but instead only depends on information theoretic quantities. We empirically show its application to a number of nonlinear control tasks and demonstrate the benefit of exploration for learning model dynamics.

Joint work with Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi.

Challenges & Opportunities in Maritime Robotics

Matthew Bays, Naval Surface Warfare Center, Panama City Division (NSWC PCD)


Location: Zoom

Time: 2:55p.m.

Abstract: Interest in unmanned systems has increased considerably within the maritime domain and specifically the U.S. Navy over the last several decades. However, the littoral (shallow water) and undersea environments offer unique challenges resulting in the need for more autonomous, more reliable, and more modular unmanned systems than is often found in other domains. In this talk, we will provide an overview of the particular challenges the U.S. Navy is attempting to solve or mitigate within the littoral environment and solutions currently in development. These challenges include the unique communication constraints of the underwater domain, the difficult maritime sensing environment, and reliability needs of undersea systems.



Location: Zoom

Time: 2:55p.m.

Abstract: GSGIC (Graduate Students for Gender Inclusion in Computing) + RGSO (Robotics Graduate Student Organization/Robotics Seminar) invite you to join us for an afternoon of origami to build community and build paper decorations for your home/office.

For those who requested materials you should be receiving them in the mail. If you did not previously RSVP feel free to join the event with your own paper anyways!

gradSLAM: Bridging classical and learned methods in SLAM

Krishna Murthy Jatavallabhula, Robotics and Embodied AI Lab (REAL), Mila, Universite de Montreal


Location: Zoom

Time: 2:55p.m.

Abstract: Modern machine learning has ushered in a new air of excitement in the design of intelligent robots. In particular, gradient-based learning architectures (deep neural networks) have enabled significant strides in robot perception, reasoning, and action. With all of these advancements, one might wonder if “classical” techniques for robot perception and state estimation are relevant in this age. I postulate that a flexible blend of “classical” and “learned” methods is the best foot forward for robot intelligence.

“What is the ideal way to combine “classical” techniques with gradient-based learning architectures?” This is the central question that my research strives to answer. I argue that such a blend should be seamless: we must neither disregard domain-specific inductive biases that influence the design of “classical” robots, nor should we compromise on the representational power that learning-based techniques offer. In particular, I tackle the problem of blending gradient-based learning with visual simultaneous localization and mapping (SLAM), and the new possibilities this opens up. My talk will focus on “gradSLAM”: a fully differentiable dense SLAM system that harnesses the power of computational graphs and automatic differentiation, to enable a new perspective of thinking about deep learning for SLAM.

RGSO Research Recaps

Claire Liang, Michael Suguitan, PhD students, Cornell


Location: Zoom

Time: 2:55p.m.


For our first homegrown seminar, some of our grad students will present summaries of their recent work since the Spring and Summer. This is an opportunity for both returning students to catch up with others’ projects and to let new students learn about the breadth of research within robotics at Cornell.