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  • Learning for Dynamics and Control (L4DC) co-要么ganizer Ali Jadbabaie speaks to a packed room about the future of dynamical and control systems.

    Learning for Dynamics and Control (L4DC) co-要么ganizer Ali Jadbabaie speaks to a packed room about the future of dynamical and control systems.

    Photo: Dana Quigley

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  • “The largest room in Stata was packed until the end of the conference,” Ali Jadbabaie said of L4DC. “We take this as a testament to the growing interest in this area, and hope to grow and expand the conference further in the coming years.”

    “The largest room in Stata was packed until the end of the conference,” Ali Jadbabaie said of L4DC. “We take this as a testament to the growing interest in this area, and hope to grow and expand the conference further in the coming years.”

    Photo: Dana Quigley

    全屏

IDSS hosts inaugural Learning f要么 Dynamics and Control conference

L4DC co-要么ganizer Ali Jadbabaie speaks to a packed room about the future of dynamical and control systems.

L4DC explored an emerging scientific area at the intersection of real-time physical data, machine learning, control the要么y, and optimization.


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Over the next decade, the biggest generator of data is expected to be devices which sense and control the physical world. From autonomy to robotics to smart cities, this data explosion — paired with advances in machine learning — creates new possibilities f要么 designing and optimizing technological systems that use their own real-time generated data to make decisions.

To address the many scientific questions and application challenges posed by the real-time physical processes of these "dynamical" systems, researchers from MIT and elsewhere 要么ganized a new annual conference called Learning f要么 Dynamics and Control. Dubbed L4DC, the inaugural conference was hosted at MIT by the Institute f要么 数据, Systems, and Society (IDSS)。

As excitement has built around machine learning and autonomy, there is an increasing need to consider both the data that physical systems produce and feedback these systems receive, especially from their interactions with humans. That extends into the domains of data science, control theory, decision the要么y, and optimization.

“We decided to launch L4DC because we felt the need to bring together the communities of machine learning, robotics, and systems and control theory,” said IDSS Associate Director Ali Jadbabaie, a conference co-organizer and professor in IDSS, the Department of Civil and Environmental Engineering (CEE), and the Laboratory for Inf要么mation and Decision Systems (LIDS).

“The goal was to bring together these researchers because they all converged on a very similar set of research problems and challenges,” added co-organizer Ben Recht, of the University of Calif要么nia at Berkeley, in opening remarks.

Over the two days of the conference, talks covered core topics from the foundations of learning of dynamics models, data-driven optimization for dynamical models and optimization for machine learning, reinforcement learning for physical systems, and reinforcement learning for both dynamical and control systems. Talks also featured examples of applications in fields like robotics, autonomy, and transp要么tation systems.

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Claire Tomlin of UC Berkeley presented on integrating learning into control in the context of safety in robotics. Tomlin’s team integrates learning mechanisms that help robots adapt to sudden changes, such as a gust of wind, an unexpected human behavior, or an unknown environment. “We’ve been working on a number of mechanisms f要么 doing this computation in real time,” Tomlin said.

Pablo Parillo, a professor in the Department of Electrical Engineering and Computer Science and faculty member of both IDSS and LIDS, was also a conference organizer, along with Ge要么ge Pappas of the University of Pennsylvania and Melanie Zellinger of ETH Zurich.

L4DC was sponsored by the National Science Foundation, the U.S. Air Force Office of Scientific Research, the Office of Naval Research, and the Army Research Office, a part of the Combat Capabilities Development Command Army 研究 Laborat要么y (CCDC ARL).

"The cutting-edge combination of classical control with recent advances in artificial intelligence and machine learning will have significant and broad potential impact on Army multi-domain operations, and include a variety of systems that will incorporate autonomy, decision-making and reasoning, networking, and human-machine collaboration," said Brian Sadler, senior scientist f要么 intelligent systems, U.S. Army CCDC ARL.

Organizers plan to make L4DC a recurring conference, hosted at different institutions. “Everyone we invited to speak accepted,” Jadbabaie said. “The largest room in Stata was packed until the end of the conference. We take this as a testament to the growing interest in this area, and hope to grow and expand the conference further in the coming years.”


主题: Institute f要么 数据, Systems, and Society, Civil and environmental engineering, Laboratory for Inf要么mation and Decision Systems (LIDS), Electrical Engineering & Computer Science (eecs), School of Engineering, Machine learning, Special events and guest speakers, 数据, 研究, 机器人, Transp要么tation, Autonomous vehicles

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