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Deployable Decision Making in Embodied Systems (DDM)
Angela Schoellig · Animesh Garg · Somil Bansal · SiQi Zhou · Melissa Greeff · Lukas Brunke

Tue Dec 14 07:00 AM -- 03:00 PM (PST) @
Event URL: https://www.dynsyslab.org/deployable-decision-making-in-embodied-systems/ »

Embodied systems are playing an increasingly important role in our lives. Examples include, but are not limited to, autonomous driving, drone delivery, and service robots. In real-world deployments, the systems are required to safely learn and operate under the various sources of uncertainties. As noted in the “Roadmap for US Robotics (2020)”, safe learning and adaptation is a key aspect of next-generation robotics. Learning is ingrained in all components of the robotics software stack including perception, planning, and control. While the safety and robustness of these components have been identified as critical aspects for real-world deployments, open issues and challenges are often discussed separately in the respective communities. In this workshop, we aim to bring together researchers from machine learning, computer vision, robotics, and control to facilitate interdisciplinary discussions on the topic of deployable decision making in embodied systems. Our workshop will focus on two discussion themes: (i) safe learning and decision making in uncertain and unstructured environments and (ii) efficient transfer learning for deployable embodied systems. To facilitate discussions and solicit participation from a broad audience, we plan to have a set of interactive lecture-style presentations, focused discussion panels, and a poster session with contributed paper presentations. By bringing researchers and industry professionals together in our workshop and having detailed pre- and post-workshop plans, we envision this workshop to be an effort towards a long-term, interdisciplinary exchange on this topic.

Author Information

Angela Schoellig (University of Toronto, Vector Institute)
Animesh Garg (University of Toronto, Nvidia, Vector Institute)

I am a CIFAR AI Chair Assistant Professor of Computer Science at the University of Toronto, a Faculty Member at the Vector Institute, and Sr. Researcher at Nvidia. My current research focuses on machine learning for perception and control in robotics.

Somil Bansal (University of Southern California)

Somil Bansal is an Assistant Professor at the Department of Electrical Engineering of the University of Southern California, Los Angeles. He received a Ph.D. in Electrical Engineering and Computer Sciences (EECS) from the University of California at Berkeley in 2020. Before that, he obtained a B.Tech. in Electrical Engineering from the Indian Institute of Technology, Kanpur, and an M.S. in Electrical Engineering and Computer Sciences from UC Berkeley in 2012 and 2014, respectively. Between August 2020 and August 2021, he spent a year as a Research Scientist at Waymo (formerly known as the Google Self-Driving Car project). He has also collaborated closely with companies like Skydio, Google, Waymo, Boeing, as well as NASA Ames. Somil is broadly interested in developing mathematical tools and algorithms for the control and analysis of autonomous systems, with a focus on bridging learning and control-theoretic approaches for safety-critical autonomous systems. Somil has received several awards, most notably the Eli Jury Award at UC Berkeley for his doctoral research, the outstanding graduate student instructor award at UC Berkeley, and the academic excellence award at IIT Kanpur.

SiQi Zhou (University of Toronto)
Melissa Greeff (University of Toronto)
Lukas Brunke (University of Toronto)

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