Abstract: To deploy robots in unstructured, human-centric environments, we must guarantee their ability to safely and reliably complete tasks. In such environments, uncertainty runs rampant and robots invariably need data to refine their autonomy stack. While machine learning can leverage data to obtain components of this stack, e.g., task constraints, dynamics, and perception modules, blindly trusting these potentially unreliable models can compromise safety. Determining how to use these learned components while retaining unified, system-level guarantees on safety and robustness remains an urgent open problem. In this talk, I will present two lines of research towards achieving safe learning-based autonomy. First, I will discuss how to use human task demonstrations to learn hard constraints which must be satisfied to safely complete that task, and how we can guarantee safety by planning with the learned constraints in an uncertainty-aware fashion. Second, I will discuss how to determine where learned perception and dynamics modules can be trusted, and to what extent. We imbue a motion planner with this knowledge to guarantee safe goal reachability when controlling from high-dimensional observations (e.g., images). We demonstrate that these theoretical guarantees translate to empirical success, in simulation and on hardware.
Bio: Glen Chou is a postdoctoral associate at MIT CSAIL, advised by Prof. Russ Tedrake. His research focuses on end-to-end safety and robustness guarantees for learning-enabled robots. Previously, Glen received an MS and PhD in Electrical and Computer Engineering from the University of Michigan in 2022, and dual B.S. degrees in Electrical Engineering and Computer Science and Mechanical Engineering from UC Berkeley in 2017. He is a recipient of the National Defense Science and Engineering Graduate (NDSEG) fellowship and is an R:SS Pioneer.
Location: We will meet only virtually. Please use the following zoom meeting information to join us:
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Meeting ID: 846 7722 4909
Looking forward to seeing you on Friday!
Negar Mehr, Guillermo Colin Navarro, Maulik Bhatt, Shaoxiong Yao, Saurabh Gupta, John M. Hart, and Kris Hauser