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DTSTART;TZID=America/New_York:20231117T103000
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SUMMARY:Fall 2023 GRASP on Robotics: Seth Hutchinson\, Georgia Institute of Technology\, “Model-Based Methods in Today’s Data-Driven Robotics Landscape”
DESCRIPTION:This is a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom. \nABSTRACT\nData-driven machine learning methods are making advances in many long-standing problems in robotics\, including grasping\, legged locomotion\, perception\, and more. There are\, however\, robotics applications for which data-driven methods are less effective\, and sometime inappropriate. Data acquisition can be expensive\, time consuming\, or dangerous — to the surrounding workspace\, humans in the workspace\, or the robot itself. In such cases\, generating data via simulation might seem a natural recourse\, but simulation methods come with their own limitations\, particularly when nondeterministic effects are significant\, or when complex dynamics are at play\, requiring heavy computation and exposing the so-called sim2real gap. Another alternative is to rely on a set of demonstrations\, limiting the amount of required data by careful curation of the training examples; however\, these methods fail when confronted with problems that were not represented in the training examples (so-called out-of-distribution problems)\, and this precludes the possibility of providing provable performance guarantees. \nIn this talk\, I will describe recent work on robotics problems that do not readily admit data-driven solutions\, including flapping flight by a bat-like robot\, vision-based control of soft continuum robots\, acrobatic maneuvering by quadruped robots\, a cable-driven graffiti-painting robot\, bipedal locomotion over granular media\, and ensuring safe operation of mobile manipulators in HRI scenarios. I will describe some specific difficulties that confront data-driven methods for these problems\, and describe how model-based approaches can provide workable solutions. Along the way\, I will also discuss how judicious incorporation of data-driven machine learning tools can enhance performance of these methods.
URL:https://seasevents.nmsdev7.com/event/fall-2023-grasp-on-robotics-seth-hutchinson-georgia-institute-of-technology-model-based-methods-in-todays-data-driven-robotics-landscape/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
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