MEMS Seminar: Seth Hutchinson, "Model-Based Methods in Today’s Data-Driven Robotics Landscape."

Presenter: Seth Hutchinson

Thomas Lord Department of Mechanical Engineering and Materials Science hosts Dr. Seth Hutchinson (Northeastern) to present the MEMS Seminar, "Model-Based Methods in Today's Data-Driven Robotics Landscape."

Abstract: Data-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. 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. In this talk, Dr. Hutchinson 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, a cable-driven graffiti-painting robot, and ensuring safe operation of mobile manipulators in HRI scenarios.

BIO: SETH HUTCHINSON is a professor at Northeastern University, with appointments in the Khoury College of Computer Sciences and the Department of Electrical and Computer Engineering (ECE).

Contact

Spaulding, Amy
919-660-5310
amy.spaulding@duke.edu