Computer Science Seminar: Seth Hutchinson

Description

Seth Hutchinson, a professor and chair for robotics in the School of Interactive Computing at the Georgia Institute of Technology, will give a talk titled "Model-Based Methods in Today's Data-Driven Robotics Landscape" for the Department of Computer Science. Hutchinson is also executive director of the Georgia Tech's Institute for Robotics and Intelligent Machines.

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, Seth 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 human-robot interaction scenarios. He will describe some specific difficulties that confront data-driven methods for these problems and how model-based approaches can provide workable solutions. Along the way, he will also discuss how judicious incorporation of data-driven machine learning tools can enhance performance of these methods.