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.
Seth Hutchinson,
Northeastern University
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, 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, a cable-driven, graffiti-painting robot, 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 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.
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). He was the Executive Director of the Institute for Robotics and Intelligent Machines at the Georgia Institute of Technology, where he was also Professor and KUKA Chair for Robotics in the School of Interactive Computing (2018-2024), and is professor emeritus in the ECE Department at the University of Illinois in Urbana-Champaign (UIUC), where he was a faculty member (1990-2017), and associate department head (2001-2007).
He received his Ph.D. from Purdue University. Hutchinson served as president of the IEEE Robotics and Automation Society (RAS) during 2020-21. He has previously served as a member of the RAS Administrative Committee, as the editor-in-chief for the “IEEE Transactions on Robotics” and as the founding editor-in-chief of the RAS Conference Editorial Board. He has served on the organizing committees for more than 100 conferences, has more than 300 publications on the topics of robotics and computer vision, and is coauthor of the books “Robot Modeling and Control,” published by Wiley, and “Principles of Robot Motion: Theory, Algorithms, and Implementations,” published by MIT Press. He is a fellow of the IEEE.