Calendar

Apr
3
Wed
LCSR Seminar: Seth Hutchinson, “Model-Based Methods in Today’s Data-Driven Robotics Landscape @ Hackerman B17
Apr 3 @ 12:00 pm – 1:00 pm

Model-Based Methods in Today’s Data-Driven Robotics Landscape
Seth Hutchinson, Georgia Tech

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, 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 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.

BIO:

Seth Hutchinson is the Executive Director of the Institute for Robotics and Intelligent Machines at the Georgia Institute of Technology, where he is also Professor and KUKA Chair for Robotics in the School of Interactive Computing. Hutchinson received his Ph.D. from Purdue University in 1988, and in 1990 joined the University of Illinois in Urbana-Champaign (UIUC), where he was a Professor of Electrical and Computer Engineering (ECE) until 2017, serving as Associate Department Head of ECE from 2001 to 2007.

Hutchinson served as president of the IEEE Robotics and Automation Society (RAS) 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, “Principles of Robot Motion: Theory, Algorithms, and Implementations,” published by MIT Press, and the forthcoming “Introduction to Robotics and Perception,” to be published by Cambridge University Press. He is a Fellow of the IEEE.

 

Apr
10
Wed
LCSR Seminar: Glen Chou, “Toward End-to-end Reliable Robot Learning for Autonomy and Interaction” @ Hackerman B17
Apr 10 @ 12:00 pm – 1:00 pm

Abstract:

Robots must behave safely and reliably if we are to confidently deploy them in the real world around humans. To complete tasks, robots must manage a complex, interconnected autonomy stack of perception, planning, and control software. While machine learning has unlocked the potential for full-stack end-to-end control in the real world, these methods can be catastrophically unreliable. In contrast, model-based safety-critical control provides rigorous guarantees, but struggles to scale to real systems, where common assumptions, e.g., perfect task specification and perception, break down.

However, we need not choose between real-world utility and safety. By taking an end-to-end approach to safety-critical control that builds and leverages knowledge of where learned components can be trusted, we can build practical yet rigorous algorithms that can make real robots more reliable. I will first discuss how to make task specification easier and safer by learning hard constraints from human task demonstrations, and how we can plan safely with these learned specifications despite uncertainty. Then, given a task specification, I will discuss how we can reliably leverage learned dynamics and perception for planning and control by estimating where these learned models are accurate, enabling probabilistic guarantees for end-to-end vision-based control. Finally, I will provide perspectives on open challenges and future opportunities in assuring algorithms for space autonomy, including robust perception-based hybrid control algorithms for reliable data-driven robotic manipulation and human-robot collaboration.

Bio:

Glen Chou is a postdoctoral associate at MIT CSAIL, advised by Prof. Russ Tedrake. His research focuses on end-to-end safety and reliability guarantees for learning-enabled robots that operate around humans. Previously, Glen received his PhD in Electrical and Computer Engineering from the University of Michigan in 2022, where he was advised by Profs. Dmitry Berenson and Necmiye Ozay. Prior to that, he received 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, the NSF Graduate Research fellowship, and is a Robotics: Science and Systems Pioneer.

Website: https://glenchou.github.io/

Zoom: Meeting ID 955 8366 7779; Passcode 530803
https://wse.zoom.us/j/95583667779

 

Apr
17
Wed
LCSR Seminar: Faculty Candidate, TBD @ Hackerman B17
Apr 17 @ 12:00 pm – 1:00 pm
Apr
24
Wed
LCSR Seminar: Professional Development @ Hackerman B17
Apr 24 @ 12:00 pm – 1:00 pm