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

Title: Robot Application Development: A Shifting Paradigm

Abstract:
Interfaces for Robot Application Development (RAD) have proven effective at empowering non-roboticist developers (i.e., robot end users and non-robotics domain experts) to specify tasks for robots to perform. Historically, RAD has adopted development paradigms that have strong ties to traditional computer programming. With recent advancements in robot artificial intelligence, however, there is a pressing need for RAD interfaces to serve instead as communication intermediaries between the developer and the robot. As communication intermediaries, these interfaces should be designed to harness any relevant developer knowledge that is unknown to the robot, while at the same time appropriately leveraging the robot’s intelligent capabilities and communicating this information back to the developer. This talk describes two separate research threads to facilitate developer-robot communication through RAD interfaces. The first thread investigates how interfaces should be designed to appropriately leverage the robot’s knowledge and capabilities. The second thread investigates how interfaces should be designed to elicit relevant tacit knowledge from developers.

Bio:
David Porfirio is an NRC Postdoctoral Research Associate at the U.S. Naval Research Laboratory. His interests lie in designing and evaluating user interfaces that facilitate robot end-user development with the goal of making robot programming more accessible and reliable for non-roboticists. His work has been published in top-tier conferences in both human-robot interaction and human-computer interaction. Prior to his postdoctoral appointment, David received his Ph.D. in 2022 from the University of Wisconsin–Madison (UW–Madison), where he was advised by Drs. Bilge Mutlu and Aws Albarghouthi. During his Ph.D., he was supported by the NSF GRFP, Microsoft Dissertation Grant, and Cisco Wisconsin Distinguished Graduate Fellowship.

Abstract:
This LCSR Professional Development Seminar is focused on essential skills for interviewing for technical jobs in industry and in academia

BIO:

Marin Kobilarov is an Associate Professor at the Johns Hopkins University and a Principal Engineer at Zoox/Amazon. At JHU he leads the Autonomous Systems, Control and Optimization (ASCO) lab which develops algorithms and software for planning, learning, and control of autonomous robotic systems. Their focus is on computational theory at the intersection of planning and learning, and on the system integration and deployment of robots that can operate safely and efficiently in challenging environments.

Whitcomb is Professor of Mechanical Engineering at the Johns Hopkins University School of Engineering, and Adjunct Scientist at the Woods Hole Oceanographic Institution. His research focuses on the navigation, dynamics, and control of robot systems in extreme environments. He is the founding Director (2007-2013) of the JHU Laboratory for Computational Sensing and Robotics and former Chair (2013-2017) of the JHU Department of Mechanical Engineering. He has received numerous best paper awards and teaching awards. He is a Fellow of the IEEE.

Talk Title: Towards robust and autonomous locomotion in cluttered terrain using insect-scale robots

Talk Abstract: Animals such as mice, cockroaches and spiders have the remarkable ability to maneuver through challenging cluttered natural terrain and have been inspiration for adaptable legged robotic systems. Recent biological research further indicates that body reorientation along pathways of minimal energy is a key factor influencing such locomotion. We propose to extend this idea by hypothesizing that body compliance of soft bodied animals and robots might be an alternate yet effective locomotion strategy to squeeze through cluttered obstacles. We present some early results related to the above using Compliant Legged Autonomous Robotic Insect (CLARI), our novel, insect-scale, origami-based quadrupedal robot. While the distributed compliance of such soft-legged robots enables them to explore complex environments, their gait design, control, and motion planning is often challenging due to a large number of unactuated/underactuated degrees of freedom. Towards addressing this issue, we present a geometric motion planning framework for autonomous, closed kinematic chain articulated systems that is computationally effective and has a promising potential for onboard and real-time gait generation.

Biography: Dr. Kaushik Jayaram is presently an Assistant Professor in Robotics at the Paul M Rady Department of Mechanical Engineering at the University of Colorado Boulder. Previously, he was a post-doctoral scholar in Prof. Rob Wood’s Microrobotics lab at Harvard University. He obtained his doctoral degree in Integrative Biology in 2015 from the University of California Berkeley mentored by Prof. Bob Full and undergraduate degree in Mechanical Engineering from the Indian Institute of Technology Bombay in 2009, with interdisciplinary research experiences at the University of Bielefeld, Germany, and Ecole Polytechnique Federale de Lausanne, Switzerland. Dr. Jayaram’s research combines biology and robotics to, uncover the principles of robustness that make animals successful at locomotion in natural environments, and, in turn, inspire the design of the next generation of novel robots for effective real-world operation. His work has been published in a number of prestigious journals and gained significant popular media attention. Besides academic research, Dr. Jayaram’s group is actively involved in several outreach activities that strive toward achieving diversity, equity and inclusivity in STEM.