Dr. Matei Ciocarlie

Associate Professor, Department of Mechanical Engineering, Columbia University

Time: 11:00 am – 12:00 pm

Date: Friday, April 26

Location: PA 466

Abstract: At long last, robot hands are becoming truly dexterous. It took advances in sensor design, mechanisms, and computational motor learning all working together, but we’re finally starting to see true dexterity, in our lab as well as others. This talk will focus on the path our lab took to get here, and questions for the future. From a mechanism design perspective, I will present our work on optimizing an underactuated hand transmission mechanism jointly the grasping policy that uses it, an approach we refer to as “Hardware as Policy”. From a sensing perspective, I will present our optics-based tactile finger, providing accurate touch information over a multi-curved three-dimensional surface with no blind spots. From a motor learning perspective, I will talk about learning tactile-based policies for dexterous in-hand manipulation and object recognition. Finally, we can discuss implications for the future: how do we consolidate these gains by making dexterity more robust, versatile, and general, and what new applications can it enable?

Bio: Matei Ciocarlie is an Associate Professor in the Mechanical Engineering Department at Columbia University, with affiliated appointments in Computer Science and the Data Science Institute. His work focuses on robot motor control, mechanism and sensor design, planning and learning, all aiming to demonstrate complex motor skills such as dexterous manipulation. Matei completed his Ph.D. at Columbia University in New York; before joining the faculty at Columbia, Matei was a Research Scientist and then Group Manager at Willow Garage, Inc., and then a Senior Research Scientist at Google, Inc. In these positions, Matei contributed to the development of the open-source Robot Operating System (ROS), and led research projects in areas such as hand design, manipulation under uncertainty, and assistive robotics. In recognition of his work, Matei was awarded the Early Career Award by the IEEE Robotics and Automation Society, a Young Investigator Award by the Office of Naval Research, a CAREER Award by the National Science Foundation, and a Sloan Research Fellowship by the Alfred P. Sloan Foundation.

Dr. Axel Krieger

Associate Professor, Department of Mechanical Engineering, Johns Hopkins University

Time: 12:00 pm – 1:00 pm

Date: Friday, April 19

Location: BC 115

Abstract: Robotic assisted surgery (RAS) systems incorporate highly dexterous tools, hand tremor filtering, and motion scaling to enable a minimally invasive surgical approach, reducing collateral damage and patient recovery times. However, current state-of-the-art telerobotic surgery requires a surgeon operating every motion of the robot, resulting in long procedure times and inconsistent results. The advantages of autonomous robotic functionality have been demonstrated in applications outside of medicine, such as manufacturing and aviation. A limited form of autonomous RAS with pre-planned functionality was introduced in orthopedic procedures, radiotherapy, and cochlear implants. Efforts in automating soft tissue surgeries have been limited so far to elemental tasks such as knot tying, needle insertion, and executing predefined motions. The fundamental problems in soft tissue surgery include unpredictable shape changes, tissue deformations, and perception challenges.

My research goal is to transform current manual and teleoperated robotic soft tissue surgery to autonomous robotic surgery, improving patient outcomes by reducing the reliance on the operating surgeon, eliminating human errors, and increasing precision and speed. This presentation will introduce our Intelligent Medical Robotic Systems and Equipment (IMERSE) lab and discuss our novel strategies to overcome the challenges encountered in soft tissue autonomous surgery.  Presentation topics will include a robotic system for supervised autonomous laparoscopic anastomosis and robotic trauma assessment and care. 

Bio: Axel Krieger, PhD, joined the Johns Hopkins University in the Department of Mechanical Engineering in July 2020. He is leading a team of students, scientists, and engineers in the research and development of robotic systems for surgery and interventions. Projects include the development of a surgical robot called smart tissue autonomous robot (STAR) and the use of 3D printing for surgical planning and patient specific implants. Professor Krieger is an inventor of over twenty patents and patent applications. Licensees of his patents include medical device start-ups Activ Surgical and PeriCor as well as industry leaders such as Siemens, Philips, and Intuitive Surgical. Before joining the Johns Hopkins University, Professor Axel Krieger was Assistant Professor in Mechanical Engineering at the University of Maryland and Assistant Research Professor and Program Lead for Smart Tools at the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children’s National. He has several years of experience in private industry at Sentinelle Medical Inc and Hologic Inc. His role within these organizations was Product Leader developing devices and software systems from concept to FDA

The Sensory Needs of Robot Learners

Dr. Dinesh Jayaraman

Assistant Professor, Grasp Lab, University of Pennsylvania

Time: 11:30 am – 12:30 pm

Date: Friday, April 12

Location: BC 220

Abstract:General-purpose robots of the future will need vision and learning, but such vision-based robot learning today is inflexible and inefficient: it needs robot-and-task-specific training experiences, expert-engineered task specifications, and large computational resources. This talk will cover algorithms that dynamically select task-relevant information during sensing, representation, decision making, and learning, enabling flexibilities in pre-training controller modules, layperson-friendly task specification, and efficient resource allocation. I will speak about pre-trained object-centric visual representations that track task-directed progress, task-relevant world model learning for model-based RL, and how robot learners can benefit from additional sensory inputs at training time.

Bio: Dinesh Jayaraman is an assistant professor at the University of Pennsylvania’s CIS department and GRASP lab. He leads the Perception, Action, and Learning  (Penn PAL) research group, which works at the intersections of computer vision, robotics, and machine learning. Dinesh’s research has received an Best Paper Award at CORL ’22, a Best Paper Runner-Up Award at ICRA ’18, a Best Application Paper Award at ACCV ‘16, an Amazon Research Award ’21, the NSF CAREER award ’23, and been featured on the cover page of Science Robotics and in several press outlets. His webpage is at: https://www.seas.upenn.edu/~dineshj/

What makes learning to control easy or hard?

Dr. Nikolai Matni

Assistant Professor, Electrical and Systems Engineering, University of Pennsylvania

Time: 12:00 pm – 1:00 pm

Date: Friday, April 5

Location: BC 115

Abstract: Designing autonomous systems that are simultaneously high-performing, adaptive, and provably safe remains an open problem. In this talk, we will argue that in order to meet this goal, new theoretical and algorithmic tools are needed that blend the stability, robustness, and safety guarantees of robust control with the flexibility, adaptability, and performance of machine and reinforcement learning. We will highlight our progress towards developing such a theoretical foundation of robust learning for safe control in the context of two case studies: (i) characterizing fundamental limits of learning-enabled control, and (ii) developing novel robust imitation learning algorithms with finite sample-complexity guarantees. In both cases, we will emphasize the interplay between robust learning, robust control, and robust stability and their consequences on the sample-complexity and generalizability of the resulting learning-based control algorithms.

Bio: Nikolai Matni is an Assistant Professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania, where he is also a member of the Department of Computer and Information Sciences (by courtesy), the GRASP Lab, the PRECISE Center, and the Applied Mathematics and Computational Science graduate group. He has held positions as a Visiting Faculty Researcher at Google Brain Robotics, NYC, as a postdoctoral scholar in EECS at UC Berkeley, and as a postdoctoral scholar in the Computing and Mathematical Sciences at Caltech. He received his Ph.D. in Control and Dynamical Systems from Caltech in June 2016. He also holds a B.A.Sc. and M.A.Sc. in Electrical Engineering from the University of British Columbia, Vancouver, Canada. His research interests broadly encompass the use of learning, optimization, and control in the design and analysis of autonomous systems. Nikolai is a recipient of the AFOSR YIP (2024), NSF CAREER Award (2021), a Google Research Scholar Award (2021), the 2021 IEEE CSS George S. Axelby Award, and the 2013 IEEE CDC Best Student Paper Award. He is also a co-author on papers that have won the 2022 IEEE CDC Best Student Paper Award and the 2017 IEEE ACC Best Student Paper Award.

Dr. Jaime Fernández Fisac

Assistant Professor, Department of Electrical and Computer Engineering, Princeton University

Time: 11 am (coffee and cookies at 10:30 am in PA 367)

Date: Friday, December 8

Location: PA 466

Abstract: Autonomous robotic systems are promising to revolutionize our homes, cities and roads—but will we trust them with our lives? This talk will take stock of today’s safety-critical robot autonomy, highlight recent advances, and offer some reasons for optimism. We will first see that a broad family of safety schemes from the last decade can be understood under the common lens of a universal safety filter theorem, lighting the way for the systematic design of next-generation safety mechanisms. We will explore reinforcement learning (RL) as a general tool to synthesize global safety filters for previously intractable robotics domains, such as walking or driving through abrupt terrain, by departing from the traditional notion of accruing rewards in favor of a safety-specific Bellman equation. We will show that this safety-RL formulation naturally allows learning from near misses and boosting robustness through adversarial gameplay. Finally, we will turn our attention to dense urban driving, where safety hinges on the autonomous vehicle’s rapidly unfolding interactions with other road users. We will examine how robots can bolster safety by leaning on their future ability to seek out missing key information about other agents’ intent or even their location. We will conclude with an outlook on future autonomous systems and the role of transparent, real-time safety proofs in generating public trust.

Bio: Jaime Fernández Fisac is an Assistant Professor of Electrical and Computer Engineering at Princeton University, where he directs the Safe Robotics Laboratory and co-directs the Princeton AI4ALL outreach summer program. His research combines control theory, artificial intelligence, cognitive science, and game theory with the goal of enabling robots to operate safely in human-populated spaces in a way that is well understood, and thereby trusted, by their users and the public at large. Prior to joining the Princeton faculty, he was a Research Scientist at Waymo (formerly Google’s Self-Driving Car project) from 2019 to 2020, working on autonomous vehicle safety and interaction. He received an Engineering Diploma from the Universidad Politécnica de Madrid, Spain in 2012, a M.Sc. in Aeronautics from Cranfield University, U.K. in 2013, and a Ph.D. in Electrical Engineering and Computer Sciences from the University of California, Berkeley in 2019. He is a recipient of the La Caixa Foundation Fellowship, the Leon O. Chua Award, the Google Research Scholar Award, and the Sony Focused Research Award.

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