Upcoming Seminars

Dr. S. Farokh Atashzar

 Assistant Professor, Departments of Mechanical and Aerospace Engineering and Electrical and Computer Engineering, New York University

Time: 12:30 pm – 1:30 pm

Date: Friday, October 4th

Location: BC 220

Abstract: In this talk, Atashzar will present his team’s recent work towards achieving human-robot symbiosis. In the first part, he will discuss a new family of data-driven nonlinear control systems based on passivity theory to enable resilient and transparent physical human-robot interaction by accounting for the passivity-based energetic signature of human biomechanics when interacting with robot dynamics. In the second part, he will present the team’s recent efforts in AI for neural engineering to decode complex neural signals for (a) predicting human intention for the control of neurorobots and (b) assessment of human neuromusculoskeletal disability for closed-loop interfacing. This ranges from novel computational models and deep networks to wearable myographic sensors. He will discuss the practical limitations of non-invasive interfaces like high-density electromyography and how AI can help to address them. Finally, he will briefly introduce two new directions of his team: (a) how AI can support the design and fabrication of soft robots and (b) how flexible probabilistic models can enable generalizable human-to-robot skill transfer, overcoming limitations of existing probabilistic learning from demonstration approaches. The overall vision for the talk will be integrated human-robot systems that combine the best of human and machine capabilities for application in medical robotics and neurorobotics.

Bio: S. Farokh Atashzar is currently an Assistant Professor at the School of Engineering, New York University (NYU). He holds joint appointments between the Departments of Mechanical and Aerospace Engineering and Electrical and Computer Engineering, which he joined in August 2019. Previously, he was a postdoctoral scientist in the Department of Bioengineering at Imperial College London and was sponsored by the National Sciences and Engineering Research Council (NSERC) of Canada postdoctoral award. At NYU, he leads the Medical Robotics and Interactive Intelligent Technologies (MERIIT) lab, focusing on the intersection of robotics, control, and AI for broad applications in human-robot interaction and neural engineering. His research aims to achieve human-robot symbiosis at Physical, Cognitive, and Metacognitive levels by developing technical and technological means to bridge human intelligence and physics with machine cognition and kinodynamics. The outcome will be next-generation machines that augment and learn from rather than replace human skills, merging the best of human and machine capabilities. He has published ~90 journal papers, ~60 conference papers, and two book chapters. He received several awards, including the MathWorks Research Award, NSERC PDF award, and IEEE RAS RAL Distinguished AE award. His research is funded by NIH R01, and five NSF grants in addition to multiple industrial grants besides a $2M equipment grant. He currently serves as Associate Editor for IEEE Transactions on Robotics, IEEE Transactions on Haptics, and IEEE Robotics and Automation Letters. Atashzar is also the chair of the IEEE RAS Cluster for human-centered robotics. He is also the General Chair for the IEEE SPS PROGRESS diversity initiative.

Dr. Gregory J. Stein

 Assistant Professor, Department of Computer Science, George Mason University

Time: 12:30 pm – 1:30 pm

Date: Friday, October 25

Location: BC 220

Abstract: The next generation of service and assistive robots will need to operate under uncertainty, expected to complete tasks and perform well despite missing information about the state of the world or the future needs of itself and other agents. Many existing approaches turn to learning to overcome the challenges of planning under uncertainty, yet are often brittle and myopic, limiting their effectiveness. Our work introduces a family of model-based approaches to long-horizon planning under uncertainty that augments (rather than replaces) planning with estimates from learning, allowing for both high-performance and reliability-by-design.

In this talk, I will present a number of recent and ongoing projects that improve long-horizon navigation and task planning in uncertain home-like environments. First, I will discuss our recent developments that improve performance and reliability in unfamiliar environments—environments potentially dissimilar from any seen during training—with a technique we call “offline alt-policy replay,” which enables fast and reliable deployment-time policy selection despite uncertainty. Second, I will discuss “anticipatory planning,” by which our robot anticipates and avoids side effects of its actions on undetermined future tasks it may later be assigned; our approach guides the robot towards behaviors that encourage preparation and organization, improving its performance over lengthy deployments.

Bio: Greg is an Assistant Professor of Computer Science at George Mason University, where he runs the Robotic Anticipatory Intelligence & Learning (RAIL) Group and is the director of the GMU Autonomous Robotics Lab. His research, at the intersection of robotics, planning, and machine learning, is centered around developing representations for planning and learning that allow robots to better understand the impact of their actions, so that they may plan quickly, intelligently, and reliably in a dynamic and uncertain world. Before joining Mason, he received his PhD in 2020 from the MIT Department of Electrical Engineering and Computer Science and previously graduated summa cum laude from Cornell University with a B.S. in Applied and Engineering Physics. His work was a finalist for Best Paper at the 2018 Conference on Robot Learning, at which he was additionally awarded Best Oral Presentation.

Website: https://cs.gmu.edu/~gjstein/