Speakers- Spring 2025

Hanumant Singh – In recent years, researchers in our lab have spent time in the Arctic, Antarctic, at the bottom of the ocean and driving on the streets of Boston. We have also been at the forefront of perception tasks related to SLAM, autonomous driving and flying in cluttered environments. In this talk I highlight some of our field work, how it meshes with algorithmic advances related to 3D structure from motion both from a geometric and machine learning standpoint, and some thoughts on the challenging problems that need to be addressed in the years to come.

Biosketch: Hanumant Singh is a professor with joint appointments in the ECE and MIE departments at Northeastern University. He received his Ph.D. from the MIT WHOI Joint Program in 1995 after which he worked on the Staff at Woods Hole Oceanographic Institution until 2016 when he joined Northeastern. His group has designed and built the Seabed AUV, as well as the Jetyak Autonomous Surface Vehicle, dozens of which are in use for scientific and academic research across the globe. He also has strong interests in the development and use of small Unmanned Aerial Systems (UAS) and Autonomous Cars. He has participated in 65 expeditions in all of the world’s oceans in support of Marine Geology, Marine Biology, Deep Water Archaeology, Chemical Oceanography, Polar Studies, and Coral Reef Ecology. His work has been featured in National Geographic Magazine, the BBC, the New York Times, Wired Magazine, Discover Magazine and other news and television outlets around the world. At Northeastern, he is co-director of the interdisciplinary MS Robotics program and Director of Northeastern Institute for Experiential Robotics. In collaboration with his students his awards include the ICRA Best Student Paper Award, the IEEE Transactions on Robotics King-Sun Fu Memorial Best Paper Award and Best Paper Awards at the Oceans Conference and at AGU. He is a Fellow of the IEEE and has received the IEEE Oceanic Engineering Society Lifetime Achievement Award for his contributions to the design and use of Autonomous Marine Systems.

Kevin Chen – Flapping-wing flight at the insect-scale is incredibly challenging. Insect muscles not only power flight but also absorb in-flight collisional impact, making these tiny flyers simultaneously agile and robust. In contrast, existing aerial robots have not demonstrated these properties. Rigid robots are fragile against collisions, while soft-driven systems suffer limited speed, precision, and controllability. In this talk, I will describe our effort in developing a new class of bio-inspired micro-flyers, ones that are powered by high bandwidth soft actuators and equipped with rigid appendages. We constructed the first heavier-than-air aerial robot powered by soft artificial muscles, which can demonstrate a 1000-second hovering flight. In addition, our robot can recover from in-flight collisions and perform somersaults within 0.10 seconds. This work demonstrates for the first time that soft aerial robots can achieve agile and robust flight capabilities absent in rigid-powered micro-aerial vehicles, thus showing the potential of a new class of hybrid soft-rigid robots. I will also discuss our recent progress in incorporating on board sensors, electronics, and batteries.

Biosketch: Kevin Chen is an associate professor at the Department of Electrical Engineering and Computer Science, MIT, USA. He received his PhD in Engineering Sciences at Harvard University in2017 and his bachelor’s degree in Applied and Engineering Physics from Cornell University in 2012. His research interests include high bandwidth soft actuators, micro robotics, and aerial robotics. He has published in top journals including Nature, Science Robotics, Advanced Materials, PNAS, Nature Communications, IEEE TRO, and Journal of Fluid Mechanics. He is a recipient of the Steven Vogel Young Investigator Award, the NSF CAREER Award, the Office of Naval Research Young Investigator Award, multiple best paper awards (TRO 21, RAL 20, IROS 15), and the Ruth and Joel Spira Teaching Excellence Award.

Michael Everett – This talk will cover some of our group’s recent work on large-scale mapping, motion planning in high-risk environments, and proving that learned controllers on expensive robots might not be as bad of an idea as some people think. Here are the papers that I’ll focus on:arXiv 2410.02961, arXiv 2412.09777, arXiv 2403.03314

Biosketch: I am currently an Assistant Professor at Northeastern University, with a joint appointment in the Department of Electrical & Computer Engineering and the Khoury College of Computer Sciences. I direct the Autonomy & Intelligence Laboratory at Northeastern University. Previously, I was a Visiting Faculty Researcher with Google’s People + AI Research (PAIR) team, developing novel techniques for explainable and trustworthy AI. Before that, I was a Research Scientist and Postdoctoral Associate at the MIT Department of Aeronautics and Astronautics. I received the PhD (2020), SM (2017), and SB (2015) degrees from MIT in Mechanical Engineering.

Sheila Russo – Sheila Russo is an Assistant Professor in the Department of Mechanical Engineering and the Division of Materials Science and Engineering at Boston University (BU). She received her Ph.D. degree at the BioRobotics Institute, Sant’Anna School of Advanced Studies, Italy. She completed her postdoctoral training at the Harvard John A. Paulson School of Engineering and Applied Sciences and the Wyss Institute for Biologically Inspired Engineering. She is the founder and director of the Material Robotics Laboratory at BU. Her research interests include medical and surgical robotics, soft robotics, origami-inspired mechanisms, sensing and actuation, and meso- and micro-scale manufacturing techniques. In 2020 she received the NIH Trailblazer Award for New and Early Stage Investigators.

Robert D. Howe – Robert D. Howe is Abbott and James Lawrence Professor of Engineering at the Harvard Paulson School of Engineering and Applied Sciences, and Founding Co-Director of the Harvard MS/MBA Degree Program. Dr. Howe founded the Harvard BioRobotics Laboratory in 1990, which investigates the roles of sensing and mechanical design and motor control, in both humans and robots. His research interests focus on manipulation, the sense of touch, and human-machine interfaces. Biomedical applications of this work include of robotic and image-guided surgery. Dr. Howe earned a bachelors degree in physics from Reed College, then worked as a design engineer in the electronics industry in Silicon Valley. He received a doctoral degree in mechanical engineering from Stanford University in 1990, and then joined the faculty at Harvard. Dr. Howe is a Fellow of the IEEE and the AIMBE, and has received Best Paper Awards at mechanical engineering, robotics, and surgery conferences. (Lab Website).

Lerrel Pinto – Lerrel Pinto is an Assistant Professor of Computer Science at NYU. His research focuses on machine learning for robots. He received a Ph.D. degree from CMU after which he did a Postdoc at UC Berkeley. His research on robot learning has received the best paper awards at ICRA 2016 and RSS 2023, and finalist at IROS 2019, and CoRL 2022. Lerrel has received the Packard Fellowship and was named a TR35 innovator under 35 for 2023. Several of his works have been featured in popular media such as The Wall Street Journal, TechCrunch, MIT Tech Review, Wired, and BuzzFeed among others. His recent work can be found on ( www.lerrelpinto.com.).

Gregory J. Stein – 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 MIT’s 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.

Dr. Nare Karapetyan – Dr. Nare Karapetyan is a Tenure Track Assistant Scientist at Woods Hole Oceanographic institution (WHOI). Her research focuses on planning and exploration problems with heterogeneous multi-agent systems, with applications in the aquatic domain. She aims to develop more efficient, task-oriented exploration techniques for environmental monitoring and survey operations. She often draws inspiration from human expertise in performing specific tasks and strives to incorporate similar reasoning into the algorithms she develops for surface and underwater robots. Dr. Karapetyan was a postdoctoral associate at Maryland Robotics Center at University of Maryland (UMD). She received her PhD in Computer Science from the University of South Carolina (UofSC), where she worked at the Autonomous Field Robotics Laboratory (AFRL). She was named Breakthrough Graduate Scholar 2022 by the UofSC and was selected as RSS Pioneers 2023. Since 2022, she is serving as an Associate Editor (AE) for the RA-L, IROS and ICRA Editorial Boards.

Robert Katzschmann – Robert Katzschmann is an Assistant Professor of Robotics at ETH Zurich, where he leads the Soft Robotics Lab. He is associated with the Center for Robotics (RobotX), the ETH AI Center, and the Center for Learning Systems, a collaboration between ETH and the Max Planck Institute (MPI). His research primarily focuses on developing musculoskeletal robots that effectively combine soft, rigid, and living materials to perform complex tasks in real-world scenarios. Before he started his tenure at ETH Zurich, he served as the CTO of Dexai Robotics and as a Senior Applied Scientist at Amazon Robotics in the USA. He earned his Ph.D. in Mechanical Engineering from the Massachusetts Institute of Technology (MIT) in 2018 and his Diplom from the Karlsruhe Institute of Technology, Germany, in 2013. His work has been published in leading journals and conferences such as Nature, Nature Communications, Science Advances, and Science Robotics, as well as at prominent robotics conferences including ICRA, IROS, CoRL, ICLR, ICML, and RoboSoft. In addition to his research, he contributes as an editor for the International Journal of Robotics Research (IJRR) and has organized several workshops for the RoboSoft conference. He also works as an associate editor for ICRA, IROS, RoboSoft, and RSS, and he is an editorial board. member of npj Robotics. His research has been featured in premier news outlets such as the New York Times, Wall Street Journal, and BBC.

Dean Molinaro – Dean Molinaro is an applied scientist at the AI Institute where he works on the control of robots during dynamic behaviors. He received his PhD in Robotics from the Georgia Institute of Technology, advised by Aaron Young, focusing on generalized control of lower-limb exoskeletons. His mission is to blend robotics and AI to develop robotic systems capable of augmenting our way of life.

Frederike Dümbgen – Frederike Dümbgen is currently a postdoctoral researcher at the Robotics Institute of University of Toronto, working with Prof. Tim Barfoot. She received her Ph.D. in 2021 from the Laboratory of AudioVisual Communications (LCAV) with Prof. Martin Vetterli and Dr. Adam Scholefield in Computer Science at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. Before that, she obtained her B.Sc. and M.Sc. in Mechanical Engineering from EPFL in 2013 and 2016, respectively, with a minor in Computational Science and Engineering, and Master’s thesis at the Autonomous Systems Lab of ETH Zürich. Her research has ranged from novel localization methods, in particular acoustic, radio-frequency and ultra-wideband localization, to, most recently, global optimization for robotics.

Maani Ghaffari – Maani Ghaffari received the Ph.D. degree from the Centre for Autonomous Systems (CAS), University of Technology Sydney, NSW, Australia, in 2017. He is currently an Assistant Professor at the Department of Naval Architecture and Marine Engineering and the Department of Robotics, University of Michigan, Ann Arbor, MI, USA, where he directs the Computational Autonomy and Robotics Laboratory (CURLY). His work on sparse, globally optimal kinodynamic motion planning on Lie groups received the best paper award finalist title at the 2023 Robotics: Science and Systems conference. He is the recipient of the 2021 Amazon Research Awards. His research interests lie in the theory and applications of robotics and autonomous systems.

Vikash Kumar – Vikash Kumar is an Adjunct Professor at CMU. His research focuses on understanding the fundamentals of embodied (physiological as well as robotic) movements. He finished his Ph.D. at the University of Washington with Prof. Sergey Levine and Prof. Emo Todorov and his M.S. and B.S. from the Indian Institute of Technology (IIT), Kharagpur. He has also spent time as Sr. Research Scientist at FAIR-MetaAI, and Research Scientist at Google-Brain and OpenAI. His research leverages data-driven techniques to develop efficient and generalizable paradigms for embodied intelligence. Applications of his work have led to human-level dexterity on anthropomorphic robotic hands as well as physiological digital twins, low-cost scalable systems capable of contact-rich behaviors, skilled multi-task multi-skill robotic agents, etc. His recent focus is on building foundation models for physiological as well as robotic embodied intelligence, primarily using off-domain data. He is the lead creator of MyoSuite, RoboHive, and a founding member of the MuJoCo physics engine, now widely used in the fields of Robotics and Machine Learning. His works have been recognized with the best Master’s thesis award, best manipulation paper at ICRA’16, best workshop paper ICRA’22, CIFAR AI chair ’20 (declined), and have been widely covered in a wide variety of media outlets such as NewYorkTimes, Reuters, ACM, WIRED, MIT Tech reviews, IEEE Spectrum, etc. (Website).