Speakers

Yilun Du

Yilun Du is an Assistant Professor at Harvard in the Kempner Institute and CS, where he runs the Embodied Minds lab. Yilun received his PhD at MIT EECS, advised by Prof. Leslie Kaelbling, Prof. Tomas Lozano-Perez and Prof. Joshua B. Tenenbaum. Previously, Yilun also obtained his bachelor's degree from MIT, was a research fellow at OpenAI, a senior research scientist at Google Deepmind, and got a gold medal at the International Biology Olympiad. His research focuses on generative models, decision making, robot learning, embodied agents, and the applications of such tools to scientific domains.

Yang Gao

Yang Gao is an Assistant Professor at Institute for Interdisciplinary Information Science (IIIS) at Tsinghua University.

Yang is interested in the intersection between computer vision and robotics. Specifically, he wants to explore how to utilize the prior knowledge we have from computer vision to do robot manipulation tasks both more efficiently and effectively. This not only involves understanding how to use the previous visual experiences but also potentially needs re-designing robotic learning algorithms to better handles the visual states.

Previously, Yang obtained his Ph.D. degree from UC Berkeley, advised by Prof. Trevor Darrell and B.E. from the Computer Science Department at Tsinghua University.

Changliu Liu

Changliu Liu is an associate professor in the Robotics Institute, School of Computer Science, Carnegie Mellon University (CMU), where she leads the Intelligent Control Lab. Prior to joining CMU in Jan 2019, Dr. Liu was a postdoc at Stanford Intelligent Systems Laboratory in 2018. She received her Ph.D. in Engineering together with Master degrees in Engineering and Mathematics from University of California at Berkeley (in 2017, 2014, 2015 respectively) where she worked at the Mechanical Systems & Control Lab. She received her bachelor degrees in Engineering and Economics from Tsinghua University (in 2012). Her research interests lie in the design and verification of human-centered intelligent systems with applications to manufacturing and transportation and on various robot embodiments, including robot arms, mobile robots, legged robots, and humanoid robots. Dr. Liu co-founded Instinct Robotics, a robotics company for intelligent manufacturing. Dr. Liu holds senior membership in IEEE, and membership in ASME and AAAI. She published the book “Designing robot behavior in human-robot interactions” with CRC Press in 2019. She is the co-founder of the International Neural Network Verification Competition launched in 2020. Her work has been recognized by NSF Career Award, Amazon Research Award, Ford URP Award, Advanced Robotics for Manufacturing Champion Award, Young Investigator Award at International Symposium of Flexible Automation, IEEE RAS Early Academic Career Award in Robotics and Automation, IFAC Robotics Outstanding Young Researcher Award, and many best/outstanding paper awards. Her research has been covered by IEEE Spectrum, ATI News, Robtiq Blog, etc, and received support from many government agencies and industrial partners, including NSF, NIST, DARPA, ARM Institute, Boeing, Siemens, Lockheed Martin, etc. She demonstrated their human-robot collaboration system for flexible manufacturing to the US President in 2022. She was a member of the academic council for Grit Venture from 2021 to 2023. She served as the associate editor of Mechatronics from 2023 to 2024 and she is currently the associate editor of ASME Journal of Dynamic Systems, Measurement and Control.

Jeannette Bohg

Jeannette Bohg is an Assistant Professor of Computer Science at Stanford University, where she leads research at the intersection of robotics, machine learning, and computer vision with a focus on autonomous robotic manipulation. Her lab aims to uncover the principles of robust sensorimotor coordination and implement them on real robots. Before joining Stanford, she was a group leader in the Autonomous Motion Department (AMD) at the Max Planck Institute for Intelligent Systems from 2012 to 2017. She earned her Ph.D. in the Division of Robotics, Perception, and Learning (RPL) at KTH Royal Institute of Technology in Stockholm, where her thesis introduced novel methods for multi-modal scene understanding in robotic grasping. She also studied at Chalmers University of Technology in Gothenburg and the Technical University of Dresden, receiving an M.Sc. in Art and Technology and a Diploma in Computer Science, respectively. Bohg’s work has been recognized with multiple Early Career and Best Paper awards, including the 2019 IEEE Robotics and Automation Society Early Career Award, the 2020 Robotics: Science and Systems Early Career Award, and the 2023 Sloan Research Fellowship.

Andy Zeng

Andy is a co-founder at Generalist. Before that, he was a research scientist and tech lead at Google DeepMind – and before that, a student at Princeton and UC Berkeley. Andy has worked on training large-scale robotics Foundation models, developing robots that write their own code, and inventing handheld data collection for robots. These days, he is interested in training large neural nets on massive amounts of robot data.

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