Speakers

Talk 1 — Toward Fast Motion Planning with OMPL 2.0 Library

Lydia Kavraki

Professor, Rice University

Lydia E. Kavraki is the Noah Harding Professor of Computer Science, professor of Bioengineering, professor of Electrical and Computer Engineering, and professor of Mechanical Engineering at Rice University. She is the Director of the Ken Kennedy Institute at Rice. Kavraki received her B.A. in Computer Science from the University of Crete in Greece and her Ph.D. in Computer Science from Stanford University working with Professor Jean-Claude Latombe.
Kavraki's research interests span robotics, AI, and biomedicine. In robotics and AI, she is interested in enabling robots to work with people and in support of people. Her research develops the underlying methodologies for achieving this goal: algorithms for motion planning for high-dimensional systems with kinematic and dynamic constraints, integrated frameworks for reasoning under sensing and control uncertainty, novel methods for learning and for using experiences, and ways to instruct robots at a high level and collaborate with them.

Thai Duong

Postdoctoral Researcher, Rice University

Thai Duong is a postdoctoral researcher at Rice University. He received his Ph.D. in Electrical and Computer Engineering from the University of California, San Diego, where he worked with Professor Nikolay Atanasov at the Existential Robotics Laboratory. His doctoral research focused on learning representations of environments and dynamics for autonomous robot navigation. Previously, he worked as a software engineer at Microsoft. He received his M.S. from Oregon State University and his B.S. from Hanoi University of Science and Technology.
His research focuses on developing efficient, safe, and reliable autonomous robotic systems through the principled integration of robot learning, planning, and control. In particular, he develops models of robot dynamics and environments that preserve domain knowledge and enable efficient motion planning and control. His work spans navigation and exploration with ground and aerial vehicles, agile legged locomotion, and task and motion planning for robotic manipulators. His broader interests include robotics, machine learning, control theory, and optimization.

Talk 2 — Open3D and Beyond: Advancing Geometric Intelligence

Jaesik Park

Associate Professor, Seoul National University

Jaesik Park is an Associate Professor in the Department of Computer Science and Engineering and the Interdisciplinary Program in Artificial Intelligence at Seoul National University. He received his bachelor’s degree from Hanyang University and his M.S. and Ph.D. degrees from KAIST, where he was advised by Professors Yu-Wing Tai and In So Kweon. He later worked as a staff research scientist at Intel’s Intelligent Systems Lab, led by Vladlen Koltun, where he co-created the Open3D library. Before joining Seoul National University, he was a faculty member at POSTECH.
His research interests include text-to-image synthesis, 3D perception, and computer vision. He serves on the program committees of leading international conferences, including CVPR, ECCV, ICCV, ICLR, ICML, NeurIPS, SIGGRAPH, SIGGRAPH Asia, and ICRA.

Talk 3 — Scaling Behavior Cloning with Open Data, Training, and Evaluation

Angjoo Kanazawa

Assistant Professor, University of California, Berkeley

Angjoo Kanazawa is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. She leads the Kanazawa AI Research (KAIR) lab under BAIR. Alongside her academic work, she has served as Chief Technical Advisor for Luma AI and on the advisory board of Wonder Dynamics. She is also an Amazon Scholar in the Frontier AI & Robotics team.
Previously, Kanazawa was a Research Scientist at Google Research, and a BAIR postdoctoral researcher at UC Berkeley advised by Jitendra Malik, Alexei A. Efros and Trevor Darrell. She completed her Ph.D. in Computer Science at the University of Maryland, College Park, under the supervision of David Jacobs. Her research lies at the intersection of computer vision, computer graphics, and machine learning.

Himanshu Gaurav Singh

Ph.D. Student, University of California, Berkeley

Himanshu Gaurav Singh is a Ph.D. student at Berkeley AI Research (BAIR), where he is co-advised by Professors Pieter Abbeel and Jitendra Malik. His research focuses on robot learning, with an emphasis on learning scalable and generalizable robot behaviors from human motion, video, and open data. His recent work includes scalable behavior cloning, sensorimotor control through the imitation of predictive human-motion models, and hand–object interaction pretraining from video.
Previously, Himanshu studied Computer Science at the Indian Institute of Technology Delhi, where he worked on integrating learning and search to improve generalization, as well as reasoning with large language models.

Talk 4 — Principia Robotica: Pinocchio, Simple, and the Making of an Open Robotics Ecosystem.

Justin Carpentier

Robotics Researcher, INRIA and ENS, Paris

Justin Carpentier is a robotics researcher at Inria and head of the Willow research group, a joint research team involving Inria, ENS, and CNRS and based in the Department of Computer Science at ENS. His research focuses on perception, learning, optimization, and control for robotics. He previously served as a postdoctoral research fellow in the Gepetto research group at LAAS-CNRS in Toulouse, France, where he was also a Ph.D. candidate from 2014 to 2017. His doctoral research focused on the computational foundations of anthropomorphic locomotion. He received his degree in Computer Science and Applied Mathematics with highest honors from École Normale Supérieure Paris-Saclay in 2013. In 2014, he was a visiting student in the Optimization in Robotics and Biomechanics group led by Katja Mombaur at Heidelberg University.

Talk 5 — From GPU-Accelerated Kernels to an Accelerated Ecosystem for Planning and Control

Brian Plancher

Assistant Professor, Dartmouth College

Brian Plancher is an Assistant Professor of Computer Science at Dartmouth College, where he leads the Accessible and Accelerated Robotics Lab (A²R Lab). Previously, he was an Assistant Professor of Computer Science at Barnard College and held affiliate positions in the Departments of Computer Science and Electrical Engineering at Columbia University. He is also a co-chair of the Tiny Machine Learning Open Education Initiative (TinyMLedu) and the IEEE-RAS Technical Committee on Model-Based Optimization for Robotics (TCOptRob).
His research focuses on improving robotic systems at all scales by developing, implementing, and evaluating next-generation algorithms and edge computing systems through algorithm–hardware–software co-design. His projects include MPCGPU, GRiD, and TinyMPC. His work lies at the intersection of robotics, computer architecture, embedded systems, numerical optimization, and machine learning.

Talk 6 — iMETRO: NASA's Open Source Facility for Space Robotics Development

Nathan Dunkelberger

Robotics Engineer, NASA Johnson Space Center

Nathan Dunkelberger is a software lead on the iMETRO team at NASA’s Johnson Space Center. In this role, he has led the development of low-level control systems and applications for a diverse range of robots, including manipulators and mobile platforms. He also contributed to the development of the mujoco_ros2_control implementation, which is now maintained by the ros2_control community. He holds a Ph.D. in Mechanical Engineering from Rice University, where his research focused on control theory for rehabilitation robotics.

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