Beyond Rigid Worlds: Representing and Interacting with Non-Rigid Objects Workshop

Short Bio: Jeannette Bohg is an Assistant Professor of Computer Science at Stanford University. She was a group leader at the Autonomous Motion Department (AMD) of the MPI for Intelligent Systems until September 2017. Before joining AMD in January 2012, Professor Bohg earned her Ph.D. at the Division of Robotics, Perception and Learning (RPL) at KTH in Stockholm. In her thesis, she proposed novel methods towards multi-modal scene understanding for robotic grasping. She also studied at Chalmers in Gothenburg and at the Technical University in Dresden where she received her Master in Art and Technology and her Diploma in Computer Science, respectively. Her research focuses on perception and learning for autonomous robotic manipulation and grasping. She is specifically interested in developing methods that are goal-directed, real-time and multi-modal such that they can provide meaningful feedback for execution and learning. Professor Bohg has received several Early Career and Best Paper awards, most notably the 2019 IEEE Robotics and Automation Society Early Career Award and the 2020 Robotics: Science and Systems Early Career Award.
Talk Title: Fine Sensorimotor Skills for Using Tools, Operating Devices, Assembling Parts, and Manipulating Non-Rigid Objects

Short Bio: Yunzhu Li is an Assistant Professor of Computer Science at Columbia University where he leads the Robotic Perception, Interaction, and Learning Lab (RoboPIL). Prior to joining Columbia, he was an Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign. He completed a postdoctoral fellowship at the Stanford Vision and Learning Lab, working with Fei-Fei Li and Jiajun Wu. He earned his Ph.D. from the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT, advised by Antonio Torralba and Russ Tedrake, and his bachelor’s degree from Peking University in Beijing. Professor Li's work is distinguished by best paper awards at ICRA and CoRL and research and innovation awards from Amazon and Sony.
Talk Title: Simulating and Manipulating Deformable Objects with Structured World Models

Short Bio: Siyuan Huang is a Research Scientist at the Beijing Institute for General Artificial Intelligence (BIGAI), directing the Embodied Robotics Center and BIGAI-Unitree Joint Lab of Embodied AI and Humanoid Robot. He received his Ph.D. from the Department of Statistics at the University of California, Los Angeles (UCLA). His research aims to build a general agent capable of understanding and interacting with 3D environments like humans. To achieve this, his work made contributions in (i) developing scalable and hierarchical representations for 3D reconstruction and semantic grounding, (ii) modeling and imitating human interactions with 3D world, and (iii) building robots proficient in interactions within the 3D world and with humans.
Talk Title: Learning to Build Interactable Replica of Articulated World

Short Bio: Steve Xie is the founder and CEO of Lightwheel, a leader in simulation and synthetic data infrastructure for embodied AI. Lightwheel partners with organizations such as NVIDIA, Google DeepMind, Figure AI, and Stanford University to accelerate robotics research and deployment. Steve previously led autonomous driving simulation at NVIDIA and Cruise where he built large-scale synthetic data pipelines that set new benchmarks for realism and Sim2Real transfer. He holds a B.S. in Physics from Peking University and a Ph.D. from Columbia University.
Talk Title: How Lightwheel Accelerates Non-Rigid Body Tasks with Simulation