Відео

CMU Learning and Control Seminar by Yufei Wang Differentiable trajectory optimization as a policy representation for reinforcement and imitation learning Bio: Yufei Wang is a third year Phd student at Robotics Institute, Carnegie Mellon University, co-advised by Prof. Zackory Erickson and Prof. David Held. He received M.S. in Computer Science from Computer Science Department, Carnegie Mellon Un...

CMU Learning and Control Seminar by Ravi Pandya Towards Influence-Aware Safe Human-Robot Interaction Bio: Ravi Pandya is a 4th year PhD student in the Robotics Institute at Carnegie Mellon University advised by Changliu Liu and Andrea Bajcsy. His work focuses on the intersection of safe control, human-robot interaction, and learning for robotics in order to make safe, adaptive, collaborative al...

WoCoCo: Learning Whole-Body Humanoid Control with Sequential Contacts Website: lecar-lab.github.io/wococo/ Paper: arxiv.org/abs/2406.06005 Authors: Chong Zhang*, Wenli Xiao*, Tairan He, Guanya Shi LeCAR (Learning and Control for Agile Robotics) Lab, Carnegie Mellon University

WoCoCo: Learning Whole-Body Humanoid Control with Sequential Contacts Website: lecar-lab.github.io/wococo/ Paper: arxiv.org/abs/2406.06005 Authors: Chong Zhang*, Wenli Xiao*, Tairan He, Guanya Shi LeCAR (Learning and Control for Agile Robotics) Lab, Carnegie Mellon University

OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning Tairan He*, Zhengyi Luo*, Xialin He*, Wenli Xiao, Chong Zhang, Weinan Zhang, Kris Kitani, Changliu Liu, Guanya Shi omni.human2humanoid.com/

New Version (OmniH2O): omni.human2humanoid.com/ Tairan He*, Zhengyi Luo*, Wenli Xiao, Chong Zhang, Kris Kitani, Changliu Liu, Guanya Shi human2humanoid.com/ H2O (Human-to-Humanoid) enables real-time whole-body teleoperation of a full-sized humanoid to perform tasks like picking and placing, walking, kicking, boxing, etc.

CMU Learning and Control Seminar by Gokul Swamy, 02/26/2024 sites.google.com/andrew.cmu.edu/cmu-lcs/home Title: Efficient Reductions for Interactive Learning from Implicit Human Feedback Abstract: Interactive approaches to imitation learning (i.e. inverse reinforcement learning IRL) and learning from human preferences (i.e. RL from Human Feedback RLHF) have become the preferred approaches for c...

International Conference on Robotics and Automation (ICRA), 2024 Authors: Wenli Xiao*, Tairan He*, John Dolan, Guanya Shi Paper: arxiv.org/abs/2310.08602 Website: sites.google.com/view/safe-deep-policy-adaptation

Website: agile-but-safe.github.io/ Paper: arxiv.org/abs/2401.17583 Authors: Tairan He*, Chong Zhang*, Wenli Xiao, Guanqi He, Changliu Liu, Guanya Shi LeCAR (Learning and Control for Agile Robotics) Lab, Carnegie Mellon University