Abbeel, " Learning Parameterized Maneuvers for Autonomous Helicopter Flight," in Proceedings 2010 Conference on Robotics and Automation (ICRA), 2010. Abbeel, " Cloth Grasp Point Detection based on Multiple-View Geometric Cues with Application to Robotic Towel Folding," in Proceedings 2010 Conference on Robotics and Automation (ICRA), 2010. Ng, " Autonomous Helicopter Aerobatics through Apprenticeship Learning," International Journal of Robotics Research (IJRR), June 2010. Goldberg, " LQG-MP: Optimized Path Planning for Robots with Motion Uncertainty and Imperfect State Information," in Proceedings of Robotics: Science and Systems (RSS), 2010. on Automation Science and Engineering: Special Issue on Cloud Robotics and Automation, vol. Goldberg, " A Survey of Research on Cloud Robotics and Automation," IEEE Trans. Goldberg, " GP-GPIS-OPT: Grasp Planning Under Shape Uncertainty Using Gaussian Process Implicit Surfaces and Sequential Convex Programming," in IEEE International Conference on Robotics and Automation, 2015.ī. Goldberg, " Learning by Observation for Surgical Subtasks: Multilateral Cutting of 3D Viscoelastic and 2D Orthotropic Tissue Phantoms," 2015. Kumar, " Information-Theoretic Planning with Trajectory Optimization for Dense 3D Mapping," in Robotics: Science and Systems (RSS) Conference, 2015.Ī. Goldberg, " Multi-Arm Bandit Models for 2D Sample Based Grasp Planning with Uncertainty," in IEEE International Conference on Automation Science and Engineering (CASE), 2015.ī. Goldberg, " A Disposable Haptic Palpation Probe for Locating Subcutaneous Blood Vessels in Robot-Assisted Minimally Invasive Surgery," in IEEE International Conference on Automation Science and Engineering (CASE), 2015. Goldberg, " Transition State Clustering: Unsupervised Surgical Trajectory Segmentation For Robot Learning," in International Symposium on Robotics Research (ISRR), 2015. Abbeel, Ed., EECS Department, University of California, Berkeley, Tech. Luo, " Reinforcement Learning for Robotic Assembly with Force Control," P.
His work is frequently featured in the press, including the New York Times, Wall Street Journal, BBC, Rolling Stone, Wired, and Tech Review.Ģ008, Ph.D., Computer Science, Stanford UniversityĢ000, M.S., Electrical Engineering, KU Leuven, Belgium Abbeel has received many awards and honors, including the PECASE, NSF-CAREER, ONR-YIP, Darpa-YFA, TR35. Abbeel has founded three companies: Gradescope (AI to help teachers with grading homework and exams), Covariant (AI for robotic automation of warehouses and factories), and Berkeley Open Arms (low-cost, highly capable 7-dof robot arms), advises many AI and robotics start-ups, and is a frequently sought after speaker worldwide for C-suite sessions on AI future and strategy. Abbeel's Intro to AI class has been taken by over 100K students through edX, and his Deep RL and Deep Unsupervised Learning materials are standard references for AI researchers. His lab also investigates how AI could advance other science and engineering disciplines.
Abbeel’s research strives to build ever more intelligent systems, which has his lab push the frontiers of deep reinforcement learning, deep imitation learning, deep unsupervised learning, transfer learning, meta-learning, and learning to learn, as well as study the influence of AI on society.
Professor Pieter Abbeel is Director of the Berkeley Robot Learning Lab and Co-Director of the Berkeley Artificial Intelligence (BAIR) Lab.
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