P
Peter Pastor
Researcher at Stanford University
Publications - 49
Citations - 10495
Peter Pastor is an academic researcher from Stanford University. The author has contributed to research in topics: GRASP & Robot. The author has an hindex of 32, co-authored 47 publications receiving 7729 citations. Previous affiliations of Peter Pastor include Max Planck Society & Honeywell.
Papers
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Journal ArticleDOI
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
TL;DR: The approach achieves effective real-time control, can successfully grasp novel objects, and corrects mistakes by continuous servoing, and illustrates that data from different robots can be combined to learn more reliable and effective grasping.
Journal ArticleDOI
Dynamical movement primitives: Learning attractor models for motor behaviors
TL;DR: Dynamical movement primitives is presented, a line of research for modeling attractor behaviors of autonomous nonlinear dynamical systems with the help of statistical learning techniques, and its properties are evaluated in motor control and robotics.
Proceedings Article
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
Dmitry Kalashnikov,Alex Irpan,Peter Pastor,Julian Ibarz,Alexander Herzog,Eric Jang,Deirdre Quillen,Ethan Holly,Mrinal Kalakrishnan,Vincent Vanhoucke,Sergey Levine +10 more
TL;DR: QT-Opt as mentioned in this paper is a scalable self-supervised vision-based reinforcement learning framework that can leverage over 580k real-world grasp attempts to train a deep neural network Q-function with over 1.2M parameters.
Proceedings ArticleDOI
STOMP: Stochastic trajectory optimization for motion planning
TL;DR: It is experimentally show that the stochastic nature of STOMP allows it to overcome local minima that gradient-based methods like CHOMP can get stuck in.
Proceedings ArticleDOI
Learning and generalization of motor skills by learning from demonstration
TL;DR: A general approach for learning robotic motor skills from human demonstration is provided and how this framework extends to the control of gripper orientation and finger position and the feasibility of this approach is demonstrated.