M
Mrinal Kalakrishnan
Researcher at Stanford University
Publications - 65
Citations - 6773
Mrinal Kalakrishnan is an academic researcher from Stanford University. The author has contributed to research in topics: Reinforcement learning & Robot. The author has an hindex of 32, co-authored 63 publications receiving 4994 citations. Previous affiliations of Mrinal Kalakrishnan include Google & University of Southern California.
Papers
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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
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
Konstantinos Bousmalis,Alex Irpan,Paul Wohlhart,Yunfei Bai,Matthew Kelcey,Mrinal Kalakrishnan,Laura Downs,Julian Ibarz,Peter Pastor,Kurt Konolige,Sergey Levine,Vincent Vanhoucke +11 more
TL;DR: In this paper, the authors study how randomized simulated environments and domain adaptation methods can be extended to train a grasping system to grasp novel objects from raw monocular RGB images, and they extensively evaluate their approaches with a total of more than 25,000 physical test grasps, including a novel extension of pixel-level domain adaptation that they termed the GraspGAN.
Proceedings ArticleDOI
Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks
Stephen James,Paul Wohlhart,Mrinal Kalakrishnan,Dmitry Kalashnikov,Alex Irpan,Julian Ibarz,Sergey Levine,Raia Hadsell,Konstantinos Bousmalis +8 more
TL;DR: This paper presents Randomized-to-Canonical Adaptation Networks (RCANs), a novel approach to crossing the visual reality gap that uses no real-world data and learns to translate randomized rendered images into their equivalent non-randomized, canonical versions.
Journal ArticleDOI
Learning, planning, and control for quadruped locomotion over challenging terrain
TL;DR: A floating-base inverse dynamics controller that allows for robust, compliant locomotion over unperceived obstacles and the generalization ability of this controller is demonstrated by presenting results from testing performed by an independent external test team on terrain that has never been shown to us.