M
Matthew Matl
Researcher at University of California, Berkeley
Publications - 13
Citations - 1342
Matthew Matl is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: GRASP & Robot. The author has an hindex of 10, co-authored 13 publications receiving 835 citations. Previous affiliations of Matthew Matl include Princeton University.
Papers
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Journal ArticleDOI
Learning ambidextrous robot grasping policies
Jeffrey Mahler,Matthew Matl,Vishal Satish,Michael Danielczuk,Bill DeRose,Stephen McKinley,Ken Goldberg +6 more
TL;DR: Dex-Net 4.0 is presented, a substantial extension to previous versions of Dex-Net that learns policies for a given set of grippers by training on synthetic datasets using domain randomization with analytic models of physics and geometry.
Proceedings ArticleDOI
Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning
TL;DR: A compliant suction contact model is proposed that computes the quality of the seal between the suction cup and local target surface and a measure of the ability of thesuction grasp to resist an external gravity wrench.
Proceedings ArticleDOI
OpenPiton: An Open Source Manycore Research Framework
Jonathan Balkind,Michael McKeown,Yaosheng Fu,Tri Nguyen,Yanqi Zhou,Alexey Lavrov,Mohammad Shahrad,Adi Fuchs,Samuel Payne,Xiaohua Liang,Matthew Matl,David Wentzlaff +11 more
TL;DR: OpenPiton is the world's first open source, general-purpose, multithreaded manycore processor and framework that leverages the industry hardened OpenSPARC T1 core with modifications and builds upon it with a scratch-built, scalable uncore creating a flexible, modern manycore design.
Proceedings ArticleDOI
Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data
Michael Danielczuk,Matthew Matl,Saurabh Gupta,Andrew Li,Andrew H. Lee,Jeffrey Mahler,Ken Goldberg +6 more
TL;DR: A method for automated dataset generation is presented and a variant of Mask R-CNN is trained with domain randomization on the generated dataset to perform category-agnostic instance segmentation without any hand-labeled data and the model is deployed in an instance-specific grasping pipeline to demonstrate its usefulness in a robotics application.
Posted Content
Dex-Net 3.0: Computing Robust Robot Suction Grasp Targets in Point Clouds using a New Analytic Model and Deep Learning
TL;DR: In this article, a compliant suction contact model is proposed to compute the quality of the seal between the suction cup and local target surface and a measure of the ability of a suction grasp to resist an external gravity wrench.