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D-Index & Metrics

Computer Science

D-Index
93
Citations
33519
World Ranking
517
National Ranking
276

Overview

Ken Goldberg is affiliated with the University of California, Berkeley in the United States. Their research spans multiple fields within computer science and engineering, with a significant focus on robotics.

Their main fields of study include:

  • Computer Science
  • Engineering

Within these broader areas, Ken Goldberg has contributed to several subfields, including:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Biomedical Engineering
  • Control and Systems Engineering
  • Surgery

The primary topics of their work reflect a focus on robotics and technology related to human-machine interaction and surgical applications. Key topics include:

  • Robot Manipulation and Learning
  • Robotic Path Planning Algorithms
  • Reinforcement Learning in Robotics
  • Augmented Reality Applications
  • Surgical Simulation and Training
  • Anatomy and Medical Technology
  • Soft Robotics and Applications

They have published extensively across various respected venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • Science Robotics
  • IEEE Transactions on Automation Science and Engineering
  • Nature Machine Intelligence
  • 2022 International Conference on Robotics and Automation (ICRA)

Their recent papers include the following titles with publication year and venues:

  • Mechanical Search on Shelves using a Novel "Bluction" Tool, 2022, 2022 International Conference on Robotics and Automation (ICRA)
  • Augmented dexterity: How robots can enhance human surgical skills, 2024, Science Robotics
  • Automated Pruning and Irrigation of Polyculture Plants, 2024, IEEE Transactions on Automation Science and Engineering
  • AI reflections in 2019, 2020, Nature Machine Intelligence
  • A roadmap for AI in robotics, 2025, Nature Machine Intelligence

Ken Goldberg frequently collaborates with several co-authors, notably:

  • Chung Min Kim
  • Letian Fu
  • Kush Hari
  • Frank C. Park
  • Pieter Abbeel

In addition to journal and conference papers, they have a contribution in book publishing, including a title published by Springer International Publishing:

  • Algorithmic Foundations of Robotics XII, 2020

Best Publications

  • Eigentaste: A Constant Time Collaborative Filtering Algorithm

    Ken Goldberg;Theresa Roeder;Dhruv Gupta;Chris Perkins

  • Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics

    Jeffrey Mahler;Jacky Liang;Sherdil Niyaz;Michael Laskey

  • A Survey of Research on Cloud Robotics and Automation

    Ben Kehoe;Sachin Patil;Pieter Abbeel;Ken Goldberg

  • Orienting polygonal parts without sensors

    Kenneth Y. Goldberg

  • Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation

    Tianhao Zhang;Zoe McCarthy;Owen Jowl;Dennis Lee

  • Learning ambidextrous robot grasping policies

    Jeffrey Mahler;Matthew Matl;Vishal Satish;Michael Danielczuk

  • Combating COVID-19-The role of robotics in managing public health and infectious diseases.

    Guang-Zhong Yang;Bradley J. Nelson;Robin R. Murphy;Howie Choset

  • LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information

    Jur Van Den Berg;Pieter Abbeel;Ken Goldberg

  • Desktop teleoperation via the World Wide Web

    K. Goldberg;M. Mascha;S. Gentner;N. Rothenberg

  • A television event marking system

    Gregory H. Kelly;Kenneth Y. Goldberg;John S. Gee;Philip D. Levinson

  • Respectful cameras: detecting visual markers in real-time to address privacy concerns

    J. Schiff;M. Meingast;D.K. Mulligan;S. Sastry

  • Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards

    Jeffrey Mahler;Florian T. Pokorny;Brian Hou;Melrose Roderick

  • Cloud-based robot grasping with the google object recognition engine

    Ben Kehoe;Akihiro Matsukawa;Sal Candido;James Kuffner

  • Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning

    Jeffrey Mahler;Matthew Matl;Xinyu Liu;Albert Li

  • RLlib: Abstractions for Distributed Reinforcement Learning

    Eric Liang;Richard Liaw;Philipp Moritz;Robert Nishihara

  • Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics

    Jeffrey Mahler;Jacky Liang;Sherdil Niyaz;Michael Laskey

  • The stochastic motion roadmap: A sampling framework for planning with Markov motion uncertainty

    Ron Alterovitz;Thierry Siméon;Kenneth Y. Goldberg

  • RLlib: Abstractions for Distributed Reinforcement Learning

    Eric Liang;Richard Liaw;Robert Nishihara;Philipp Moritz

  • The Robot in the Garden: Telerobotics and Telepistemology in the Age of the Internet

    Ken Goldberg;Hubert Dreyfus;Alvin Goldman;Oliver Grau

  • Superhuman performance of surgical tasks by robots using iterative learning from human-guided demonstrations

    Jur van den Berg;Stephen Miller;Daniel Duckworth;Humphrey Hu

  • ViBE: a compressed video database structured for active browsing and search

    C. Taskiran;Jau-Yuen Chen;A. Albiol;L. Torres

  • Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation

    Tianhao Zhang;Zoe McCarthy;Owen Jow;Dennis Lee

Frequent Co-Authors

Pieter Abbeel
Pieter Abbeel University of California, Berkeley
Sachin Patil
Sachin Patil University of California, Berkeley
Ron Alterovitz
Ron Alterovitz University of North Carolina at Chapel Hill
Animesh Garg
Animesh Garg University of Toronto
Joseph E. Gonzalez
Joseph E. Gonzalez University of California, Berkeley
John Canny
John Canny University of California, Berkeley
Ion Stoica
Ion Stoica University of California, Berkeley
Jur van den Berg
Jur van den Berg University of Utah
Michael J. Franklin
Michael J. Franklin University of Chicago
Allison M. Okamura
Allison M. Okamura Stanford University

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