H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 65 Citations 18,747 328 World Ranking 1122 National Ranking 655

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Operating system
  • Mechanical engineering

Ken Goldberg focuses on Artificial intelligence, Robot, Computer vision, Motion planning and Simulation. His work deals with themes such as Data collection, GRASP and Polygon, which intersect with Artificial intelligence. His Robot research includes elements of Grippers, The Internet and Human–computer interaction.

Ken Goldberg works mostly in the field of Computer vision, limiting it down to topics relating to Robustness and, in certain cases, Tension, Surgical scissors, Point and Model predictive control. His Motion planning research is multidisciplinary, incorporating elements of Markov decision process, Mathematical optimization, Control theory and Configuration space. His Simulation study combines topics from a wide range of disciplines, such as Kinematics, Stereopsis, Convex optimization, Discretization and Finite-state machine.

His most cited work include:

  • Eigentaste: A Constant Time Collaborative Filtering Algorithm (1172 citations)
  • A Survey of Research on Cloud Robotics and Automation (489 citations)
  • Orienting polygonal parts without sensors (388 citations)

What are the main themes of his work throughout his whole career to date?

Ken Goldberg mainly focuses on Artificial intelligence, Robot, Computer vision, Algorithm and GRASP. In most of his Artificial intelligence studies, his work intersects topics such as Machine learning. His research investigates the link between Robot and topics such as Task that cross with problems in Reinforcement learning.

His Computer vision study focuses mostly on Point cloud and Segmentation. His Algorithm research is multidisciplinary, incorporating perspectives in Upper and lower bounds and Sequence. The concepts of his GRASP study are interwoven with issues in Cloud computing and Motion planning.

He most often published in these fields:

  • Artificial intelligence (39.08%)
  • Robot (32.76%)
  • Computer vision (16.67%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (39.08%)
  • Robot (32.76%)
  • Computer vision (16.67%)

In recent papers he was focusing on the following fields of study:

Ken Goldberg mostly deals with Artificial intelligence, Robot, Computer vision, GRASP and Robotics. His research is interdisciplinary, bridging the disciplines of Machine learning and Artificial intelligence. His Robot study incorporates themes from Task, Human–computer interaction, Supervisor, Grippers and Algorithm.

The various areas that Ken Goldberg examines in his Computer vision study include Automation and Calibration. His GRASP research incorporates elements of Heap, Metric, Wrench, Set and Bin. His Robotics study deals with Cloud computing intersecting with Teleoperation.

Between 2017 and 2021, his most popular works were:

  • Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation (195 citations)
  • Learning ambidextrous robot grasping policies (147 citations)
  • RLlib: Abstractions for Distributed Reinforcement Learning (133 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Operating system
  • Mechanical engineering

Artificial intelligence, Robot, Computer vision, Human–computer interaction and Robotics are his primary areas of study. His biological study spans a wide range of topics, including Supervisor, Machine learning and Folding. His studies in Robot integrate themes in fields like GRASP, Task, Task analysis, Grippers and Visualization.

Ken Goldberg interconnects Transfer of learning and Calibration in the investigation of issues within Computer vision. Ken Goldberg combines subjects such as Free space, Bin and Bin picking with his study of Human–computer interaction. His work investigates the relationship between Robotics and topics such as Code that intersect with problems in Variety and Dynamics.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Eigentaste: A Constant Time Collaborative Filtering Algorithm

Ken Goldberg;Theresa Roeder;Dhruv Gupta;Chris Perkins.
Information Retrieval (2001)

1967 Citations

A Survey of Research on Cloud Robotics and Automation

Ben Kehoe;Sachin Patil;Pieter Abbeel;Ken Goldberg.
IEEE Transactions on Automation Science and Engineering (2015)

628 Citations

Orienting polygonal parts without sensors

Kenneth Y. Goldberg.
Algorithmica (1993)

598 Citations

A television event marking system

Gregory H. Kelly;Kenneth Y. Goldberg;John S. Gee;Philip D. Levinson.
(1996)

529 Citations

Desktop teleoperation via the World Wide Web

K. Goldberg;M. Mascha;S. Gentner;N. Rothenberg.
international conference on robotics and automation (1995)

521 Citations

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.
robotics science and systems (2017)

511 Citations

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

Jur Van Den Berg;Pieter Abbeel;Ken Goldberg.
The International Journal of Robotics Research (2011)

404 Citations

Motion planning with sequential convex optimization and convex collision checking

John Schulman;Yan Duan;Jonathan Ho;Alex Lee.
The International Journal of Robotics Research (2014)

356 Citations

Cloud-based robot grasping with the google object recognition engine

Ben Kehoe;Akihiro Matsukawa;Sal Candido;James Kuffner.
international conference on robotics and automation (2013)

307 Citations

Planning for Steerable Bevel-tip Needle Insertion Through 2D Soft Tissue with Obstacles

R. Alterovitz;K. Goldberg;A. Okamura.
international conference on robotics and automation (2005)

274 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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