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.
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.
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.
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.
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Eigentaste: A Constant Time Collaborative Filtering Algorithm
Ken Goldberg;Theresa Roeder;Dhruv Gupta;Chris Perkins.
Information Retrieval (2001)
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)
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)
Orienting polygonal parts without sensors
Kenneth Y. Goldberg.
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)
A television event marking system
Gregory H. Kelly;Kenneth Y. Goldberg;John S. Gee;Philip D. Levinson.
Desktop teleoperation via the World Wide Web
K. Goldberg;M. Mascha;S. Gentner;N. Rothenberg.
international conference on robotics and automation (1995)
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)
Respectful cameras: detecting visual markers in real-time to address privacy concerns
J. Schiff;M. Meingast;D.K. Mulligan;S. Sastry.
intelligent robots and systems (2007)
Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation
Tianhao Zhang;Zoe McCarthy;Owen Jowl;Dennis Lee.
international conference on robotics and automation (2018)
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