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US Patent 9811074 Optimization of robot control programs in physics-based simulated environment

Patent 9811074 was granted and assigned to Truphysics on November, 2017 by the United States Patent and Trademark Office.

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Contents

Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent

Patent attributes

Patent Applicant
Truphysics
Truphysics
Current Assignee
Truphysics
Truphysics
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
9811074
Patent Inventor Names
Albert Groz10
Fabian Aichele10
Date of Patent
November 7, 2017
Patent Application Number
15614489
Date Filed
June 5, 2017
Patent Citations Received
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1
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US Patent 11919163 Method for validating programmed execution sequences or teaching programs for a robot in a working cell, and a robot and/or robot controller for said method
2
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US Patent 11971709 Learning device, control device, learning method, and recording medium
3
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US Patent 11971712 Self-aware visual-textual co-grounded navigation agent
4
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US Patent 12011827 Robot teaching with scans in and out of robot workspace
5
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6
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US Patent 11701773 Viewpoint invariant visual servoing of robot end effector using recurrent neural network
7
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US Patent 11707838 Artificial intelligence system for efficiently learning robotic control policies
8
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Patent Primary Examiner
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Bhavesh V Amin
Patent abstract

A disclosed system includes a physically plausible virtual runtime environment to simulate a real-life environment for a simulated robot and a test planning and testing component to define a robotic task and generate virtual test cases for the robotic task. The test planning and testing component is further operable to generate virtual test cases for the robotic task, determine a control strategy for executing the virtual test cases, and create the physics-based simulated environment. The system further includes a robot controller operable to execute the virtual test cases in parallel in the physics-based simulated environment, measure a success of the execution, and store training and validation data to a historical database to train a machine learning algorithm. The robot controller may continuously execute the virtual test cases and use the machine learning algorithm to adjust parameters of the control strategy until optimal test cases are determined.

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