Hybrid machine learning-based systems and methods for training an object picking robot with real and simulated performance data

For training an object picking robot with real and simulated grasp performance data, grasp locations on an object are assigned based on object physical properties. A simulation experiment for robot grasping is performed using a first set of assigned locations. Based on simulation data from the simul...

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Bibliographic Details
Main Authors Fuhlbrigge, Thomas A, Choi, Sangeun, Huang, Jinmiao, Martinez, Carlos
Format Patent
LanguageEnglish
Published 13.09.2022
Subjects
CALCULATING
CHAMBERS PROVIDED WITH MANIPULATION DEVICES
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
HAND TOOLS
MANIPULATORS
PERFORMING OPERATIONS
PHYSICS
PORTABLE POWER-DRIVEN TOOLS
TRANSPORTING
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Summary:For training an object picking robot with real and simulated grasp performance data, grasp locations on an object are assigned based on object physical properties. A simulation experiment for robot grasping is performed using a first set of assigned locations. Based on simulation data from the simulation, a simulated object grasp quality of the robot is evaluated for each of the assigned locations. A first set of candidate grasp locations on the object is determined based on data representative of simulated grasp quality from the evaluation. Based on sensor data from an actual experiment for the robot grasping using each of the candidate grasp locations, an actual object grasp quality is evaluated for each of the candidate locations.
Bibliography:Application Number: US201916366700