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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Format | Patent |
Language | English |
Published |
13.09.2022
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Abstract | 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. |
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AbstractList | 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. |
Author | Fuhlbrigge, Thomas A Choi, Sangeun Martinez, Carlos Huang, Jinmiao |
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Snippet | For training an object picking robot with real and simulated grasp performance data, grasp locations on an object are assigned based on object physical... |
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SubjectTerms | 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 |
Title | Hybrid machine learning-based systems and methods for training an object picking robot with real and simulated performance data |
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