公开(公告)号：US20240036534A1

公开(公告)日：2024-02-01

申请号：US18462332

申请日：2023-09-06

Applicant: XEROX CORPORATION

Inventor： Anne Plochowietz ,  Anand Ramakrishnan ,  Warren Jackson ,  Lara S. Crawford ,  Bradley Rupp ,  Sergey Butylkov ,  Jeng Ping Lu ,  Eugene M. Chow

IPC: G05B13/04 ,  G06N7/08 ,  G06N3/08 ,  G05B13/02

CPC classification number: G05B13/048 ,  G05B13/042 ,  G06N7/08 ,  G06N3/08 ,  G05B13/027

Abstract: Control loop latency can be accounted for in predicting positions of micro-objects being moved by using a hybrid model that includes both at least one physics-based model and machine-learning models. The models are combined using gradient boosting, with a model created during at least one of the stages being fitted based on residuals calculated during a previous stage based on comparison to training data. The loss function for each stage is selected based on the model being created. The hybrid model is evaluated with data extrapolated and interpolated from the training data to prevent overfitting and ensure the hybrid model has sufficient predictive ability. By including both physics-based and machine-learning models, the hybrid model can account for both deterministic and stochastic components involved in the movement of the micro-objects, thus increasing the accuracy and throughput of the micro-assembly.

公开(公告)号：US11762348B2

公开(公告)日：2023-09-19

申请号：US17326902

申请日：2021-05-21

Applicant: XEROX CORPORATION

Inventor： Anne Plochowietz ,  Anand Ramakrishnan ,  Warren Jackson ,  Lara S. Crawford ,  Bradley Rupp ,  Sergey Butylkov ,  Jeng Ping Lu ,  Eugene M. Chow

IPC: G05B13/04 ,  G06N7/08 ,  G06N3/08 ,  G05B13/02

CPC classification number: G05B13/048 ,  G05B13/027 ,  G05B13/042 ,  G06N3/08 ,  G06N7/08

Abstract: Control loop latency can be accounted for in predicting positions of micro-objects being moved by using a hybrid model that includes both at least one physics-based model and machine-learning models. The models are combined using gradient boosting, with a model created during at least one of the stages being fitted based on residuals calculated during a previous stage based on comparison to training data. The loss function for each stage is selected based on the model being created. The hybrid model is evaluated with data extrapolated and interpolated from the training data to prevent overfitting and ensure the hybrid model has sufficient predictive ability. By including both physics-based and machine-learning models, the hybrid model can account for both deterministic and stochastic components involved in the movement of the micro-objects, thus increasing the accuracy and throughput of the micro-assembly.