Aspects of the disclosed technology provide a computational model that utilizes machine learning for detecting errors during a manual assembly process and determining a sequence of steps to complete the manual assembly process in order to mitigate the detected errors. In some implementations, the disclosed technology evaluates a target object at a step of an assembly process where an error is detected to a nominal object to obtain a comparison. Based on this comparison, a sequence of steps for completion of the assembly process of the target object is obtained. The assembly instructions for creating the target object are adjusted based on this sequence of steps.

Aspects of the disclosed technology provide an Artificial Intelligence Process Control (AIPC) for automatically detecting errors in a manufacturing workflow of an assembly line process, and performing error mitigation through the update of instructions or guidance given to assembly operators at various stations. In some implementations, the disclosed technology utilizes one or more machine-learning models to perform error detection and/or propagate instructions/assembly modifications necessary to rectify detected errors or to improve the product of manufacture.

Aspects of the disclosed technology provide an Artificial Intelligence Process Control (AIPC) for automatically detecting errors in a manufacturing workflow of an assembly line process, and performing error mitigation through the update of instructions or guidance given to assembly operators at various stations. In some implementations, the disclosed technology utilizes one or more machine-learning models to perform error detection and/or propagate instructions/assembly modifications necessary to rectify detected errors or to improve the product of manufacture.

Aspects of the disclosed technology provide a computational model that utilizes machine learning for detecting errors during a manual assembly process and determining a sequence of steps to complete the manual assembly process in order to mitigate the detected errors. In some implementations, the disclosed technology evaluates a target object at a step of an assembly process where an error is detected to a nominal object to obtain a comparison. Based on this comparison, a sequence of steps for completion of the assembly process of the target object is obtained. The assembly instructions for creating the target object are adjusted based on this sequence of steps.

Aspects of the disclosed technology provide a computational model that utilizes machine learning for detecting errors during a manual assembly process and determining a sequence of steps to complete the manual assembly process in order to mitigate the detected errors. In some implementations, the disclosed technology evaluates a target object at a step of an assembly process where an error is detected to a nominal object to obtain a comparison. Based on this comparison, a sequence of steps for completion of the assembly process of the target object is obtained. The assembly instructions for creating the target object are adjusted based on this sequence of steps.

Aspects of the disclosed technology provide an Artificial Intelligence Process Control (AIPC) for automatically detecting errors in a manufacturing workflow of an assembly line process, and performing error mitigation through the update of instructions or guidance given to assembly operators at various stations. In some implementations, the disclosed technology utilizes one or more machine-learning models to perform error detection and/or propagate instructions/assembly modifications necessary to rectify detected errors or to improve the product of manufacture.

Aspects of the disclosed technology provide a computational model that utilizes machine learning for detecting errors during a manual assembly process and determining a sequence of steps to complete the manual assembly process in order to mitigate the detected errors. In some implementations, the disclosed technology evaluates a target object at a step of an assembly process where an error is detected to a nominal object to obtain a comparison. Based on this comparison, a sequence of steps for completion of the assembly process of the target object is obtained. The assembly instructions for creating the target object are adjusted based on this sequence of steps.

Aspects of the disclosed technology provide an Artificial Intelligence Process Control (AIPC) for automatically detecting errors in a manufacturing workflow of an assembly line process, and performing error mitigation through the update of instructions or guidance given to assembly operators at various stations. In some implementations, the disclosed technology utilizes one or more machine-learning models to perform error detection and/or propagate instructions/assembly modifications necessary to rectify detected errors or to improve the product of manufacture.

A system and method for automatic recovery from a failure in a robotic assembly operation using multiple sensor input. Moreover, following detection of an error in an assembly operation from data provided by a first sensor, a recovery plan can be executed, and, if successful, a reattempt at the failed assembly operation can commence. The assembly stage during which the error occurred can be detected by a second sensor that is different from the first sensor. Identification of the assembly stage can assist with determining the recovery plan, as well as identifying the assembly operation that is to be reattempted. The failure can be detected by comparing information obtained from a sensor, such as, for example, a force signature, with corresponding historical information, including historical information obtained at the identified assembly stage for prior workpieces.

Aspects of the disclosed technology provide a computational model that utilizes machine learning for detecting errors during a manual assembly process and determining a sequence of steps to complete the manual assembly process in order to mitigate the detected errors. In some implementations, the disclosed technology evaluates a target object at a step of an assembly process where an error is detected to a nominal object to obtain a comparison. Based on this comparison, a sequence of steps for completion of the assembly process of the target object is obtained. The assembly instructions for creating the target object are adjusted based on this sequence of steps.