An automated fastener insert installation system for composite panels is provided. A first module receives and secures a composite panel with respect to an origin of a first coordinate system, wherein the composite panel has opposed major surfaces and defines an insert-receiving orifice extending through one of the major surfaces, and is secured such that one of the major surfaces is externally accessible. A second module engages each of a plurality of fastener inserts with an installation aide. Third module determines a configuration of the orifice defined by the composite panel, selects a corresponding one of the fastener inserts engaged with the installation aide, inserts the selected fastener insert into the orifice, and dispenses an adhesive material through the installation aide and into the orifice about selected fastener insert such that the adhesive material secures the selected fastener insert within the orifice. Associated systems are also provided.

Provide are a method, an IIoT system, and a storage medium for assembly quality control. The method includes: obtaining quality inspection information of a plurality of parts to be assembled; determining, based on the quality inspection information and assembly information, an assembly risk value of each of at least one assembly process through an assembly database; generating a first assembly parameter in response to determining that the assembly risk value satisfies a risk condition; obtaining assembly process data of an assembly operation based on a monitoring device; determining assembly quality of a completed process; in response to determining that the assembly quality does not satisfy a quality requirement, determining a second assembly parameter; generating quality warning information based on the assembly quality and the second assembly parameter; and generating, based on the completed process, quality update data for updating the assembly database.

A computer-implemented method of determining actions for controlling a robot, in particular an assembly robot, includes (i) receiving a first and second input, wherein the first input is a sentence describing an action which should be carried out by the robot, wherein the second input is an image of a current state of an environment of the robot, (ii) feeding the first input into a first machine learning model and feeding the second input into a second machine learning model, wherein the first and second machine learning models are configured to determine tokens for their respective inputs, and (iv) feeding the tokens into a third machine learning model, wherein the third machine learning model outputs two outputs, wherein the first output is a switch for incorporating specialized skill networks and the second output are actions.

The control device manages a process of a mobile body manufactured through a plurality of processes. The plurality of steps includes a first process relating to inspection, an assembling process, an adjustment, and a second process. The control device includes: an acquisition unit that acquires information indicating a state of the mobile body; a determination unit that determines whether adjustment is necessary and whether or not the second process is necessary according to a result of the first process using the information; and a management unit that performs or does not perform the second process or does not perform the adjustment according to the determination.

A method for controlling a robotic manipulator according to a task comprises accepting a feedback signal including a sequence of multi-modal observations of a state of execution of the task. The multi-modal observations are processed with a neural network having a self-attention module with a hierarchically conditioned output to produce a skill of the robotic manipulator and an action conditioned on the skill. The neural network is trained in a supervised manner with demonstration data to produce a sequence of skills and a corresponding sequence of actions for the actuators of the robotic manipulator to perform the task. The method further comprises determining one or more control commands for the one or more actuators based on the produced action and submitting the one or more control commands to the one or more actuators causing a change of the state of execution of the task.

Provide are a method, an IIoT system, and a storage medium for assembly quality control. The method includes: obtaining quality inspection information of a plurality of parts to be assembled; determining, based on the quality inspection information and assembly information, an assembly risk value of each of at least one assembly process through an assembly database; generating a first assembly parameter in response to determining that the assembly risk value satisfies a risk condition; obtaining assembly process data of an assembly operation based on a monitoring device; determining assembly quality of a completed process; in response to determining that the assembly quality does not satisfy a quality requirement, determining a second assembly parameter; generating quality warning information based on the assembly quality and the second assembly parameter; and generating, based on the completed process, quality update data for updating the assembly database.

Provide are a method, a system, and a storage medium for assembly operation control. The method includes: obtaining quality inspection information of a plurality of parts to be assembled; determining, based on the quality inspection information and assembly information, an assembly risk value of each of at least one assembly process through an assembly database; generating a first assembly parameter in response to determining that the assembly risk value satisfies a risk condition; obtaining assembly process data of an assembly operation based on a monitoring device; determining assembly quality of a completed process; in response to determining that the assembly quality does not satisfy a quality requirement, determining a second assembly parameter; generating quality warning information based on the assembly quality and the second assembly parameter; and generating, based on the completed process, quality update data for updating the assembly database.