Implementations utilize deep reinforcement learning to train a policy neural network that parameterizes a policy for determining a robotic action based on a current state. Some of those implementations collect experience data from multiple robots that operate simultaneously. Each robot generates instances of experience data during iterative performance of episodes that are each explorations of performing a task, and that are each guided based on the policy network and the current policy parameters for the policy network during the episode. The collected experience data is generated during the episodes and is used to train the policy network by iteratively updating policy parameters of the policy network based on a batch of collected experience data. Further, prior to performance of each of a plurality of episodes performed by the robots, the current updated policy parameters can be provided (or retrieved) for utilization in performance of the episode.

A method for improving a cycle time of a process of a product is provided. The method includes: collecting process profile data from a plurality of tool groups running the process, and calculating values of a plurality of key-performance-indicators (KPIs) of each tool group including calculating a standard deviation of an output of a stage of a bottleneck tool group of the tool groups; feeding the values of the KPIs and a work-in-progress (WIP) of each tool group into a neural network model in order to output an impact on the WIP for each KPI of each tool group by the neural network model; selecting a set of major KPIs of each tool group from the KPIs according to the impact of each tool group; and controlling the tool groups according to the impact of the set of major KPIs of each tool group in order to reduce a total WIP.

The present disclosure provides an Industrial Internet of Things system conducive to system scalability and control method thereof. The system comprises a user platform, a service platform, a management platform, a sensor network platform, and an object platform. The service platform or the sensor network platform adopts independent layout. The service platform and the sensor network platform both include sub platforms and each sub platform is provided with a database, a processor, and/or an information channel. The object platform includes a production line, and the production line is configured with a plurality of sensors. In each sub platform of the sensor network platform, a same communication protocol is used with the sensors connected to a same sub platform of the sensor network platform. Any sub platform of the sensor network platform is connected with a unique sub platform of the service platform through the management platform.

The invention relates to method and system for automatic identification of a primary manufacturing process (PMP) from a three-dimensional (3D) model of a product. The method includes generating a plurality of images corresponding to a plurality of views of the product based on the 3D model of the product; determining a plurality of confidence score vectors, based on the plurality of images, using a first Artificial Neural Network (ANN) model; determining an aggregate confidence score vector, representing a pre-defined PMP category with maximum frequency, based on the plurality of confidence score vectors; extracting a set of manufacturing parameters associated with the product, based on the 3D model of the product; and identifying the PMP based on the aggregate confidence score vector and the set of manufacturing parameters, using a second ANN model.

A system includes: a robotic arm which has an instrument interface; a force/torque sensor for sensing forces at the instrument interface; a robot controller for controlling the robotic arm and to control a robot control parameter; and a system controller. The system controller: receives temporal force/torque data, wherein the temporal force/torque data represents the forces at the instrument interface over time during a collaborative procedure with a user; analyzes the temporal force/torque data to determine a current intention of the user and/or a state of the collaborative procedure; and causes the robot controller to control the robotic arm in a control mode which is predefined for the determined current intention of the user or state of the collaborative procedure, wherein the control mode determines the robot control parameter.

A material completeness detection method configured to detect whether materials of a target object in a physical production line are complete, includes: inputting an image of the target object in the physical production line into a material completeness detection algorithm to acquire a first detection result; inputting a virtual model of the target object in a virtual production line into the material completeness detection algorithm to acquire a second detection result, where the virtual production line is a DT of the physical production line; and acquiring a material completeness detection result of the target object based on the first detection result and the second detection result. The embodiments of the present disclosure can realize efficient and accurate material completeness detection.

Disclosed herein is technology for performing predictive modeling to identify inputs for a manufacturing process. An example method may include receiving expected output data for a manufacturing process, wherein the expected output data defines an attribute of an output of the manufacturing process; accessing a plurality of machine learning models that model the manufacturing process; determining, using a first machine learning model, input data for the manufacturing process based on the expected output data for the manufacturing process, wherein the input data comprises a value for a first input and a value for a second input; combining the input data determined using the first machine learning model with input data determined using the second machine learning model to produce a set of inputs for the manufacturing process, wherein the set of inputs comprises candidate values for the first input and candidate values for the second input.

The present disclosure provides an Industrial Internet of Things system conducive to system scalability and control method thereof. The system comprises a user platform, a service platform, a management platform, a sensor network platform, and an object platform. The service platform or the sensor network platform adopts independent layout. The service platform and the sensor network platform both include sub platforms and each sub platform is provided with a database, a processor, and/or an information channel. The object platform includes a production line, and the production line is configured with a plurality of sensors. In each sub platform of the sensor network platform, a same communication protocol is used with the sensors connected to a same sub platform of the sensor network platform. Any sub platform of the sensor network platform is connected with a unique sub platform of the service platform through the management platform.

In an embodiment, a method includes identifying a force and torque for a robot to accomplish a task and identifying context of a portion of a movement plan indicating motion of the robot to perform the task. Based on the identified force, torque, and context, a context specific torque is determined for at least one aspect of the robot while the robot executes the portion of the movement plan. In turn, a control signal is generated for the at least one aspect of the robot to operate in accordance with the determined context specific torque.

Current technologies allow a robot to acquire a tool only if the tool is in a set known location, such as in a rack. In an embodiment, a method and corresponding system, can determine the previously unknown pose of a tool freely placed in an environment. The method can then calculate a trajectory that allows for a robot to move from its current position to the tool and attach with the tool. In such a way, tools can be located and used by a robot when placed at any location in an environment.