A robot control device includes: a learned model created through learning work data composed of input and output data, the input data including states of a robot and the surroundings where humans operate the robot to perform a series of works, the output data including human operation corresponding to the case or movement of the robot caused thereby; a control data acquisition section that acquires control data by obtaining output data related to human operation or movement from the model, being presumed in response to and in accordance with the input data; a completion rate acquisition section acquiring a completion rate indicating to which progress level in the series of works the output data corresponds; and a certainty factor acquisition section that acquires a certainty factor indicating a probability of the presumption in a case where the model outputs the output data in response to the input data.

A control device 1X mainly includes an operation sequence generation means 16X, a first robot control means 171X, a switching determination means 18X, and a second robot control means 172X. The operation sequence generation means 16X is configured to generate an operation sequence of a robot. The first robot control means 171X is configured to perform a first robot control that is a control of the robot based on the operation sequence. The switching determination means 18X is configured, during execution of the first robot control, to determine, based on the operation sequence, whether or not to switch to a second robot control, which is a control of the robot based on an external input. The second robot control means 172X is configured, if it is determined by the switching determination means 18X that the switching is required, to perform the second robot control.

An information technology system for a distributed manufacturing network includes an additive manufacturing management platform configured to manage process and production workflows for a set of distributed manufacturing network entities through design, modeling, printing, and supply chain stages. The information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities of the distributed manufacturing network to optimize digital production processes and workflows. The information technology system includes a distributed ledger system integrated with a digital thread configured to provide unified views of workflow and transaction information to entities in the distributed manufacturing network.

A distributed manufacturing network includes a distributed ledger system and an artificial intelligence system. The distributed ledger system is integrated with digital threads of a set of distributed manufacturing network entities for storing information on event, activities and transactions related to the distributed manufacturing network entities. The artificial intelligence system is configured to learn on a training set of outcomes, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.

A dynamic vision system includes a variable focus liquid lens optical assembly. The dynamic vision system includes a control system configured to adjust one or more optical parameters and data collected from the variable focus liquid lens optical assembly in real time. The dynamic vision system includes a processing system that dynamically learns on a training set of outcomes, parameters, and data collected from the variable focus liquid lens optical assembly to train one or more machine learning models to recognize an object.

A robot fleet platform for preparing a job request includes one or more processors configured to execute instructions. The instructions include a job request ingestion system configured to receive job content relating to at least one of picking, packing, moving, storing, warehousing, transporting or delivering of items in a supply chain. The job content includes an electronic job request and related data. The instructions include a job content parsing system configured to apply filters to the received job content to identify candidate portions thereof for robot automation. The instructions include a fleet intelligence layer that activates a set of intelligence services to process terms in the candidate portions of the job content and receive therefrom at least one recommended robot task and associated contextual information. The instructions include a demand intelligence layer that provides real time information relating to a parameter of demand for the items in the supply chain.

A digital twin system includes a library of different types of robot operating unit digital twins stored in a storage system. The digital twin system includes one or more interfaces through which information associated with a physical robot operating unit corresponding to an instance of the robot operating unit digital twins is communicated. The digital twin system includes a set of processors that execute a set of computer-readable instructions to collectively operate one or more execution environments for executing instances of a portion of the different types of robot operating unit digital twins. The digital twin system also generates digital twin instances for individual robot operating units, a team of robot operating units, or a fleet of robot operating units. The digital twin system simulates operation of a physical robot by executing an instance of a digital twin generated for the physical robot based on information communicated through the interfaces.

A system includes a fleet resources data store that maintains a fleet resource inventory indicating fleet resources that can be assigned to perform tasks. For each fleet resource, the inventory indicates features of each fleet resource and a respective status. A set of task definitions is accessible to an intelligence layer to facilitate improving task definition based on feedback from task-specific outcomes. The system receives a job request for a robotic fleet to perform a job and determines a job definition data structure indicating a set of tasks to be performed for the job. The system applies an outcome of performing a task by a resource assigned to perform the task to a machine learning system of the intelligence layer that facilitates improving, based on the outcome, the set of task definitions. The system updates the set of task definitions based on a result of applying the machine learning system.

A robot fleet management platform includes one or more processors configured to execute instructions. The instructions include receiving a job request comprising information descriptive of job deliverable and request-specific constraints for delivering the job deliverable. The instructions include applying content and structural filters to content received in association with a job request to identify portions thereof suitable for robot automation. The instructions include establishing a set of robot tasks, each defining at least a type of robot and a task objective, based on the portions of the job request that are suitable for robot automation and meet a first fleet objective. The instructions include applying fleet configuration services to the job content and the set of robot tasks to produce a fleet resource configuration data structure for the job request that associates at least one robot operating unit with each task in the set of tasks and robot adaptation instructions.