Provided is an operation command generation device, which is configured to generate an operation command, which is a collection of jobs to be performed by a process system of at least a robot, based on a protocol chart of at least a plurality of process symbols, the operation command generation device circuitry includes: a job generation unit configured to generate, based on the protocol chart, a job; a priority instruction unit configured to instruct a priority condition for determining a job execution order; and an execution order determination unit configured to determine an execution order of the jobs based on the priority instructed by the priority instruction unit by using a first condition about repeatedly execution of the jobs according to the number of the containers and a second condition about execution order of the jobs according to the arrangement of the process symbols in the protocol chart.

A system for analyzing geometric properties for an object includes designing the object in a first computer process and producing information relating to the geometric properties of the object, and receiving the information in a second computer processor which identifies a first portion of the geometric property information as masked or private and second portion identified as public or shared, analysis is performed by the second processor on the public/shared portion of the geometric property information. An output based on the analysis may be provided to an industrial system performing processes on the object. A binary privacy label may be assigned to each triangle in a set of triangles representing the surfaces of the object in a 3D object mesh. The privacy label denotes an associated triangle as being private or shared. The system may be used to produce a set of planned grasps for a robotic gripper.

Robot control systems, methods, control modules and computer program products that leverage one or more large language model(s) (LLMs) in order to achieve at least some degree of autonomy are described. Robot control parameters and/or instructions may advantageously be specified in natural language (NL) and communicated with the LLM via a recursive sequence of NL prompts or queries. Corresponding NL responses from the LLM may then be converted into robot control parameters and/or instructions. In this way, an LLM may be leveraged by the robot control system to enhance the autonomy of various operations and/or functions, including without limitation task planning, motion planning, human interaction, and/or reasoning about the environment.

A programming assistance apparatus includes circuitry. The circuitry generates a first display data to be displayed in a first input area in which to input, for each of a plurality of task groups including a plurality of tasks, a first condition under which at least one robot executes the tasks. The circuitry generates a second display data to be displayed in a second input area in which to input a second condition for an execution order of the plurality of task groups. The circuitry sets the first condition based on an input into the first input area. The circuitry sets the second condition based on an input into the second input area. The circuitry generates, based on the first condition and the second condition, a motion program for causing the at least one robot to execute the plurality of task groups.

A robotic controller is provided for generating sequences of movement primitives for sequential tasks of a robot having a manipulator. The controller includes at least one control processor, and a memory circuitry storing a dictionary including the movement primitives, a pretrained learning module, and a graph-search based planning module having instructions stored thereon. The controller to perform steps acquiring a planned task provided by an interface device operated by a user, wherein the planned task is represented by an initial state and a goal state with respect to an object, generating a planning graph by searching a feasible path of the object for the novel task using the graph-search based planning module and selecting movement primitives from the dictionary in the pretrained learning module, wherein the pretrained learning module has been trained based on demonstration tasks, parameterizing the feasible path represented by the movement primitives as dynamic movement primitives (DMPs) using the initial state and goal state, and implementing the parameterized feasible path as a trajectory according to the selected movement primitives using the manipulator of the robot by tracking and following the parameterized for the planned task.

Robot control systems, methods, control modules and computer program products that leverage one or more large language model(s) (LLMs) in order to achieve at least some degree of autonomy are described. Robot control parameters and/or instructions may advantageously be specified in natural language (NL) and communicated with the LLM via an NL prompt or query. The LLM module provides a task plan in NL, which can be evaluated for at least one fault or error. If at least one fault or error is identified, the LLM module can be queried to provide a resolution.

A process plan optimization method for manufacturing a workpiece by adding a material in a plurality of layers is provided. The method includes: building a predicting model, the predicting model configured to predict a temperature variation of at least a portion of the workpiece; predicting an expected temperature variation of the portion of the workpiece to be manufactured during a given time period based on the predicting model and the process plan; and adjusting the process plan in response to the expected temperature variation of the portion failing to meet a preset condition, to make the expected temperature variation of the portion meet the preset condition.

Methods, apparatus, systems, and computer-readable media are provided for determining, based on a task to be performed by a robot and past behavior by robots while performing tasks similar to the task, a suggested task-level movement parameter for application to movement of the robot while performing the task; providing output indicative of the suggested task-level movement parameter; receiving input indicative of user selection of the suggested task-level movement parameter or a user-defined task-level movement parameter; determining, based on the received input, an actual task-level movement parameter to be applied to movement of the robot while performing the task; and identifying, based on the actual task-level movement parameter, a plurality of component-level movement parameters to be applied to a plurality of motion primitives implemented by one or more operational components of the robot to perform the task.

A method of generating a robot operation command includes extracting a work command from an overall work flow; extracting, from a command definition database, a command definition corresponding to the extracted work command, the work command having been read; generating, by referring to the read work command, a set of unit jobs in which at least one work command is arranged in order, based on the extracted command definition; generating, for a plurality of unit jobs, a connecting job for causing a robot to be moved from an end position of a previous unit job to a start position of a subsequent unit job; and generating a robot operation command in which the unit jobs and the connecting job are continuous.

Robot control systems, methods, control modules and computer program products that leverage one or more large language model(s) (LLMs) in order to achieve at least some degree of autonomy are described. Robot control parameters and/or instructions may advantageously be specified in natural language (NL) and communicated with the LLM via a recursive sequence of NL prompts or queries. Corresponding NL responses from the LLM may then be converted into robot control parameters and/or instructions. In this way, an LLM may be leveraged by the robot control system to enhance the autonomy of various operations and/or functions, including without limitation task planning, motion planning, human interaction, and/or reasoning about the environment.