To reliably execute processing of a client or application to be prioritized according to processing contents for each processing state for an industrial machine. A control device relates to a control device that executes processing of processing requests for an industrial machine from a plurality of clients, the control device including: a priority determination unit configured to determine, in a case in which a plurality of the processing requests is received from the plurality of clients, a priority of each of the plurality of clients according to a processing state of the industrial machine, and a processing switching unit configured to switch an order of processing of each of the plurality of the processing requests based on a priority of each of the plurality of clients determined by the priority determination unit.

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 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.

Robots are deployed for handling different tasks in various field of applications. For the robots to function, task planning is required to be done. During the task planning, goal setting is done, as well as actions to be executed for corresponding to each goal are decided. Traditionally, this is carried out first and then the robots start executing the task plan, thereby failing to capture any change in the environment the robots operate, post the task plan generation. Disclosed herein is a method and system for robotic task planning in which a task plan is generated and is executed. However if the task execution fails due to change in any of the parameters/factors, then the system dynamically invokes an adaptation and re-planning mechanism which either updates the already generated task plan (by capturing the change) or generates a new task plan, which the robot can execute to achieve the goal.

[Problem] To optimally switch tasks of a robot depending on the situation. [Solution] According to the present disclosure, there is provided a robot control apparatus including: a determination unit configured to determine whether or not an end condition of a task is satisfied when a robot performs the task; and a switching unit configured to perform switching to a next task corresponding to the end condition in a case where the end condition is satisfied. With this configuration, it is possible to optimally switch tasks of the robot depending on the situation.

A method for planning a manipulation task of an agent, particularly a robot. The method includes: learning a number of manipulation skills wherein a symbolic abstraction of the respective manipulation skill is generated; determining a concatenated sequence of manipulation skills selected from the number of learned manipulation skills based on their symbolic abstraction so that a given goal specification indicating a given complex manipulation task is satisfied; and executing the sequence of manipulation skills.

Systems, methods, devices, and other techniques for planning and re-planning robot motions. The techniques can include obtaining a schedule that defines an execution order for a plurality of plans, each plan defining a sequence of tasks; providing instructions to the robotic system to execute plans from the plurality of plans according to the schedule; obtaining measurements for one or more parameters that represent a state of the robotic system or its environment that results from execution of at least one plan from the plurality of plans; determining, based on the measurements, that a particular plan from the plurality of plans, which has not completed execution, is to be revised; generating, using the measurements, a revised version of the particular plan; and adjusting the schedule so as to resolve a conflict between the revised version of the particular plan and at least one other plan in the plurality of plans.

This disclosure relates generally to robotic network, and more particularly to a method and system for hierarchical decomposition of tasks and task planning in a robotic network. While a centralized system is used for action planning in a robotic network, any communication network issues can adversely affect working of the robotic network. Further, hardcoding one or more specific tasks to a robot restricts use of the robots irrespective of capabilities of the robots. The robotic agent decomposes a goal assigned to the robot to multiple sub-goals, and for each sub-goal, identifies one or more tasks to be executed/performed by the robot. An action plan is generated based on all such tasks identified, and the robot executes the action plan, in response to the goal assigned to the robot.

The techniques disclosed herein enable a machine learning model to learn a termination condition of a sub-task. A sub-task is one of a number of sub-tasks that, when performed in sequence, accomplish a long-running task. A machine learning model used to perform the sub-task is augmented to also provide a termination signal. The termination signal indicates whether the sub-task's termination condition has been met. Monitoring the termination signal while performing the sub-task enables subsequent sub-tasks to seamlessly begin at the appropriate time. A termination condition may be learned from the same data used to train other model outputs. In some configurations, the model learns whether a sub-task is complete by periodically attempting subsequent sub-tasks. If a subsequent sub-task can be performed, positive reinforcement is provided for the termination condition. The termination condition may also be trained using synthetic scenarios designed to test when the termination condition has been met.

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.