An apparatus and a method for planning contact-interaction trajectories are provided. The apparatus is a robot that accepts contact interactions between the robot and the environment. The robot stores a dynamic model representing geometric, dynamic, and frictional properties of the robot and the environment, and a relaxed contact model to representing dynamic interactions between the robot and the object via virtual forces. The robot further determines, iteratively until a termination condition is met, a trajectory, associated control commands for controlling the robot, and virtual stiffness values by performing optimization reducing stiffness of the virtual force and minimizing a difference between the target pose of the object and a final pose of the object moved from the initial pose. Further, an actuator moves a robot arm of the robot according to the trajectory and the associated control commands.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that distributes skill bundles that can guide robot execution. One of the methods includes receiving data for a skill bundle from a skill developer. The data can include a definition of one or more preconditions for a robotic system to execute a skill; one or more effects to an operating environment after the robotic system has executed the skill; and a software module implementing the skill. The software module can define a state machine of subtasks. A skill bundle can be generated from the data received from the skill developer. Data identifying the generated skill bundle can be added to a skill registry. The skill bundle can be provided to the execution robot system for installation on the robot execution system.

A control device 1A mainly includes a display control means 15A and an operation sequence generation means 16A. The display control means 15A is configured to transmit display information S2 relating to a task to be executed by a robot to a display device 2A. The operation sequence generation means 16A is configured, in a case that the display control means 15A has received, from the display device 2A, task designation information that is input information which schematically specifies the task, to generate an operation sequence to be executed by the robot based on the task designation information Ia.

An apparatus and a method for planning contact-interaction trajectories are provided. The apparatus is a robot that accepts contact interactions between the robot and the environment. The robot stores a dynamic model representing geometric, dynamic, and frictional properties of the robot and the environment, and a relaxed contact model to representing dynamic interactions between the robot and the object via virtual forces. The robot further determines, iteratively until a termination condition is met, a trajectory, associated control commands for controlling the robot, and virtual stiffness values by performing optimization reducing stiffness of the virtual force and minimizing a difference between the target pose of the object and a final pose of the object moved from the initial pose. Further, an actuator moves a robot arm of the robot according to the trajectory and the associated control commands.

A remote controlled device comprises one or more memories and one or more processors. The one or more processors are configured to, when an event relating to a task being executed by a remote control object occurs: transmit information on a subtask of the task, receive a command relating to the subtask, and execute the task based on the command.

Described herein is a technique that provides a first robot associated with a first trust domain to identify and authenticate a second robot associated with a second trust domain. The identification and authentication technique described herein is based on a robot processing a variety of input signals obtained via sensors, in order to generate robot identification information that can be compared with known or trusted information. Advantageously, the identification technique occurs locally, for example, at the robot and at the location of the robot-to-robot interaction, eliminating any requirement for communicating with an authoritative remote or cloud-based service, at the time and place of the robot-to-robot interaction.

Software for controlling processes in a heterogeneous semiconductor manufacturing environment may include a wafer-centric database, a real-time scheduler using a neural network, and a graphical user interface displaying simulated operation of the system. These features may be employed alone or in combination to offer improved usability and computational efficiency for real time control and monitoring of a semiconductor manufacturing process. More generally, these techniques may be usefully employed in a variety of real time control systems, particularly systems requiring complex scheduling decisions or heterogeneous systems constructed of hardware from numerous independent vendors.

A programmable logic controller controls at least one robot to manipulate a plurality of workpieces. The programmable logic controller includes a sensor interface configured to receive sensor data that represents information of the workpieces. The programmable logic controller includes a scheduler configured to create a schedule that includes information representing an order in which the workpieces are to be manipulated. The schedule created by the scheduler is based on the sensor data. The programmable logic controller includes a synchronizer that is configured to receive the schedule. The synchronizer is configured to cause a robot to manipulate the workpieces based on the schedule and based on a function block. The function block is configured via the programmable logic controller.

Described herein is a technique that provides a first robot associated with a first trust domain to identify and authenticate a second robot associated with a second trust domain. The identification and authentication technique described herein is based on a robot processing a variety of input signals obtained via sensors, in order to generate robot identification information that can be compared with known or trusted information. Advantageously, the identification technique occurs locally, for example, at the robot and at the location of the robot-to-robot interaction, eliminating any requirement for communicating with an authoritative remote or cloud-based service, at the time and place of the robot-to-robot interaction.