A robot teaching device capable of simplifying a work involved in teaching a robot. The robot teaching device includes a position data storing section configured to store target position data for a robot; an operation instruction storing section configured to store an operation instruction for arranging the robot at a target position, the operation instruction not including the target position data; and a position data writing section configured to acquire current position data of the robot when the operation instruction is input, the position data writing section further configured to write, to the position data storing section, the current position data as the target position data together with a unique identifier, in which the identifier of the target position data is automatically given to the operation instruction being input to teach an operation to the robot.

Computerized engineering tool and methodology to develop neural skills for computerized autonomous systems, such as a robotics system (50), are provided. A disclosed computerized engineering tool (10) may involve an integrated arrangement of respective modular functionalities arranged in a closed loop, such as may include a physics engine (14), a neural data editor (16), an experiment editor (18), a neural skills editor (20), and a machine learning environment (22). Disclosed embodiments are conducive to cost-effectively simplifying development efforts involving neural skills, such as by reducing the time involved to develop the neural skills involved in any given robotics system and by reducing the level of expertise involved to develop neural skills.

Apparatuses, systems, and techniques provide a policy that can be executed to cause a machine to move. In at least one embodiment, a first policy layer is provided to cause the machine to execute a first motion that causes the machine to accelerate to reach an unbiased state. A second policy layer is provided to cause the machine to execute a second motion without influencing the unbiased state to be reached by machine. The policy can comprise the first and second policy layers.

A robot controller configured to control operation of a robot includes: a memory, which is configured to store a system configuration including at least user-defined project data and project-independent software; and a processor operable to execute the system configuration in accordance with a currently selected project. The robot controller is configured to: obtain a backup copy including user-defined project data and project-independent software; obtain restore input indicating a portion of the user-defined project data and project-independent software in the backup copy; and perform a partial restore of the backup copy, wherein only the indicated portion is loaded into the memory of the robot controller.

A technique allows efficient registration of an accurate gripping position of a robot hand with an auxiliary view appearing on a screen in accordance with the robot hand. A user selects a hand type to be used in gripping a gripping target and designates an auxiliary view to be rendered in accordance with the hand. In response to a two-finger hand being selected (step S11), a plane (step S13), a cylinder (step S14), or a rectangular prism (step S15) is rendered based on the view designated by the user (step S12). In response to a suction hand being selected, a plane is rendered (step S16).

A substrate assembling device (1) includes a first end effector 10 attached to a first arm (3), a second end effector 20 attached to a second arm (3), and a controller 4. The second end effector 20 includes a pair of grippers 22 configured to grip a second substrate 102, and a placing part 23 where threaded elements are placed. The controller 4 is adapted to control operations of the first arm and the second arm to position the second substrate 102 on a first substrate 101 while gripping the second substrate 102 by using the pair of grippers 22 of the second end effector 20, and hold the threaded element placed on the placing part 23 of the second end effector 20 and fasten the held threaded element, by using the first end effector 10, to join the first substrate 101 and the second substrate 102 together.

A work management system is applied to a work area in which a predetermined work is executed by a worker and a robot. The work management system includes a control section configured to control the robot such that a specified operation set in advance is included in an operation by the robot executing the predetermined work, an information acquisition section configured to acquire work information indicating a presence or absence of the specified operation in at least one of an execution result and an execution process of the predetermined work, and an identification section configured to identify whether a work subject of the predetermined work is the worker or the robot based on the work information acquired by the information acquisition section.

A nut runner, an attachment unit, and a sliding member are provided in a second arm that is a leading arm of the robot and has a leading end shaft movable along the direction of an elevation axis. The nut runner includes a screw fastening driver, a drive shaft rotationally driven by the screw fastening driver, and an extension bar that is so connected to rotate together with the drive shaft in the circumferential direction and to move in the axial direction. The attachment unit fixedly connects the screw fastening driver to the second arm so that a screw fastening axis parallel to the elevation axis serves as a rotation axis of the screw fastening driver. The sliding member is connected to the leading end shaft and supports the extension bar so as to move together in the axial direction and are rotatable relatively in the circumferential direction.

An exemplary method and system are disclosed to flexibly and adaptably manufacture and assemble a workpiece by using recordings of a user in machine learning/artificial intelligence algorithms to train a robot for subsequent automated manufacture. Machine learning and artificial intelligence learning can generate libraries of generalized dynamic motion primitives that can be subsequently combined for any type of manufacturing or assembling activity. The exemplary method and system can flexibly generate a model of an existing workpiece as a template or primer workpiece that can then be used in conjunction with the DMP operations to fabricate subsequent workpieces.

A method for preemptive control planning can include: optionally determining a graph that defines dependencies between behaviors; executing an execution plan for an executing behavior; generating a set of predictions with the executing behavior; propagating the set of predictions to child behaviors of the executing behavior; determining child execution plans and child predictions with the child behaviors; repeating the above for descendant behaviors of each child behavior; determining a realized world state associated with completion of the execution plan; and executing a child execution plan associated with a world state prediction matching the realized world state.