A method for controlling a robot to perform a task. The method includes acquiring a target image data element comprising at least one target image from a perspective of an end-effector of the robot at a target position of the robot in which the robot has performed the task, acquiring an origin image data element comprising at least one origin image from the perspective of the end-effector of the robot at an origin position of the robot, supplying the origin image data element and the target image data element to a machine learning model configured to derive a delta movement between the origin current position and the target position and controlling the robot to move according to the delta movement to perform the task.

A metallurgical technology probe insertion calibration method employing visual measurement and an insertion system thereof are provided. A vision sensor (5), a cylindrical rod (1), and a metallurgical technology probe (2) are used to construct an agreed region (6). In the agreed region (6), the vision sensor (5) acquires relative positions and orientations of the cylindrical rod (1) and the metallurgical technology probe (2), and an acquired position and orientation result is used to control a driving device (3) to insert the cylindrical rod (1) into the metallurgical technology probe (2). To improve the accuracy and reliability of the insertion, a standard probe (7) and a fixing device (4) are used together to perform effective calibration on an initial position, orientation, and axis in the insertion.

Proposed is an apparatus for inserting an electrode assembly, the apparatus including a clamping unit configured to clamp or unclamp the electrode assembly, and an articulated robot including one or more links rotatably connected to each other by joint shafts and configured to mount the clamping unit on a front link. Here, the electrode assembly is inclinedly introduced and accommodated in a pouch through an open space between an upper pouch and a lower pouch. In addition, proposed is a method of inserting an electrode assembly using the above-described apparatus.

An approach to positioning one or more robotic arms in an assembly system may be described herein. For example, a system for robotic assembly may include a first robot, a second robot, and a control unit. The control unit may be configured to receive a first target location proximal to a second target location. The locations may indicate where the robots are to position the features. The control unit may be configured to calculate a first calculated location of the first feature of the first subcomponent, measure a first measured location of the first feature of the first subcomponent, determine a first transformation matrix between the first calculated location and the first measured location, reposition the first feature of the first subcomponent to the first target location using the first robot, the repositioning based on the first transformation matrix.

The disclosure is provided to shorten a total allowed cycle time of insertion or a press-fitting operation. A control device of a robot performing insertion or a press-fitting operation includes: a measuring part, measuring an elapsed time from a time point of starting a correcting operation in a case where the insertion or the press-fitting operation does not end normally; a calculating part, calculating a total remaining time of the correcting operation by subtracting a time from a start to an end of the correcting operation from a preset total allowed time of the correcting operation; a comparing part, comparing the remaining time with a required operation time which is an elapsed time from the start to the end of the correcting operation; and a control part, interrupting the correcting operation before the allowed time elapses in a case where the remaining time is less than the required operation time.

A robotic line kitting system is disclosed. In various embodiments, data indicating for each of a plurality of operating zones comprising a workspace with which the robotic line kitting system is associated a corresponding safety state information is stored. The data is used to dynamically schedule and perform tasks associated with a higher level objective, including by operating the one or more robotic instrumentalities in one or more operating zones, if any, indicated by said data to currently be available to perform tasks using the one or more robotic instrumentalities and by not operating the one or more robotic instrumentalities in one or more operating zones, if any, indicated by said data to not currently be available to perform tasks using the one or more robotic instrumentalities.

A machine learning device and a machine learning method are provided. A controller, which serves as the machine learning device and the machine learning method, is provided to perform machine learning of algorithms for arranging a flexible wire-like workpiece to a predetermined state using an industrial robot. In an embodiment, the machine learning device includes: an acquisition unit that acquires, as state variables, a state of the workpiece before arrangement starts and a state of the workpiece during arrangement; and a learning unit that performs machine learning of an algorithm for arranging the workpiece based on the state variables acquired by the acquisition unit.

A robot control device includes: a trained model built by being trained on work data; a control data acquisition section which acquires control data of the robot based on data from the trained model; base trained models built for each of a plurality of simple operations by being trained on work data; an operation label storage section which stores operation labels corresponding to the base trained models; a base trained model combination information acquisition section which acquires combination information when the trained model is represented by a combination of a plurality of the base trained models, by acquiring a similarity between the trained model and the respective base trained models; and an information output section which outputs the operation label corresponding to each of the base trained models which represent the trained model.

A robot system has a manipulator that conveys a conveying object to a conveyance destination having a bottom surface and a wall surface connected to the bottom surface. The system includes: a first process in which the manipulator puts a first conveying object on the bottom surface of the conveyance destination; a second process in which the manipulator conveys a second conveying object to a position that does not come into contact with the first conveying object on the bottom surface of the conveyance destination; and a third process in which the manipulator moves the second conveying object in the direction of the first conveying object from the position that does not come into contact with the first conveying object, and moves the first conveying object by further moving the second conveying object after coming into contact with the first conveying object.

Provided are a plate holding device, a plate detaching apparatus, a plate attaching apparatus and a plate attaching-detaching apparatus which are capable of reliably attaching and/or detaching a plate with respect to a plate-receiving metal frame. The plate holding device comprises: a plurality of holding members for holding a plate for a sliding nozzle device; widening and narrowing mechanisms to selectively widen and narrow a distance between the holding members; a pressing unit for pressing a central region of the plate when the plate is held by the holding members; and a force sensor for detecting a force received by the holding members and/or the pressing unit from the held plate. The plate holding device is configured to be mounted to a distal end of a robot arm.