A method, system and computer program product are provided for aligning wire contacts with wire contact insertion holes of a connector to facilitate the automated insertion of the wire ends of a wire bundle assembly into the wire contact insertion holes of a connector. Methods may include: obtaining captured images from at least two image capture devices attached to an end-effector of a robot of a wire gripper of the end-effector; detecting, within at least one image from each of the at least two image capture devices, a wire contact; detecting, within at least one image from each of the at least two image capture devices, one or more insertion holes of the connector; identifying corrective movement for the robot end-effector that aligns a target hole of the one or more insertion holes of the connector with the wire connector; and causing the robot to move the end-effector according to the identified corrective movement.

Provided is a robot control device capable of facilitating the work of setting a control center for controlling the operation of a robot. The robot control device controls a robot manipulator which is equipped with an end effector. The robot control device includes: an image processing unit that, by using a feature extraction model for detecting images of the robot manipulator, and position/posture information of the robot manipulator, detects, from images (M1, M2) in which at least part of the robot manipulator is captured, a position in a three-dimensional space which corresponds to designated positions (P1, P2) designated on the images, as a position relative to the robot manipulator; and a coordinate system determination unit that sets a control center, for controlling the operation of the robot manipulator, to the position detected in the three-dimensional space.

A robot includes: an end effector including a tubular structure and a force sensor; and a controller, the controller to: control the robot holding a terminal to insert the terminal into an insertion hole; control the robot to, after the inserting, position an outer peripheral surface of a distal end of the tubular structure horizontally and bend the tubular structure at a predetermined angle; and control the robot to, after the positioning and bending, advance the end effector through a first distance that is predetermined.

A method for controlling a robot to perform a task. The method includes acquiring, for each target of a sequence of targets comprising at least one intermediate target of the task and a final target of a task, a target image data element comprising at least one target image from a perspective of an end-effector of the robot at a respective target position of the robot and successively according to the sequence of targets, for each target in the sequence, acquiring, for the target, an origin image data element, supplying the origin image data element and the target image data 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.

A technique allows a robot gripping one of two objects to perform appropriate surface alignment between the two objects. A control apparatus for a robot includes a virtual sensor associated with a force sensor and set on a control plane for predetermined control to control a position of a first object relative to a second object by causing a first surface of the first object gripped by a gripper to abut on a second surface of the second object, and a controller that performs the predetermined control based on a detection result from the virtual sensor using a control point. The virtual sensor is projected on an imaginary line at a projection position between a predetermined contact point and the control point in an extending direction of the imaginary line on the control plane.

A technique automates robotic assembly of two parts with a fastener structure including an engaging portion and a receiving portion. A robot system includes a robot that grips a workpiece, a force sensor located on the robot to measure a force and a moment acting on the workpiece, and a controller that controls the robot. The controller monitors, while moving the workpiece in a direction along a first axis and inserting the workpiece into a part, a change in a force F in the direction along the first axis and a change in a moment M about a second axis perpendicular to the first axis measured by the force sensor to determine a state of assembly of the workpiece with the part.

The present invention makes it possible, in force control during production, to automatically adjust a force control parameter optimally so as not to cause oscillation of a robot or failure of work. This robot system comprises: a robot arm having a hand at the end thereof for holding a workpiece; a force detector for detecting a force and moment received by the workpiece held by the hand; and a control device for moving the workpiece held by the hand with respect to a target object while performing force control of the robot arm to correct a position error and an attitude error of the workpiece, on the basis of a predetermined force control parameter and a detected value of the force detector. The control device has a parameter automatic adjustment unit for automatically adjusting the force control parameter by executing the moving of the workpiece with respect to the target object a plurality of times. The parameter automatic adjustment unit automatically adjusts the force control parameter by executing the moving of the workpiece with respect to the target object from a plurality of attitude error directions among a plurality of position error directions and the plurality of attitude error directions.

A control apparatus of a robot may include a state obtaining unit configured to obtain state observation data including flexible related observation data, which is observation data regarding a state of at least one of a flexible portion, a portion of the robot on a side where an object is gripped relative to the flexible portion, and the gripped object; and a controller configured to control the robot so as to output an action to be performed by the robot to perform predetermined work on the object, in response to receiving the state observation data, based on output obtained as a result of inputting the state observation data obtained by the state obtaining unit to a learning model, the learning model being learned in advance through machine learning and included in the controller.

A method for training a machine learning model to derive a movement vector for a robot from image data. The method includes acquiring images from a perspective of an end-effector of the robot, forming training image data elements from the acquired images, generating augmentations of the training image data elements, training an encoder network using contrastive loss and training a neural network to reduce a loss between movement vectors output by the neural network in response to embedding outputs provided by the encoder network and respective ground truth movement vectors.

An improvement in reliability of manufacture of an assembly including a tap is achieved. A control section of a manufacturing system for manufacturing an assembly including a tap causes a robot to perform: a step of producing a first assembly by gripping a cap having an opening and engaging the cap with a collet having a recess such that the cap is placed on the collet; a step of producing a second assembly by gripping the first assembly and inserting the first assembly into a recess of a tap holder; and a step of producing a third assembly, which is an assembly including a tap, by gripping the tap and inserting the tap through the opening of the cap of the second assembly into the recess of the collet.