A robot system includes: a robot including an end effector connected to an arm thereof; a vision sensor mounted to the robot; and a controller configured to output an operation signal that enables the robot to operate when an input is generated through a touch screen. Each of an object and a target to which the object is placed is inputted through the touch screen. The touch screen displays a recommendation region of the target in a distinguished manner.

A robotic automated filling and capping system is made up of a cartridge infeed conveyor, a cap infeed conveyor, an outfeed conveyor, a six-axis robot, and a selective compliance articulated robot arm, all of which are configured to work together to automatically, and sanitarily, fill and cap vape oil cartridges.

A parts fastening system using a cooperative robot that fastens a module part to a fastening target includes: a jig to load the module part at a predetermined position; a loading robot to grip the module part loaded on the jig, and to move and align the module part to a fastening area in which the module part is fastened to the fastening target; a fastening robot including a first camera, the fastening robot to fasten the module part to the fastening target; and a control device to control movements of the loading robot and the fastening robot.

Various embodiments described herein relate to techniques for object height detection for palletizing operations and/or depalletizing operations. In this regard, an automated industrial system comprises at least a column portion, a robot arm portion, and an end effector configured to grasp an object. An image-capturing device is mounted onto the automated industrial system and is configured to rotate, based on movement of the robot arm portion, to scan the object grasped by the end effector and to generate image-capturing data associated with the object. Furthermore, a processing device is configured to determine height data for the object based on the image-capturing data. The processing device is also configured to determine location data for the object with respect to a conveyor system based on the height data.

A robot includes a first arm having a hole and extending along a first axis, a second arm coupled to the first arm, and rotating around a second axis crossing the first axis, a sensor configured to detect a target, and an attachment member provided to the second arm, and configured to support the sensor, wherein the attachment member is inserted through the hole, and extending along the second axis. Further, the sensor may be located outside an outer surface of the first arm.

A control method for a robot system includes setting a robot arm in a first attitude, performing work in a first region of an object while moving a tool relative to the object by a moving stage with the first attitude maintained, setting the robot arm in a second attitude, imaging the object using a camera and correcting a position of the tool by driving of the moving stage based on an imaging result with the second attitude maintained, and performing the work in a second region of the object while moving the tool relative to the object by the moving stage with the second attitude maintained.

A machine learning method for learning an action of a robot including a hand to pick out a workpiece from a container containing a plurality of the workpieces stacked in bulk and install the workpiece such that the workpiece is in a predetermined installation state includes learning a reverse-order action of removing, by the hand, the workpiece in the predetermined installation state after completion of installation, and learning an installation order of the workpiece based on a learning result of the reverse-order action of removing the workpiece.

A robotic system includes first and second manipulator assemblies in an operating environment and having separately movable bases. A processing unit is configured to receive first sensor data from a first plurality of sensors disposed on the first manipulator assembly, wherein the first sensor data provide spatial information about the operating environment external to the first manipulator assembly. A first spatial relationship of the second manipulator assembly relative to the first manipulator assembly is determined using data including the first sensor data. A first alignment relationship between the first and second manipulator assemblies is established based on the first spatial relationship. Based on the first alignment relationship, motion of the second manipulator assembly is commanded in response to a command from a first input device operable by an operator.

A method for picking up an object by means of a robotic device. The method includes obtaining at least one depth image of the object; determining, for each of a plurality of points of the object, the value of a measure of the scattering of surface normal vectors in an area around the point of the object; supplying the determined values to a neural network configured to output, in response to an input containing measured scattering values, an indication of object locations for pick-up; determining a location of the object for pick-up from an output which the neural network outputs in response to the supply of the determined values; and controlling the robotic device to pick up the object at the determined location.

A method for training a neural network to derive, from a force and a moment exerted on an object when pressed on a plane in which an insertion for inserting the object is located, a movement vector to insert an object into an insertion. The method includes, for a plurality of positions in which the object or the part of the object held by the robot touches a plane in which the insertion is located, controlling the robot to move to the position, controlling the robot to press the object onto the plane, measuring the force and moment experienced by the object, scaling the pair of force and moment by a number randomly chosen between zero and a predetermined positive maximum number and labelling the scaled pair by a movement vector between the position and the insertion, and training the neural network using the labelled pairs of force and moment.