A method and system for programming picking and placing of a workpiece is provided. Embodiments may include associating a workpiece with an end effector that is attached to a robot and scanning the workpiece while the workpiece is associated with the end effector. Embodiments may also include determining a pose of the workpiece relative to the robot, based upon, at least in part, the scanning.

A system and method for remote control of a mobile device is provided herein. The system includes a primary receiver for providing primary command and control of the mobile device; a secondary receiver for providing secondary command and control of the mobile device; the mobile device configured to respond to command and control signals sent by any of the primary receiver and the secondary receiver; and a relay platform for relaying the command and control signals throughout the system. The primary receiver may include an extended reality component.

A control method of a robot, the robot including a first member, a second member connected to the first member, a drive device configured to rotate or slide the second member with respect to the first member, and an end effector connected to the second member, wherein posture of the end effector is changed by drive of the drive device, the robot control method includes detecting, based on an output signal from an inertial sensor disposed on the end effector, a gravity influence amount indicating a degree of influence of gravity received by the end effector, determining, based on the detected gravity influence amount, a drive algorithm for the drive device from among a plurality of drive modes, and driving the drive device by the determined drive algorithm.

In embodiments, a holding device includes a suction pad, a first link, a second link, a base, and a tube member. The first link supports the suction pad so that the suction pad can rotate around a first rotation axis. The second link supports the first link so that the first link can rotate around a second rotation axis. The base supports the second link so that the second link can rotate around a third rotation axis. The tube member communicates the suction pad with the base and can be bent. The second rotation axis and the third rotation axis are not parallel to each other.

An apparatus and method for calibrating or teaching a robot, the apparatus includes a reflective photoelectric sensor arranged on a gripper of the robot and a controller. The controller is configured to: cause the reflective photoelectric sensor to scan over a target object; monitor changes in an output signal from the reflective photoelectric sensor; for each detected change exceeding a threshold, determine a coordinate of a gripping component on the gripper in a robot coordinate system, to obtain a set of coordinates; determine a position of the target object in the robot coordinate system based on the set of coordinates and a predefined offset value (PO) between the reflective photoelectric sensor and the gripping component; and store the position of target object for future use in assembling objects.

A robot hand controller includes an air supply unit configured to supply air into fingers of a robot hand and configured to discharge air in the fingers, and a controller configured to control the air supply unit, where the air supply unit includes two or more air passages respectively connected to the different fingers, the air passages capable of supplying the air into the fingers and discharging the air in the fingers independently from each other, and the controller controls supply and discharge of the air through each of the two or more air passages in response to a shape of the workpiece and an object in a vicinity of a transport destination of the workpiece.

An example apparatus of the present disclosure may include an end effector. The end effector may include a pair of actuator-driven fingers that each include a compliant tip. The compliant tips may be used to scoop underneath items as part of item manipulation.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for automatically generating object representations. One of the methods includes grasping, by a robot, an object at a first grasp point and generating a first partial object mesh based on one or more first sensor measurements of the object when held by the robot at the first grasp point. A second grasp point is identified for the object that is located in a region captured by the one or more first sensor measurements. A second partial object mesh is generated based on one or more second sensor measurements of the object when held by the robot at the second grasp point.

A human-like tactile perception apparatus for providing enhanced tactile information (feedback data) from an end-effector/gripper to the control circuit of an arm-type robotic system. The apparatus's base structure is attached to the gripper's finger and includes a flat/planar support plate that presses a pressure sensor array against a target object during operable interactions. The pressure sensor array generates pressure sensor data that indicates portions of the array contacted by surface features of the target object. A sensor data processing circuit generates tactile information in response to the pressure sensor data, and then transmits the tactile information to the robotic system's control circuit. An optional mezzanine connector extends through an opening in the support plate to pass pressure sensor data to the processing circuit. An encapsulating layer covers the pressure sensor array and transmits pressure waves generated by slipping objects to enhance the tactile information.

A grasp generation technique for robotic pick-up of parts. A database of solid or surface models is provided for all objects and grippers which are to be evaluated. A gripper is selected and a random initialization is performed, where random objects and poses are selected from the object database. An iterative optimization computation is then performed, where many hundreds of grasps are computed for each part with surface contact between the part and the gripper, and sampling for grasp diversity and global optimization. Finally, a physical environment simulation is performed, where the grasps for each part are mapped to simulated piles of objects in a bin scenario. The grasp points and approach directions from the physical environment simulation are then used to train neural networks for grasp learning in real-world robotic operations, where the simulation results are correlated to camera depth image data to identify a high quality grasp.