A three-dimensional information acquiring unit for acquiring three-dimensional information of a target object, a gripping information acquiring unit for acquiring gripping information that defines a method of gripping the target object by using a picking hand, and an information management unit for storing, in a storage apparatus in association with each other, (i) three-dimensional information of the target object acquired by the three-dimensional information acquiring unit and (ii) gripping information acquired by the gripping information acquiring unit are included. The three-dimensional information of the target object may include at least one of origin position information and the coordinate axis information. A two-dimensional information acquiring unit for acquiring two-dimensional information of the target object may be further included.

A handling device according to an embodiment has an arm, a holder, a storage, and a controller. The arm includes at least one joint. The holder is attached to the arm and is configured to hold an object. The storage stores a function map including at least one of information about holdable positions of the holder and information about possible postures of the holder. The detector is configured to detect information about the object. The controller is configured to generate holdable candidate points on the basis of the information detected by the detector, to search the function map for a position in an environment in which the object is present, the position being associated with the generated holdable candidate points, and to determine a holding posture of the holder on the basis of the searched position. The function map associates a manipulability with each position in the environment in which the object is present. The manipulability is a parameter calculated from at least one joint angle of the holder.

Various embodiments of the present technology generally relate to robotic devices and artificial intelligence. More specifically, some embodiments relate to an artificial neural network training method that does not require extensive training data or time expenditure. The few-shot training model disclosed herein includes attempting to pick up items and, in response to a failed pick up attempt, transferring and generalizing information to similar regions to improve probability of success in future attempts. In some implementations, the training method is used to robotic device for picking items from a bin and perturbing items in a bin. When no picking strategies with high probability of success exist, the robotic device may perturb the contents of the bin to create new available pick-up points. In some implementations, the device may include one or more Computer-vision systems.

Various embodiments of the present technology generally relate to robotic devices and artificial intelligence. More specifically, some embodiments relate to a robotic device for picking items from a bin and perturbing items in a bin. In some implementations, the device may include one or more computer-vision systems. A computer-vision system, in accordance with the present technology, may use at least two two-dimensional images to generate three-dimensional (3D) information about the bin and items in the bin. Based on the 3D information, a strategy for picking up items from the bin is determined. When no strategies with high probability of success exist, the robotic device may perturb the contents of the bin to create new available pick-up points and re-attempt to pick up an item.

A method for operating a robotic system includes determining a discretized object model based on source sensor data; comparing the discretized object model to a packing plan or to master data; determining a discretized platform model based on destination sensor data; determining height measures based on the destination sensor data; comparing the discretized platform model and/or the height measures to an expected platform model and/or expected height measures; and determining one or more errors by (i) determining at least one source matching error by identifying one or more disparities between (a) the discretized object model and (b) the packing plan or the master data or (ii) determining at least one destination matching error by identifying one or more disparities between (a) the discretized platform model or the height measures and (b) the expected platform model or the expected height measures, respectively.

Various embodiments of the present technology generally relate to robotic devices and artificial intelligence. More specifically, some embodiments relate to a robotic device for picking items from a bin and perturbing items in a bin. The robotic device may include one or more picking elements and one or more perturbation elements for disturbing a present arrangement of items in the bin. In an exemplary embodiment, a perturbation element comprises a compressed air valve. In some implementations, the robotic device may also include one or more computer-vision systems. Based on image data from the one or more computer-vision systems, a strategy for picking up items from the bin is determined. When no strategies with high probability of success exist, the robotic device may perturb the contents of the bin to create new available pick-up points.

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 tool changer at a distal end of a robotic arm may include a proximal engagement plate and a tool may include a distal engagement plate magnetically engaged with the proximal engagement plate. The tool changer may be configured to magnetically engage and disengage with a variety of tools as different tools are needed for operations being performed by the robotic arm. Decisions regarding which tools to couple to the tool changer may be made on-the-fly and based on changing circumstances as the robotic arm is used to operate on objects.

A robot system (2a . . . 2d) is specified, which comprises a robot (1a, 1b) having a gripping unit (4) for collecting and placing down/throwing goods (26a, . . . 26g), wherein the goods (26a, . . . 26g) are differentiated into multiple types with respect to their dimensional stability, compressive stability, flexural rigidity, strength, their absolute weight and/or specific weight. When the goods (26a, . . . 26g) are manipulated, the robot (1a, 1b) and/or the gripping unit (4) are controlled depending on the type determined for the goods (26a, . . . 26g). Moreover, a method for operating the robot system (2a, . . . 2d) is specified.

When one of the objects placed on a placement board is sucked and held by a suction nozzle and is taken out by an actuator under the control of an operation controller, a first acquirer acquires a difference M between a first moment M1 applied to the suction nozzle and a second moment M2 applied to the suction nozzle when the object is taken out by the suction nozzle by a first taking-out movement distance. The operation controller performs control, based on the acquired difference between the moments, whether the taking-out operation is to be further continued.