Abstract: Various embodiments are directed to methods, apparatuses, systems, computing devices, computing entities, and the like for robotically depalletizing objects. In various embodiments, a controller-implemented method for robotic depalletization may comprise detecting a first object of a plurality of palletized objects arranged in a plurality of pallet layers, the first object defining at least a portion of a top pallet layer; identifying an initial pallet height defined by the first object; identifying a second pallet height defined by a second object of the plurality of palletized objects; detecting a pallet height difference based at least in part on the second pallet height; and determining that the top pallet layer has been depalletized based at least in part on a comparison of the pallet height difference to a threshold pallet layer height.

Abstract: Methods, apparatuses, systems, computing devices, and/or the like are provided. An example method may include detecting a first set of objects, the first set of objects including two or more palletized objects of a plurality of palletized objects in a stack, wherein the two or more palletized objects of the first set of objects are disposed above a threshold height, wherein the stack has a front side and a back side; determining that a first object of the first set is closer than a second object of the first set is to the front side, wherein the first object of the first set is disposed lower than the second object of the first set; and manipulating an end effector to remove the first object from the stack before removing the second object from the stack and to deposit the first object in one or more deposit locations.

Abstract: Methods, apparatuses, systems, and computer program products for an improved anti-sway control system and adjustable end effector for a robotic arm are provided. An example method includes determining at least one of a size, shape, or orientation of a package to be picked up by an end effector of a robotic arm, adjusting a position of a suction cup on the end effector to grasp the package by linearly moving the suction cup from an initial position on a rail associated with the end effector to a predetermined end position on the rail associated with the end effector, determining a path for the robotic arm to move to the predetermined end position; and controlling movement of the end effector via a robotic joint to reduce force on the suction cup by the package due to an acceleration of the package due to movement of the robotic arm.

Abstract: Methods, apparatuses, systems, and computer program products for an improved anti-sway control system and adjustable end effector for a robotic arm are provided. An example method includes determining at least one of a size, shape or orientation of a package to be picked up by an end effector of a robotic arm and facilitating adjusting a position of a suction cup on the end effector, wherein the position is determined based on the at least one determined size, shape, or orientation of the package. The method further includes facilitating grasping the package with the end effector and facilitating movement of the end effector via a robotic joint to reduce force on the suction cup by the package due to an acceleration of the package due to movement of the robotic arm.

Abstract: Various embodiments described herein relate to techniques for reinforcement learning based conveyoring control. In this regard, a conveyor system is configured to transport one or more objects via a conveyor belt. Furthermore, a vision system comprises one or more sensors configured to scan the one or more objects associated with the conveyor system. A processing device is configured to employ a machine learning model to determine object pose data associated with the one or more objects. The processing device is further configured to generate speed control data for the conveyor belt of the conveyor system based on a set of control policies associated with the object pose data.

Abstract: Methods, apparatuses, systems, computing devices, and/or the like are provided. An example method may include receiving, from a perception subsystem associated with an object depalletization system, first imaging data associated with a plurality of objects disposed on a pallet; calculate, based at least in part on the first imaging data, one or more comparative dimension measures associated with the plurality of objects; determine whether the one or more comparative dimension measures satisfy a comparative dimension threshold range; and in response to determining that the one or more comparative dimension measures satisfy the comparative dimension threshold range, cause an execution subsystem associated with the object depalletization system to operate in a constant pallet mode.

Abstract: 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.

Abstract: A truck unloader for unloading cartons from a carton pile includes a controller with a detection system configured to periodically receive one or more images from a vision system and a processor subsystem in connection with the detection system. The processor subsystem configured to calculate a distance from at least a portion of the truck unloader to the carton pile based on the one or more images and advance the truck unloader to perform a sweeping operation based on the distance in an instance in which the distance satisfies a predefined distance threshold. The processor subsystem further configured to monitor a torque input received as a feedback from the one or more sensors while performing the sweeping operation, wherein the sweeping operation is configured to continue until the torque input exceeds a predefined torque threshold.

Abstract: A method for controlling a robotic item handler is described. The method includes obtaining first point cloud data related to a first three-dimensional (3D) image and second point cloud data related to a second 3D image, captured by a first sensor device and a second sensor device respectively. Further, the method can include transforming the first point cloud data and the second point cloud data to combined point cloud data that is used as an input to a convolutional neural network, to construct a machine learning model. The machine learning model can output a decision classification indicative of a first probability associated with a first operating mode and a second probability associated with a second operating mode. Furthermore, the method can include operating the robotic item handler according to the first operating mode or the second operating mode based on a comparison of the first probability and the second probability.

Abstract: Various embodiments described herein relate to techniques for reinforcement learning based conveyoring control. In this regard, a conveyor system is configured to transport one or more objects via a conveyor belt. Furthermore, a vision system comprises one or more sensors configured to scan the one or more objects associated with the conveyor system. A processing device is configured to employ a machine learning model to determine object pose data associated with the one or more objects. The processing device is further configured to generate speed control data for the conveyor belt of the conveyor system based on a set of control policies associated with the object pose data.

Abstract: Methods, apparatuses, systems, and computer program products for an improved anti-sway control system and adjustable end effector for a robotic arm are provided. An example method includes determining at least one of a size, shape or orientation of a package to be picked up by an end effector of a robotic arm and facilitating adjusting a position of a suction cup on the end effector, wherein the position is determined based on the at least one determined size, shape, or orientation of the package. The method further includes facilitating grasping the package with the end effector and facilitating movement of the end effector via a robotic joint to reduce force on the suction cup by the package due to an acceleration of the package due to movement of the robotic arm.