Systems and methods for performing a task in an operation environment of an aerial device with an autonomous or semi-autonomous robot are described. In some embodiments, a robot is disposed at an end of a boom of an aerial device. The robot may comprise cameras, actuators, sensors, processors, and manipulators that work together to perform tasks fully autonomously or semi-autonomously. Furthermore, the robot may comprise tools for performing the tasks and computer-executable instructions for performing the tasks may be based on the various sensory inputs, the tools, and the tasks to be performed.

A method for training a machine learning model for generating descriptor images for images of one or of multiple objects. The method includes: formation of pairs of images which show the one or the multiple objects from different perspectives; generation, for each image pair, using the machine learning model, of a first descriptor image for the first image, and of a second descriptor image for the second image, which assigns descriptors to points of the one or multiple objects shown in the second image; sampling, for each image pair, of descriptor pairs, which include in each case a first descriptor from the first descriptor image and a second descriptor from the second descriptor image, which are assigned to the same point, and the adaptation of the machine learning method for reducing a loss.

Systems, computer program products, and methods are described herein for intelligent control code update for robotic process automation. The present invention is configured to retrieve execution logs associated with robotic process automation (RPA) sessions, wherein the execution logs comprises exceptions. Next, the present invention is configured to initiate machine learning algorithms configured to process the one or more execution logs and classify the exceptions into predetermined classes. Next, the present invention is configured to deploy automated exception handling subroutines to address the exceptions based on at least classifying the exceptions into the predetermined classes.

Systems, computer program products, and methods are described herein for intelligent control code update for robotic process automation. The present invention is configured to electronically retrieve one or more execution logs associated with one or more robotic process automation (RPA) sessions, wherein the one or more execution logs comprises one or more exceptions; initiate one or more machine learning algorithms configured to process the one or more execution logs; classify the one or more exceptions into one or more predetermined classes based on at least initiating the one or more machine learning algorithms on the one or more execution logs; and deploy one or more automated exception handling subroutines to address the one or more exceptions based on at least classifying the one or more exceptions into the one or more predetermined classes.

A workpiece picking device includes a sensor measuring a plurality of workpieces randomly piled in a three-dimensional space; a robot folding the workpieces; a hand mounted to the robot and hold the workpieces; a holding position posture calculation unit calculating holding position posture data of a position and a posture to hold the workpieces by the robot based on an output of the sensor; a loading state improvement operation generation unit generating loading state improvement operation data of improving a loading state of the workpieces by the robot based on an output of the sensor; and a robot control unit controlling the robot and the hand. The robot control unit controls the robot and the hand based on an output of the holding position posture calculation unit and the loading state improvement operation generation unit to pick the workpieces or perform a loading state improvement operation.

A workpiece picking device includes a sensor measuring a plurality of workpieces randomly piled in a three-dimensional space; a robot folding the workpieces; a hand mounted to the robot and hold the workpieces; a holding position posture calculation unit calculating holding position posture data of a position and a posture to hold the workpieces by the robot based on an output of the sensor; a loading state improvement operation generation unit generating loading state improvement operation data of improving a loading state of the workpieces by the robot based on an output of the sensor; and a robot control unit controlling the robot and the hand. The robot control unit controls the robot and the hand based on an output of the holding position posture calculation unit and the loading state improvement operation generation unit to pick the workpieces or perform a loading state improvement operation.

Systems and methods for performing a task in an operation environment of an aerial device with an autonomous or semi-autonomous robot are described. In some embodiments, a robot is disposed at an end of a boom of an aerial device. The robot may comprise cameras, actuators, sensors, processors, and manipulators that work together to perform tasks fully autonomously or semi-autonomously. Furthermore, the robot may comprise tools for performing the tasks and computer-executable instructions for performing the tasks may be based on the various sensory inputs, the tools, and the tasks to be performed.

Systems and methods for performing a task in an operation environment of an aerial device with an autonomous or semi-autonomous robot are described. In some embodiments, a robot is disposed at an end of a boom of an aerial device. The robot may comprise cameras, actuators, sensors, processors, and manipulators that work together to perform tasks fully autonomously or semi-autonomously. Furthermore, the robot may comprise tools for performing the tasks and computer-executable instructions for performing the tasks may be based on the various sensory inputs, the tools, and the tasks to be performed.