Disclosed herein is a method implemented by a task mining engine. The task mining engine is stored as processor executable code on a memory. The processor executable code is executed by a processor that is communicatively coupled to the memory. The method includes receiving recorded tasks identifying user activity with respect to a computing environment and clustering the recorded user tasks into steps by processing and scoring each recorded user task. The method also includes extracting step sequences that identify similar combinations or repeated combinations of the steps to mimic the user activity.

The invention relates to a method for programming a robot, in particular a robot comprising a robotic arm, in which method a movement to be performed by the robot is set up preferably in a robot programme by means of a predefined motion template, the motion template is selected from a database comprising a plurality of motion templates, the motion template comprises one or more execution modules that can be parameterized and at least one learning module, the one or more execution modules are used for planning and/or performing the robot movement or part of the robot movement, the leaning module records one or more configurations of the robot during an initialization process, in particular in the form of a teaching process, and the learning module calculates parameters for the one or more execution modules on the basis of the recorded configurations, preferably using an automatic learning process. Also disclosed is a corresponding system for programming a robot.

A method for controlling a robotic device, in which a composite robot trajectory model made up of robot trajectory models of the movement skills is generated for a sequence plan for a task to be carried out by the robot including a sequence of movement skills and primitive actions to be carried out, and the robot is controlled, if after one movement skill according to the sequence plan one or multiple primitive action(s) is/are to be executed before the next movement skill, by interrupting the control of the robot according to the composite robot trajectory model after carrying out the movement skill, and by executing the one or multiple primitive action(s) and then resuming the control of the robot according to the composite robot trajectory model.

A robot system includes: at least one non-learned robot that has not learned a learning compensation amount of position control based on an operation command; at least one learned robot that has learned the learning compensation amount of the position control based on the operation command; and a storage device that stores the operation command and the learning compensation amount of the learned robot, the non-learned robot comprising a compensation amount estimation unit that compensates the learning compensation amount of the learned robot stored in the storage device based on a difference between the operation command of the learned robot stored in the storage device and an operation command of an own robot, and estimates the compensated learning compensation amount as a learning compensation amount of the own robot.

A control device repeats learning of: calculating an allowable condition for speed variations during a processing operation based on an allowable condition for processing error; setting an operating speed change rate used to increase or reduce an operating speed of a robot mechanism unit using a calculated allowable condition for speed variations; and, while increasing or reducing the operating speed change rate over a plurality of repetitions within a range not exceeding a maximum value of the operating speed change rate and within a range of an allowable condition for vibrations occurring in a control target, calculates a new correction amount based on an amount of difference between a position of the control target detected based on a sensor and a target position, and a previously-calculated correction amount.

Apparatus and methods for training and operating of robotic devices. Robotic controller may comprise a predictor apparatus configured to generate motor control output. The predictor may be operable in accordance with a learning process based on a teaching signal comprising the control output. An adaptive controller block may provide control output that may be combined with the predicted control output. The predictor learning process may be configured to learn the combined control signal. Predictor training may comprise a plurality of trials. During initial trial, the control output may be capable of causing a robot to perform a task. During intermediate trials, individual contributions from the controller block and the predictor may be inadequate for the task. Upon learning, the control knowledge may be transferred to the predictor so as to enable task execution in absence of subsequent inputs from the controller. Control output and/or predictor output may comprise multi-channel signals.

Methods and apparatus related to training and/or utilizing a convolutional neural network to generate grasping parameters for an object. The grasping parameters can be used by a robot control system to enable the robot control system to position a robot grasping end effector to grasp the object. The trained convolutional neural network provides a direct regression from image data to grasping parameters. For example, the convolutional neural network may be trained to enable generation of grasping parameters in a single regression through the convolutional neural network. In some implementations, the grasping parameters may define at least: a reference point for positioning the grasping end effector for the grasp; and an orientation of the grasping end effector for the grasp.

Apparatus and methods for training and controlling of, for instance, robotic devices. In one implementation, a robot may be trained by a user using supervised learning. The user may be unable to control all degrees of freedom of the robot simultaneously. The user may interface to the robot via a control apparatus configured to select and operate a subset of the robot's complement of actuators. The robot may comprise an adaptive controller comprising a neuron network. The adaptive controller may be configured to generate actuator control commands based on the user input and output of the learning process. Training of the adaptive controller may comprise partial set training. The user may train the adaptive controller to operate first actuator subset. Subsequent to learning to operate the first subset, the adaptive controller may be trained to operate another subset of degrees of freedom based on user input via the control apparatus.

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.

Apparatus and methods for training and operating of robotic devices. Robotic controller may comprise a predictor apparatus configured to generate motor control output. The predictor may be operable in accordance with a learning process based on a teaching signal comprising the control output. An adaptive controller block may provide control output that may be combined with the predicted control output. The predictor learning process may be configured to learn the combined control signal. Predictor training may comprise a plurality of trials. During initial trial, the control output may be capable of causing a robot to perform a task. During intermediate trials, individual contributions from the controller block and the predictor may be inadequate for the task. Upon learning, the control knowledge may be transferred to the predictor so as to enable task execution in absence of subsequent inputs from the controller. Control output and/or predictor output may comprise multi-channel signals.