Implementations described herein relate to training and refining robotic control policies using imitation learning techniques. A robotic control policy can be initially trained based on human demonstrations of various robotic tasks. Further, the robotic control policy can be refined based on human interventions while a robot is performing a robotic task. In some implementations, the robotic control policy may determine whether the robot will fail in performance of the robotic task, and prompt a human to intervene in performance of the robotic task. In additional or alternative implementations, a representation of the sequence of actions can be visually rendered for presentation to the human can proactively intervene in performance of the robotic task.

Implementations described herein relate to training and refining robotic control policies using imitation learning techniques. A robotic control policy can be initially trained based on human demonstrations of various robotic tasks. Further, the robotic control policy can be refined based on human interventions while a robot is performing a robotic task. In some implementations, the robotic control policy may determine whether the robot will fail in performance of the robotic task, and prompt a human to intervene in performance of the robotic task. In additional or alternative implementations, a representation of the sequence of actions can be visually rendered for presentation to the human can proactively intervene in performance of the robotic task.

An image recognition method includes obtaining measurement data of a target object, comparing a 3D model having a plurality of feature points and the measurement data and updating importance degrees of the plurality of feature points based on differences between the 3D model and the measurement data, performing learning using the updated importance degrees, and performing object recognition for the target object based on a result of the learning.

A control system for a robotic device comprising a task embedding network to receive one or more demonstrations of a task and to generate a task embedding. The task embedding comprises a representation of the task, and each demonstration comprises one or more observations of a performance of the task. The control system includes a control network to receive the task embedding from the task embedding network and to apply a policy to map a plurality of successive observations of the robotic device to respective control instructions for the robotic device. The policy applied by the control network is modulated across the plurality of successive observations of the robotic device using the task embedding from the task embedding network.

A robot control system includes: an interface configured to receive a user input; a controller configured to generate a motion command corresponding to the user input and a motion command group including the motion command, and generate hardware interpretable data by analyzing the motion command; and a driver configured to drive a motion of at least one hardware module based on the hardware interpretable data to be interpreted by the at least one hardware module.

A servo control system for controlling a plurality of axes of a machine tool, comprises: a plurality of servo control units for controlling the plurality of axes, respectively; a plurality of learning control units that are provided one each in the plurality of servo control units, and each configured to control a cyclic operation highly precisely; a common learning memory for storing correction data which at least a portion of the plurality of learning control units generates; a memory allocation unit for allocating at least a portion of a memory area in the learning memory to the axis that the learning control unit that generated the correction data controls; and a memory amount notifying unit for notifying the memory allocation unit as to the amount of memory that each of the plurality of learning control units of the respective axes requires.

The invention relates to a system for controlling actuators of an articulated robot and for enabling the robot executing a given task, including a first unit providing a specification of robot skills s selectable from a skill space depending on the task, a second unit, wherein the second unit is connected to the first unit and further to a learning unit and to an adaptive controller, wherein the adaptive controller receives skill commands .sub.cmd, wherein the skill commands .sub.cmd include the skill parameters P.sub.l, wherein based on the skill commands .sub.cmd the controller controls the actuators of the robot, wherein the actual status of the robot is sensed by respective sensors and/or estimated by respective estimators and fed back to the controller and to the second unit, wherein based on the actual status, the second unit determines the performance Q(t) of the skill carried out by the robot, and wherein the learning unit receives P.sub.D, and Q(t) from the second unit, determines updated skill parameters P.sub.l(t) and provides P.sub.l(t) to the second unit to replace hitherto existing skill parameters P.sub.l.

A control system for a robotic device comprising a task embedding network to receive one or more demonstrations of a task and to generate a task embedding. The task embedding comprises a representation of the task, and each demonstration comprises one or more observations of a performance of the task. The control system includes a control network to receive the task embedding from the task embedding network and to apply a policy to map a plurality of successive observations of the robotic device to respective control instructions for the robotic device. The policy applied by the control network is modulated across the plurality of successive observations of the robotic device using the task embedding from the task embedding network.

A control device includes a processor that is configured to execute computer-executable instructions so as to control a robot, wherein the processor is configured to calculate an optical parameter related to an optical system imaging a target object, by using machine learning, detect the target object on the basis of an imaging result in the optical system by using the calculated optical parameter, and control a robot on the basis of a detection result of the target object.

A control device includes a processor that is configured to execute computer-executable instructions so as to control a robot, wherein the processor is configured to calculate an operation parameter related to an operation of a robot by using machine learning, and control the robot on the basis of the calculated operation parameter.