A software compensated robotic system makes use of recurrent neural networks and image processing to control operation and/or movement of an end effector. Images are used to compensate for variations in the response of the robotic system to command signals. This compensation allows for the use of components having lower reproducibility, precision and/or accuracy that would otherwise be practical.

A machine learning device for learning a threshold value of detecting an abnormal load in a machine tool, includes a state observation unit, and a learning unit. The state observation unit observes a state variable obtained based on at least one of information about a tool, main spindle revolution rate, and amount of coolant of the machine tool, material of a workpiece, and moving direction, cutting speed, and cut depth of the tool, and the learning unit learns the threshold value of detecting an abnormal load based on training data created from an output of the state observation unit and data related to detection of an abnormal load in the machine tool and on teacher data.

A software compensated robotic system makes use of recurrent neural networks and image processing to control operation and/or movement of an end effector. Images are used to compensate for variations in the response of the robotic system to command signals. This compensation allows for the use of components having lower reproducibility, precision and/or accuracy that would otherwise be practical.

A software compensated robotic system makes use of recurrent neural networks and image processing to control operation and/or movement of an end effector. Images are used to compensate for variations in the response of the robotic system to command signals. This compensation allows for the use of components having lower reproducibility, precision and/or accuracy that would otherwise be practical.

A software compensated robotic system makes use of recurrent neural networks and image processing to control operation and/or movement of an end effector. Images are used to compensate for variations in the response of the robotic system to command signals. This compensation allows for the use of components having lower reproducibility, precision and/or accuracy that would otherwise be practical.

A machine learning device for learning a threshold value of detecting an abnormal load in a machine tool, includes a state observation unit, and a learning unit. The state observation unit observes a state variable obtained based on at least one of information about a tool, main spindle revolution rate, and amount of coolant of the machine tool, material of a workpiece, and moving direction, cutting speed, and cut depth of the tool, and the learning unit learns the threshold value of detecting an abnormal load based on training data created from an output of the state observation unit and data related to detection of an abnormal load in the machine tool and on teacher data.

A servo control system includes a position command generator, a position detector for a feed axis, a positional deviation obtainer for calculating a positional deviation, a position control loop, a band limiting filter for attenuating a high frequency component of the positional deviation, a dynamic characteristic compensation element for advancing a phase, a learning controller including the band limiting filter and the dynamic characteristic compensation element, a sine wave sweep input unit for applying a sine wave sweep to the position control loop, a frequency characteristic calculator for estimating the gain and phase of position control loop input and output signals, and a learning control characteristic evaluation function calculator for calculating an evaluation function, which indicates a position control characteristic with the learning controller based on a frequency characteristic by actual measurement and a frequency characteristic of the learning controller.

A robot apparatus includes a robot mechanism; a sensor provided in a portion whose position is to be controlled, of the robot mechanism, for detecting a physical quantity to obtain positional information of the portion; and a robot controller having an operation control unit for controlling the operation of the robot mechanism. The robot controller includes a learning control unit for calculating a learning correction value to improve a specific operation of the robot mechanism based on the physical quantity detected, while the operation control unit makes the robot mechanism perform the specific operation, with the sensor; and a learning extension unit for obtaining the relationship between the learning correction value calculated by the learning control unit and information about the learned specific operation, and calculates another learning correction value to improve a new operation by applying the obtained relationship to information about the new operation without sensor.

A robot apparatus includes a robot mechanism; a sensor provided in a portion whose position is to be controlled, of the robot mechanism, for detecting a physical quantity to obtain positional information of the portion; and a robot controller having an operation control unit for controlling the operation of the robot mechanism. The robot controller includes a learning control unit for calculating a learning correction value to improve a specific operation of the robot mechanism based on the physical quantity detected, while the operation control unit makes the robot mechanism perform the specific operation, with the sensor; and a learning extension unit for obtaining the relationship between the learning correction value calculated by the learning control unit and information about the learned specific operation, and calculates another learning correction value to improve a new operation by applying the obtained relationship to information about the new operation without sensor.

A servo control system includes a position command generator, a position detector for a feed axis, a positional deviation obtainer for calculating a positional deviation, a position control loop, a band limiting filter for attenuating a high frequency component of the positional deviation, a dynamic characteristic compensation element for advancing a phase, a learning controller including the band limiting filter and the dynamic characteristic compensation element, a sine wave sweep input unit for applying a sine wave sweep to the position control loop, a frequency characteristic calculator for estimating the gain and phase of position control loop input and output signals, and a learning control characteristic evaluation function calculator for calculating an evaluation function, which indicates a position control characteristic with the learning controller based on a frequency characteristic by actual measurement and a frequency characteristic of the learning controller.