Disclosed is a robot and a method for controlling a robot. The robot according to an embodiment of the present disclosure may include an end-effector configured to grip a tool, a tactile sensor disposed in the end-effector, the tactile sensor configured to generate tactile information about an identifier formed on the tool, and a processor configured to cause the end-effector to grip the tool, and determine at least one of a type or a posture of the tool gripped by the end-effector based on the tactile information received from the tactile sensor. Embodiments of the present disclosure may be implemented by executing an artificial intelligence algorithm and/or machine learning algorithm in a 5G environment connected for the Internet of Things.

A method for calculating the reliability of the thermal error model of a machine tool based on deep neural network (DNN) and the Monte Carlo method, which belongs to the field of the thermal error compensation of computer numerical control (CNC) machine tools. Firstly, according to the probability distribution of the thermal parameters and thermal error model, a set of data for training the DNN is generated. Next, the DNN is constructed based on the deep belief networks (DBNs) and trained with the training data. Then, a group of random sampling data is obtained according to the probability distribution of the thermal characteristic parameters of the machine tool, and the group of random sampling is taken as the input and the output is obtained by the trained depth neural network. Finally, the reliability of the thermal error model is calculated based on the Monte Carlo method.

An operation control device for a robot comprises: an input part inputting at least one operation candidate, and a captured image including an object to be processed; a first learning device that has finished learning performed according to first learning data to output a first evaluation value indicating evaluation of each operation candidate when the robot performs a first processing operation upon input of the captured image and the operation candidate; a second learning device that has finished learning performed according to second learning data which differs from the first learning data, to output a second evaluation value indicating evaluation of each operation candidate when the robot performs a second processing operation upon input of the captured image and the operation candidate; and an evaluation part that, based on at least one of the first evaluation value and the second evaluation value, calculates a command value.

A robot system according to an embodiment includes one or more processors. The processors acquire first input data predetermined as data affecting an operation of a robot. The processors calculate a calculation cost of inference processing using a machine learning model for inferring control data used for controlling the robot, on the basis of the first input data. The processors infer the control data by the machine learning model set according to the calculation cost. The processors control the robot using the inferred control data.

A robot operable within a 3-D volume includes a gripper movable between an open position and a closed position to grasp any one of a plurality of objects, an articulatable portion coupled to the gripper and operable to move the gripper to a desired position within the 3-D volume, and an object detection system operable to capture information indicative of the shape of a first object of the plurality of objects positioned to be grasped by the gripper. A computer is coupled to the object detection system. The computer is operable to identify a plurality of possible grasp locations on the first object and to generate a numerical parameter indicative of the desirability of each grasp location, wherein the numerical parameter is at least partially defined by the next task to be performed by the robot.

In some aspects, a system comprises a computer hardware processor and a non-transitory computer-readable storage medium storing processor-executable instructions for receiving, from one or more sensors, sensor data relating to a robot; generating, using a statistical model, based on the sensor data, first control information for the robot to accomplish a task; transmitting, to the robot, the first control information for execution of the task; and receiving, from the robot, a result of execution of the task.

In some aspects, a system comprises a computer hardware processor and a non-transitory computer-readable storage medium storing processor-executable instructions for receiving, from one or more sensors, sensor data relating to a robot; generating, using a statistical model, based on the sensor data, first control information for the robot to accomplish a task; transmitting, to the robot, the first control information for execution of the task; and receiving, from the robot, a result of execution of the task.

A multi-scale inspection and intelligent diagnosis system and method for tunnel structural defects includes: a traveling section; a supporting section, disposed on the traveling section, and including a rotatable telescopic platform, where two mechanical arms working in parallel are disposed on the rotatable telescopic platform; an inspection section, mounted on the supporting section, and configured to perform multi-scale inspection on surface defects and internal defects in different depth ranges of a same position of a tunnel structure, and transmit inspected defect information to a control section; and the control section, configured to: construct a deep neural network-based defect diagnosis model; construct a data set by using historical surface defect and internal defect information, and train the deep neural network-based defect diagnosis model; and receive multi-scale inspection information in real time, and automatically recognize types, positions, contours, and dielectric attributes of the internal and surface defects.

To select a picking position of a workpiece in a simpler method. A robot system includes a three-dimensional measuring device for generating a range image of a plurality of workpieces, a robot having a hand for picking up at least one of the plurality of workpieces, a display part for displaying the range image generated by the three-dimensional measuring device, and a reception part for receiving a teaching of a picking position for picking-up by the hand on the displayed range image. The robot picks up at least one of the plurality of workpieces by the hand on the basis of the taught picking position.

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.