A system and method is provided for determining grasping positions for two-handed grasps of industrial objects. The system may include a processor configured to determine a three dimensional (3D) voxel grid for a 3D model of a target object. In addition, the processor may be configured to determine at least one pair of spaced apart grasping positions on the target object at which the target object is capable of being grasped with two hands at the same time based on processing the 3D voxel grid for the target object with a neural network trained to determine grasping positions for two-handed grasps of target objects using training data. Such training data may include 3D voxel grids of a plurality of 3D models of training objects and grasping data including corresponding pairs of spaced-apart grasping positions for two-handed grasps of the training objects. Also, the processor may be configured to provide output data that specifies the determined grasping positions on the target object for two-handed grasps.

An apparatus is provided which includes a processing circuit and a plurality of sensors connected to a vehicle, where at least one of the plurality of sensors is positioned on an undercarriage of the vehicle. The plurality of sensors can detect variations in a road on which the vehicle is traveling. The plurality of sensors can also generate information corresponding to the variations of the road. The plurality of sensors can also transmit the information corresponding to the variations in the road to the processing circuit. The information collected by the plurality of sensors may then be used to augment a driving capability of the vehicle.

A method (1000) of diagnosis of operation of a machine tool (10, 100) that includes one or more axes (X, Y, Z) moved by one or more actuators (101, 102, 104) and at least one sensor (30) coupled to the machine tool (10, 100), the method (1000) comprising operations of: generating (1200) a programming sequence of movement of the axes (X, Y, Z) of the machine tool (10, 100); controlling (1210) the movement of the axes (X, Y, Z) of the machine tool (10, 100) according to the programming sequence; receiving (1220) a read-out signal (S) of the at least one sensor (30) coupled to the machine tool (10, 100); and processing (1230) the read-out signal (S) of the at least one sensor (30) coupled to the machine tool (10, 100). The programming sequence comprises instructions that are such as to apply (T) at least one single impulsive variation of a kinematic quantity that regards one or more actuators (101, 102, 104). The operation (1230) of processing the read-out signal (S) comprises processing a response of the machine tool (10, 100) to at least one single impulsive variation. The operation (1230) of processing the read-out signal (S) comprises artificial-neural-network processing (206) via one or more artificial neural networks (206, 2060) configured for analysing operating profiles in particular, one or more signals indicative of the status of the machine tool (W) in the read-out signal (S).

Robots and robotic systems and methods can employ artificial neural networks (ANNs) to significantly improve performance. The ANNs can operate alternatingly in forward and backward directions in interleaved fashion. The ANNs can employ visible units and hidden units. Various objective functions can be optimized. Robots and robotic systems and methods can execute applications including a plurality of agents in a distributed system, for instance with a number of hosts executing respective agents, at least some of the agents in communications with one another. The hosts can execute agents in response to occurrence of defined events or trigger expressions, and can operate with a maximum latency guarantee and/or data quality guarantee.

A processor controller includes: a deep neutral network, for extracting, based upon feature information of process control data, from a process control data storage device, process control data available to a production device to be controlled, the feature information of the process control data including at least production device feature parameters and a production device load; and an enhanced neural network, for performing, based upon a process control prediction model, process control prediction by using real-time process control data of said production device. In an embodiment, the process control prediction model is trained by using the extracted available process control data. The process controller further includes a process control decision unit, for determining an operation control instruction for the production device based upon the result of process control prediction. As such, prediction accuracy and training efficiency of the process control prediction model of the process controller can be improved.

A controller for generating a control signal for a computer-controlled machine. A neural network may be applied to a current sensor signal, the neural network being configured to map the sensor signal to a raw control signal. A projection function may be applied to the raw control signal to obtain a stable control signal to control the computer-controllable machine.

Robots and robotic systems and methods can employ artificial neural networks (ANNs) to significantly improve performance. The ANNs can operate alternatingly in forward and backward directions in interleaved fashion. The ANNs can employ visible units and hidden units. Various objective functions can be optimized. Robots and robotic systems and methods can execute applications including a plurality of agents in a distributed system, for instance with a number of hosts executing respective agents, at least some of the agents in communications with one another. The hosts can execute agents in response to occurrence of defined events or trigger expressions, and can operate with a maximum latency guarantee and/or data quality guarantee.

The present disclosure provides action imitation method as well as a robot and a computer readable storage medium using the same. The method includes: collecting at least a two-dimensional image of a to-be-imitated object; obtaining two-dimensional coordinates of each key point of the to-be-imitated object in the two-dimensional image and a pairing relationship between the key points of the to-be-imitated object; converting the two-dimensional coordinates of the key points of the to-be-imitated object in the two-dimensional image into space three-dimensional coordinates corresponding to the key points of the to-be-imitated object through a pre-trained first neural network model, and generating an action control instruction of a robot based on the space three-dimensional coordinates corresponding to the key points of the to-be-imitated object and the pairing relationship between the key points, where the action control instruction is for controlling the robot to imitate an action of the to-be-imitated object.

A motor position compensation method for an ophthalmic surgical laser system employs a deep artificial neural network to characterize motor following errors of the motors of the system. The artificial neural network is trained using a large number of commanded motor positions and corresponding measured actual motor positions (measured by encoders associated with the motors) as training data, to obtain a trained artificial neural network that can predict the actual motor position for any commanded motor position. Before executing a treatment scan, the original commanded motor positions calculated from the intended scan pattern are inputted to the trained artificial neural network to predict the actual motor positions, and the predicted actual motor positions are used to adjust the original commanded motor positions. The adjusted commanded motor positions are then used to perform the treatment scan, which produces an actual scan pattern that more closely match the intended scan pattern.

A robotic arm system comprising an artificial intelligence (AI) processor system, a transceiver electrically coupled to the AI processor system, and a robotic arm having an optical data communication network that communicates with the transceiver. The robotic arm further comprises a transmitter, a plurality of sensors electrically coupled to the transmitter, a receiver, and a plurality of motion actuators electrically coupled to the receiver. The optical data communication network comprises gigabit plastic optical fiber (GbPOF) having a graded-index core made of a transparent carbon-hydrogen bond-free perfluorinated polymer with dopant. In one embodiment, one GbPOF optically couples the transmitter to the transceiver and another GbPOF optically couples the transceiver to the receiver. The flexible high-data-rate GbPOF enables robotic arm control using artificial intelligence.