The present invention provides intelligent test robot system, which operation platform of intelligent robot body connected storage module for storing test script, operation platform respectively connected signal output unit and image input unit, further connected network communication module linked remote console computer. When operation platform automatically performs tests accordance test script, e.g., power switches, string inputs through keyboard, mouse cursor movements, clicks etc., it is possible to send instructions contained test script to test object via signal output unit for action controls, image input unit receives images outputted by test object and returns them to operation platform, then automatic graphic recognitions can be performed on captured images according to image recognition instructions in test script. Moreover, remote console computer may control plural intelligent robot bodies through Internet for synchronously executing automatic test flows so as to facilitate shortened test time and reduced costs thus further elevating the integral production efficiency.

The present invention relates to a method, such as a surgical method for assisting a surgeon for placing screws in the spine using a robot attached to a passive structure. The present invention also related to a method, such as a surgical method for assisting a surgeon for removing volumes in the body of a patient using a robot attached to a passive structure and to a device to carry out said methods. The present invention further concerns a device suitable to carry out the methods according to the present invention.

Methods, apparatus, and computer-readable media for determining and utilizing corrections to robot actions. Some implementations are directed to updating a local features model of a robot in response to determining a human correction of an action performed by the robot. The local features model is used to determine, based on an embedding generated over a corresponding neural network model, one or more features that are most similar to the generated embedding. Updating the local features model in response to a human correction can include updating a feature embedding, of the local features model, that corresponds to the human correction. Adjustment(s) to the features model can immediately improve robot performance without necessitating retraining of the corresponding neural network model.

A manipulator system configured to perform a work to a workpiece being moved by a moving device, includes a robotic arm, having one or more joints and to which a tool configured to perform the work to the workpiece is attached, an operating device configured to operate the robotic arm, a first imaging means configured to image the workpiece, while following the movement of the workpiece, a second imaging means fixedly provided in a work area to image a situation of the work to the workpiece, a displaying means configured to display an image imaged by the first imaging means and an image imaged by the second imaging means, and a control device configured to control the operation of the robotic arm based on an operating instruction of the operating device, while detecting a moving amount of the workpiece being moved by the moving device and carrying out a tracking control of the robotic arm according to the moving amount of the workpiece.

Apparatus and methods for training and controlling of e.g., robotic devices. In one implementation, a robot may be utilized to perform a target task characterized by a target trajectory. The robot may be trained by a user using supervised learning. The user may interface to the robot, such as via a control apparatus configured to provide a teaching signal to the robot. The robot may comprise an adaptive controller comprising a neuron network, which may be configured to generate actuator control commands based on the user input and output of the learning process. During one or more learning trials, the controller may be trained to navigate a portion of the target trajectory. Individual trajectory portions may be trained during separate training trials. Some portions may be associated with robot executing complex actions and may require additional training trials and/or more dense training input compared to simpler trajectory actions.

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.

Embodiments of the present disclosure are directed to methods, computer program products, and computer systems of a robotic apparatus with robotic instructions replicating a food preparation recipe. In one embodiment, a robotic control platform, comprises one or more sensors; a mechanical robotic structure including one or more end effectors, and one or more robotic arms; an electronic library database of minimanipulations; a robotic planning module configured for real-time planning and adjustment based at least in part on the sensor data received from the one or more sensors in an electronic multi-stage process file, the electronic multi-stage process recipe file including a sequence of minimanipulations and associated timing data; a robotic interpreter module configured for reading the minimanipulation steps from the minimanipulation library and converting to a machine code; and a robotic execution module configured for executing the minimanipulation steps by the robotic platform to accomplish a functional result.

Grasping of an object, by an end effector of a robot, based on a grasp strategy that is selected using one or more machine learning models. The grasp strategy utilized for a given grasp is one of a plurality of candidate grasp strategies. Each candidate grasp strategy defines a different group of one or more values that influence performance of a grasp attempt in a manner that is unique relative to the other grasp strategies. For example, value(s) of a grasp strategy can define a grasp direction for grasping the object (e.g., “top”, “side”), a grasp type for grasping the object (e.g., “pinch”, “power”), grasp force applied in grasping the object, pre-grasp manipulations to be performed on the object, and/or post-grasp manipulations to be performed on the object.

Robotic devices may be trained by a user guiding the robot along a target trajectory using a correction signal. A robotic device may comprise an adaptive controller configured to generate control commands based on one or more of the trainer input, sensory input, and/or performance measure. Training may comprise a plurality of trials. During an initial portion of a trial, the trainer may observe robot's operation and refrain from providing the training input to the robot. Upon observing a discrepancy between the target behavior and the actual behavior during the initial trial portion, the trainer may provide a teaching input (e.g., a correction signal) configured to affect robot's trajectory during subsequent trials. Upon completing a sufficient number of trials, the robot may be capable of navigating the trajectory in absence of the training input.

The present invention relates to a method, such as a surgical method for assisting a surgeon for placing screws in the spine using a robot attached to a passive structure. The present invention also related to a method, such as a surgical method for assisting a surgeon for removing volumes in the body of a patient using a robot attached to a passive structure and to a device to carry out said methods. The present invention further concerns a device suitable to carry out the methods according to the present invention.