Provided are a robot system, a robot control device, a control method, and a program which make it possible to more simply teach a robot action. The robot system comprises: a feature point teaching unit which causes a storage unit to store the position of a feature point that has been taught using lead-through; an input accepting unit which accepts the input of an angle value of a tool with respect to a workpiece W; a posture determining unit which determines the posture of the tool on the basis of the angle value of the tool; and a program generating unit which generates a robot program for a robot on the basis of the position of the feature point and the posture.

Technology disclosed herein includes a wearable data collection device for training robotic systems. In an implementation, a wearable data collection device includes a hand element configured to receive a user's hand, multiple finger elements extending from the hand element, and joints coupling the finger elements to the hand element. The finger elements are constrained to movements that match capabilities of a robotic counterpart device. Multiple sensors mounted on the device capture pressure, position, visual, proximity, and acoustic data during recording sessions. The device may integrate with position tracking technologies such as mobile devices or augmented reality headsets. Data collected through the wearable device serves as training input for a neural network that controls the robotic counterpart.

Technology disclosed herein includes a wearable data collection device for training robotic systems. In an implementation, a wearable data collection device includes a hand element configured to receive a user's hand, multiple finger elements extending from the hand element, and joints coupling the finger elements to the hand element. The finger elements are constrained to movements that match capabilities of a robotic counterpart device. Multiple sensors mounted on the device capture pressure, position, visual, proximity, and acoustic data during recording sessions. The device may integrate with position tracking technologies such as mobile devices or augmented reality headsets. Data collected through the wearable device serves as training input for a neural network that controls the robotic counterpart.

Technology disclosed herein includes a wearable data collection device for training robotic systems. In an implementation, a wearable data collection device includes a hand element configured to receive a user's hand, multiple finger elements extending from the hand element, and joints coupling the finger elements to the hand element. The finger elements are constrained to movements that match capabilities of a robotic counterpart device. Multiple sensors mounted on the device capture pressure, position, visual, proximity, and acoustic data during recording sessions. The device may integrate with position tracking technologies such as mobile devices or augmented reality headsets. Data collected through the wearable device serves as training input for a neural network that controls the robotic counterpart.

Technology disclosed herein includes a wearable data collection device for training robotic systems. In an implementation, a wearable data collection device includes a hand element configured to receive a user's hand, multiple finger elements extending from the hand element, and joints coupling the finger elements to the hand element. The finger elements are constrained to movements that match capabilities of a robotic counterpart device. Multiple sensors mounted on the device capture pressure, position, visual, proximity, and acoustic data during recording sessions. The device may integrate with position tracking technologies such as mobile devices or augmented reality headsets. Data collected through the wearable device serves as training input for a neural network that controls the robotic counterpart.

Technology disclosed herein includes a wearable data collection device for training robotic systems. In an implementation, a wearable data collection device includes a hand element configured to receive a user's hand, multiple finger elements extending from the hand element, and joints coupling the finger elements to the hand element. The finger elements are constrained to movements that match capabilities of a robotic counterpart device. Multiple sensors mounted on the device capture pressure, position, visual, proximity, and acoustic data during recording sessions. The device may integrate with position tracking technologies such as mobile devices or augmented reality headsets. Data collected through the wearable device serves as training input for a neural network that controls the robotic counterpart.

Technology disclosed herein includes a wearable data collection device for training robotic systems. In an implementation, a wearable data collection device includes a hand element configured to receive a user's hand, multiple finger elements extending from the hand element, and joints coupling the finger elements to the hand element. The finger elements are constrained to movements that match capabilities of a robotic counterpart device. Multiple sensors mounted on the device capture pressure, position, visual, proximity, and acoustic data during recording sessions. The device may integrate with position tracking technologies such as mobile devices or augmented reality headsets. Data collected through the wearable device serves as training input for a neural network that controls the robotic counterpart.

A machine for machining and/or measuring gearing, having two or more numerically controlled machine axes for carrying out machining movements and/or measuring movements, having a machine controller, wherein the machine controller has a control software, wherein the control software is set up for executing machine functions, such as executing program sequences for controlling the numerically controlled machine axes, the output of service reports or the like. The machine controller has a user interface set up for interaction with a machine operator, wherein for at least one of the machine functions it is predetermined by the control software that the execution of this machine function requires at least one manual input by a machine operator at the user interface. A bot software is assigned to the control software in order to replace the at least one manual input and to execute the machine function independently of a machine operator.

A machine for machining and/or measuring gearing, having two or more numerically controlled machine axes for carrying out machining movements and/or measuring movements, having a machine controller, wherein the machine controller has a control software, wherein the control software is set up for executing machine functions, such as executing program sequences for controlling the numerically controlled machine axes, the output of service reports or the like. The machine controller has a user interface set up for interaction with a machine operator, wherein for at least one of the machine functions it is predetermined by the control software that the execution of this machine function requires at least one manual input by a machine operator at the user interface. A bot software is assigned to the control software in order to replace the at least one manual input and to execute the machine function independently of a machine operator.

An exemplary method and system are disclosed to flexibly and adaptably manufacture and assemble a workpiece by using recordings of a user in machine learning/artificial intelligence algorithms to train a robot for subsequent automated manufacture. Machine learning and artificial intelligence learning can generate libraries of generalized dynamic motion primitives that can be subsequently combined for any type of manufacturing or assembling activity. The exemplary method and system can flexibly generate a model of an existing workpiece as a template or primer workpiece that can then be used in conjunction with the DMP operations to fabricate subsequent workpieces.