A determination device 1X mainly includes a proposition determination means 18X. The proposition determination means 18X performs a completion determination of a task based on a first proposition representing a current state of the task and a second proposition representing a completion state of the task, in which the first proposition and the second proposition are detected by a sensor, when an operation sequence concerning the task has completed or when a predetermined time length has lapsed from a start of the task.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing robot planning using a process definition graph. One of the methods includes receiving an initial underconstrained process definition graph for one or more robots, wherein the process definition graph is a directed acyclic graph having constraint nodes and action nodes. A plurality of transformers are repeatedly applied to the initial process definition graph, wherein each application of a transformer generates a respective modified process definition graph according to the constraint nodes of the process definition graph, wherein applying the plurality of transformers generates a schedule that specifies which of the one or more robots are to perform which of one or more actions represented by actions nodes according to constraints imposed by the constraint nodes in the process definition graph.

An update apparatus is provided. The update apparatus may comprise: a grouping unit configured to group a plurality of robots using a network; and an update unit configured to update the plurality of grouped robots through the network.

A first sensor is disposed in such a way that optical axes of a light emitting element and a light receiving element thereof are parallel to a left-right direction. A second sensor is disposed in such a way that optical axes of a light emitting element and a light receiving element thereof are parallel to a front-rear direction. When a position of a wafer to be loaded on a loading portion is taught, a position of the loading portion or a teaching jig to be loaded on the loading portion in the front-rear direction is detected by the first sensor by moving a hand in the front-rear direction by a moving mechanism, and a position of the loading portion or the teaching jig in the left-right direction is detected by the second sensor by moving the hand in the left-right direction by the moving mechanism.

Systems, methods and apparatus for automated vehicle wheel removal and replacement are provided. One system includes a computer system with applications for scheduling the replacement of tires for the vehicle. An electronically controlled lift device and robotic apparatus is configured for interaction with the computer system. The lift device mechanically adjusts arms for placement on lift points of vehicles. The robotic apparatus detects positioning of lug nut configuration for a wheel, removes lug nuts, and then removes the wheel from the wheel hub with gripping arms. The wheel and tire are then handed off to a separate tire changing machine. When a new tire is replaced the robotic apparatus then mounts the wheel to the original wheel hub, and then secures the lug nuts to the lug nut bolts.

An apparatus for estimating a proximity direction of an obstacle includes an acoustic transmitter attached to a surface of the apparatus; a first acoustic receiver spaced apart from the surface of the apparatus; a second acoustic receiver spaced apart from the surface of the apparatus; and at least one processor configured to: control the acoustic transmitter to generate an acoustic wave along the surface; obtain first and second proximity direction signals based on first and second acoustic wave signals corresponding to the generated acoustic wave; and estimate a proximity direction of the obstacle with respect to the apparatus based on the first and second proximity direction signals.

A method includes receiving, by a mobile computing device from an electroencephalogram (EEG) monitoring headset, an incoming wireless communication signal including an EEG data stream. The method may further include processing, by an application running on the mobile computing device, the received EEG data stream to determine at least one actionable command for at least one peripheral device. The method may also include transmitting, by the mobile computing device to the at least one peripheral device, at least one outgoing wireless communication signal including the at least one determined actionable command.

Systems, methods and apparatus for automated vehicle wheel removal and replacement are provided. One system includes a computer system with applications for scheduling the replacement of tires for the vehicle. An electronically controlled lift device and robotic apparatus is configured for interaction with the computer system. The lift device mechanically adjusts arms for placement on lift points of vehicles. The robotic apparatus detects positioning of lug nut configuration for a wheel, removes lug nuts, and then removes the wheel from the wheel hub with gripping arms. The wheel and tire are then handed off to a separate tire changing machine. When a new tire is replaced the robotic apparatus then mounts the wheel to the original wheel hub, and then secures the lug nuts to the lug nut bolts.

The invention relates to a method for determining optimized program parameters for a robot program, wherein the robot program is used to control a robot having a manipulator, preferably in a robot cell, comprising the steps: creating the robot program by means of a component-based graphical programming system on the basis of user inputs, wherein the robot program is formed from program components which are parameterizable via program parameters, and wherein initial program parameters are generated for the program components of the robot program; providing an interface for selecting one or more critical program components, wherein optimizable program parameters can be defined for the critical program components; carrying out an exploration phase for exploring a parameter range in relation to the optimizable program parameters, the robot program being carried out multiple times, the parameter range being scanned for the critical program components and trajectories of the robot being recorded such that training data are present for the critical program components; carrying out a learning phase in order to generate component representatives for the critical program components of the robot program on the basis of the training data collected in the exploration phase, wherein a component representative represents a system model which, in the form of a differentiable function, maps a specified state of the robot and specified program parameters to a predicted trajectory; carrying out an inference phase for determining optimized program parameters for the critical program components of the robot program, wherein optimizable program parameters of the component representative are iteratively optimized in respect of a specified target function by means of a gradient-based optimization method using the component representative. The invention furthermore relates to a corresponding system.

A robot system useful in manufacturing environments for diverse applications. The system is characterized by an elongate robot arm operable to selectively position an end effector. The robot arm is configured for movement by a CPU based control system and/or by physical manipulation by a human operator.