A first characteristic portion of a first workpiece and a second characteristic portion of a second workpiece are previously determined. A characteristic amount detection unit detects a first characteristic amount related to the position of the first characteristic portion and a second characteristic amount related to the position of the second characteristic portion in an image captured by a camera. A calculation unit calculates, as a relative position amount, the difference between the first characteristic amount and the second characteristic amount. A command generation unit generates a movement command for operating a robot based on a relative position amount in the image captured by the camera and a relative position amount in a predetermined reference image.

A mobile assembly apparatus and a method of assembling a retail product utilizing a retail assembly are discussed. The mobile assembly apparatus may include a cabinet structure, an expandable work surface, a frame, wheels, an articulated arm, outrigger supports, a power supply, sensors, a hardware dispenser, and a computing device. The mobile assembly apparatus scans a machine readable identifier and retrieves assembly instructions for an item associated with the identifier. The computing device controls the outrigger supports and extendable surface based on the instructions. The computing device monitors the progress of the assembly of the time and validates the assembly based on assembly data from the sensors.

A server assembly service determines configurations for custom assembled servers based on time-series utilization metadata for servers executing workloads similar to workloads that are to be executed on the custom assembled servers. The server assembly service determines trends in the time-series utilization metadata and compares the identified trends to associations between workload utilization trends and application classes to determine one or more application classes for applications executing the workloads. The service uses the determined application classes to select server configurations for custom servers that are to be assembled to execute workloads similar to the workloads related to the server utilization metadata. In some embodiments, the service selects custom server configurations without access to applications or application data for workloads of concern. For example, the service may select custom server configurations without using profiling techniques that may intrude on customer privacy by requiring access to underlying applications or application data.

It is an object of the present invention to obtain a work determination apparatus capable of preventing or reducing a time lag between the occurrence and determination of an anomaly in work. A work determination apparatus (30) includes: a sensor data output unit (31) that outputs sensor data with an output interval of an integral multiple of a control period with which a motion of a robot (1) is controlled, the sensor data representing a state of work of the robot (1); and a determination unit (32) that determines a quality of the work of the robot (1) by inference using a recurrent neural network, based on the sensor data outputted from the sensor data output unit (31).

Systems and methods for automated manufacture are provided. User input is received by way of user systems indicating nominal data measurements for an article. Automated material handling machines move parts within view of a machine vision system which performs an initial scan to identify features of said parts. Locations of areas for joining are determined by comparing the identified features to the nominal data measurements and the automated material handling machines move the parts into positions in accordance with the nominal data measurements to form the article. The automated material joining machines join the parts at said areas specified in said user input to form the article.

A robotic system is provided for assembling parts together. In the assembly process, both parts are moving separately with one part moving on an assembly base and another part moving on a moveable arm of a robot base. Motion data is measured by an inertial measurement (IMU) sensor. Movement of the robot base or moveable arm is then compensated based on the measured motion to align the first and second parts with each other and assemble the parts together.

A robotic assembly operation is provided for assembling a second part to a first part. During setup of the assembly operation, control parameters and a control scheme are set and changed by simulating the operation and testing whether performance requirements are met. A dry run may be performed thereafter, and test data may be collected after running the simulation to determine if the performance requirements are satisfied during the dry run. During production, production data may also be collected and control parameters may be tuned when changes occur during production in order to maintain stable assembly.

A method for controlling a robot to perform a task. The method includes acquiring, for each target of a sequence of targets comprising at least one intermediate target of the task and a final target of a task, a target image data element comprising at least one target image from a perspective of an end-effector of the robot at a respective target position of the robot and successively according to the sequence of targets, for each target in the sequence, acquiring, for the target, an origin image data element, supplying the origin image data element and the target image data to a machine learning model configured to derive a delta movement between the origin current position and the target position and controlling the robot to move according to the delta movement.

An improvement in reliability of manufacture of an assembly including a tap is achieved. A control section of a manufacturing system for manufacturing an assembly including a tap causes a robot to perform: a step of producing a first assembly by gripping a cap having an opening and engaging the cap with a collet having a recess such that the cap is placed on the collet; a step of producing a second assembly by gripping the first assembly and inserting the first assembly into a recess of a tap holder; and a step of producing a third assembly, which is an assembly including a tap, by gripping the tap and inserting the tap through the opening of the cap of the second assembly into the recess of the collet.

A method for controlling a robot to perform a task. The method includes acquiring a target image data element comprising at least one target image from a perspective of an end-effector of the robot at a target position of the robot in which the robot has performed the task, acquiring an origin image data element comprising at least one origin image from the perspective of the end-effector of the robot at an origin position of the robot, supplying the origin image data element and the target image data element to a machine learning model configured to derive a delta movement between the origin current position and the target position and controlling the robot to move according to the delta movement to perform the task.