A controlling method for an artificial intelligence moving robot according to an aspect of the present disclosure includes: checking nodes within a predetermined reference distance from a node corresponding to a current position; determining whether there is a correlation between the nodes within the reference distance and the node corresponding to the current position; determining whether the nodes within the reference distance are nodes of a previously learned map when there is no correlation; and registering the node corresponding to the current position on the map when the nodes within the reference distance are determined as nodes of the previously learned map, thereby being able to generate a map in which the environment of a traveling section and environmental changes are appropriately reflected.

A program identification method is for identifying an application program that is stored in a terminal device coupled to a robot system and that is used for teaching work on an operation of a robot provided in the robot system. The method includes: acquiring program information corresponding to the application program from the terminal device; and comparing the program information with first information stored in the robot system and thus identifying whether the application program is a first application program corresponding to the first information or not.

A robot controller includes: axis motor control units that control motors for driving axes of a robot; and an action command generation unit that generates a first action command having the shortest action time when the robot is moved from an action start point to an action goal point without considering an obstacle, and selects, from among the axes, a major axis having the longest action time when the action is performed in accordance with the first action command. The first action command includes another axis command, and a major axis command, and the action command generation unit adjusts the other axis command so as to reduce an action time according to the other axis command and outputs a second action command including the major axis command and the adjusted other axis command and corresponding to a first trajectory when determining that the first trajectory avoids a clash between the robot and the obstacle.

A method for controlling a robot device. The method includes acquiring an image(s) of in a workspace of the robot device; determining, by a neural network, object hierarchy information specifying stacking relations of the objects with respect to each other in the workspace of the robot device and confidence information for the object hierarchy information from the image(s); if the confidence information indicates a confidence above a confidence threshold, manipulating an object of the objects; if the confidence information indicates a confidence lower than the confidence threshold, acquiring an additional image of the objects and determining, by the neural network, additional object hierarchy information specifying stacking relations of the objects with respect to each other in the workspace of the robot device and additional confidence information for the additional object hierarchy information from the additional image and control the robot using the additional object hierarchy information.

There are provided systems, methods, and apparatus, for optimizing a policy controller to control a robotic agent that interacts with an environment to perform a robotic task. One of the methods includes optimizing the policy controller using a neural network that generates numeric embeddings of images of the environment and a demonstration sequence of demonstration images of another agent performing a version of the robotic task.

A robot is provided. The robot includes a microphone, a camera, a communication interface including a circuit, a memory storing at least one instruction, and a processor, wherein the processor is configured to acquire a user voice through the microphone, identify a task corresponding to the user voice, determine whether the robot can perform the identified task, and control the communication interface to transmit information on the identified task to an external robot based on the determination result.

An electronic device may receive log information regarding a motion of a wearable device from the wearable device, determine a value of at least one of one or more mobile parameters to be applied to a robot parameter algorithm for calculating a value of a robot parameter used to control the wearable device based on the log information, and determine the value of the robot parameter based on the robot parameter algorithm and the determined value of at least one of the mobile parameters.

Provided is a primary-and-secondary robot system including: a primary robot whose posture is changed by external force applied by a user; a secondary robot whose posture is controlled to be the same as the posture of the primary robot; and a control unit that is configured to control the primary robot and the secondary robot, the control unit causing the posture of the primary robot to be the same as the posture of the secondary robot, and limiting an acceleration rate of a movement of the primary robot to a limited acceleration rate or lower in causing the posture of the primary robot to be the same as the posture of the secondary robot.

A robot and an operation method thereof are disclosed. A robot may include a loading box provided to load goods, and to be movable at a certain distance with respect to the robot when closed and opened, a drive wheel configured to drive the robot, an auxiliary wheel provided at a position spaced apart from the drive wheel, and a variable supporter configured to change the position of the auxiliary wheel, and supporting the loading box, and the variable supporter may move the auxiliary wheel so as to correspond to the movement direction of the center of gravity of the robot. The robot may transmit and receive a wireless signal on the mobile communication network constructed according to a 5 Generation (G) communication.

Systems and methods for AI assisted reconfigurable, fixtureless manufacturing is disclosed. The invention eliminates geometry-setting tools (hard points, pins and nets—traditionally known as 3-2-1 fixturing schemes) and to replace the physical geometry setting with virtual datums driven by learning AI algorithms. A first type of part and a second type of part may be located by a machine vision system and moved by material handling devices and robots to locations within an assembly area. The parts may be aligned with one another and the alignment may be checked by the machine vision system which is configured to locate datums, in the form of features, of the parts and compare such datums to stored virtual datums. The parts may be joined while being held by the material handling devices or robots to form a subassembly in a fixtureless fashion. The material handling devices are able to grasp a number of different types of parts so that a number of different types of subassemblies are capable of being assembled. The system enables one skilled in the art to develop a product design with self-locating parts that will eliminate and minimize the need for geometry setting dedicated line tools and fixtures. This leads to the development of a manufacturing process that utilizes the industry 4.0 technologies to once again eliminate or significantly reduces the need for geometry setting line tools.