An electronic device is provided. The electronic device includes at least one processor and a memory. The memory stores instructions that, when executed, cause the at least one processor to identify a task corresponding to a task execution instruction acquired by an input device of the electronic device, identify user information corresponding to the task, identify a target spot of the electronic device for executing the task, based on the task and the user information, with respect to a position of a user corresponding to the identified user information, and control a driving circuit of the electronic device to move the electronic device to the identified target spot.

A robot for physical human-robot interaction may include a number of sensors, a processor, a controller, an actuator, and a joint. The sensors may receive a corresponding number of sensor measurements. The processor may reduce a dimensionality of the number of sensor measurements based on temporal sparsity associated with the number of sensors and spatial sparsity associated with the number of sensors and generate an updated sensor measurement dataset. The processor may receive an action associated with a human involved in pHRI with the robot. The processor may generate a response for the robot based on the updated sensor measurement dataset and the action. The controller may implement the response via an actuator within a joint of the robot.

A system for detection of an anomaly in a discrete manufacturing process (DMP) with human-robot teams executing a task. Receive signals including robot, worker and DMP signals. Predict a sequence of events (SOEs) from DMP signals. Determine whether the predicted SOEs in the DMP signals is inconsistent with a behavior of operation of the DMP described in a DMP model, and if the predicted SOEs from DMP signals is inconsistent with the behavior, then an alarm is to be signaled. Input worker data into a Human Performance (HP) model, to obtain a state of the worker based on previously learned boundaries of human state. The state of the HW is then input into the HRI model and the DMP model to determine a classification of anomaly or no anomaly. Update a Human-Robot Interaction (HRI) model to obtain a control action of a robot or a type of an anomaly alarm.

A robot for outputting various reactions according to user behaviors is disclosed. A control method for a robot using an artificial intelligence model, according to the present disclosure, comprises the steps of: acquiring data related to at least one user; inputting the data related to the at least one user into the artificial intelligence model as learning data so as to learn a user state for each user of which there is at least one; determining representative reactions corresponding to the user states learned on the basis of the data related to the at least one user; and inputting the input data into the artificial intelligence model so as to determine a user state of a first user and controlling the robot on the basis of a representative reaction corresponding to the determined user state, when input data related to the first user among the users, of which there is a least one, is acquired.

A system for detection of an anomaly in a discrete manufacturing process (DMP) with human-robot teams executing a task. Receive signals including robot, worker and DMP signals. Predict a sequence of events (SOEs) from DMP signals. Determine whether the predicted SOEs in the DMP signals is inconsistent with a behavior of operation of the DMP described in a DMP model, and if the predicted SOEs from DMP signals is inconsistent with the behavior, then an alarm is to be signaled. Input worker data into a Human Performance (HP) model, to obtain a state of the worker based on previously learned boundaries of human state. The state of the HW is then input into the HRI model and the DMP model to determine a classification of anomaly or no anomaly. Update a Human-Robot Interaction (HRI) model to obtain a control action of a robot or a type of an anomaly alarm.

A system for detecting an anomaly in an execution of a task in mixed human-robot processes. Receiving human worker (HW) signals and robot signals. A processor to extract from the HW signals, task information, measurements relating to a state of the HW, and input into a Human Performance (HP) model, to obtain a state of the HW based on previously learned boundaries of the state of the HW, the state of the HW is then inputted into a Human-Robot Interaction (HRI) model, to determine a classification of an anomaly or no anomaly. Update HRI model with robot operation signals, HW signals and classified anomaly, determine a control action of a robot interacting with the HW or a type of an anomaly alarm using the updated HRI model and classified anomaly. Output the control action of the robot to change a robot action or output the type of the anomaly alarm.

A robot includes an input detection portion, a motion detection portion, and a control portion. The input detection portion is configured to detect an input given from an operator to a robot body. The motion detection portion is configured to detect a motion by using the input detection portion, the motion being given by the operator. The control portion is configured to execute a motion instruction associated with the motion detected by the motion detection portion.

A remote control system includes: an imaging unit that shoots an environment in which a device to be operated including an end effector is located; an operation terminal having a function for displaying a shot image of the environment shot by the imaging unit and receiving handwritten input information input to the displayed shot image, and allowing a user to have a conversation with the device to be operated through a text chat; and an estimation unit that estimates an object to be grasped which has been requested to be grasped by the end effector and estimates a way of performing a grasping motion by the end effector based on the handwritten input information input to the shot image and a conversation history of the text chat, the grasping motion having been requested to be performed with regard to the object to be grasped.

A system and method of instructing a device is disclosed. The system includes a signal source for providing at least one visual signal where the at least one visual signal is substantially indicative of at least one activity to be performed by the device. A visual signal capturing element captures the at least one visual signal and communicates the at least one visual signal to the device where the device interprets the at least one visual signal and performs the activity autonomously and without requiring any additional signals or other information from the signal source.

A robot-training system permits a user touch, click on or otherwise select items from a display projected in the actual workspace in order to define task goals and constraints for the robot. A planning procedure responds to task definitions and constraints, and creates a sequence of robot instructions implementing the defined tasks.