Disclosed herein is a handheld device for training at least one movement and at least one activity of a machine. The handheld device may include a handle, an input unit configured to input activation information for activating the training of the machine, an output unit configured to output the activation information for activating the training of the machine to a device external to the handheld device, and a coupling structure for releasably coupling an interchangeable attachment configured according to the at least one activity.

A method for controlling a robot (1) for handling a part to be handled (14), the handling robot (1) being linked to a control interface comprising a glove (40) comprising a first finger (41) provided with a first contact sensor (42) and a second finger (43) provided with a second contact sensor (44), the method comprising the following steps; a) associating, in a signal library (25), a first and a second recorded combination of signals (26, 21); b) acquiring a combination of signals originating from the sensors (26, 27) of the glove (40); c) comparing the acquired combination of signals with the recorded combinations (27, 28, 29) in the library (25); d) controlling the handling robot (1) in such a way as to perform a movement according to the velocity vector associated with the acquired combination of signals. A handling glove (40) and handling device implementing the method.

Provided is an off-line simulation system that enables performance of efficient vision correction training. This vision correction training system for vision correction training is provided with a head mount display capable of displaying an image in a virtual space, and a teaching device communicably connected to the head mount display. The teaching device has: a vision correction unit that, on the basis of a captured image captured by a camera after the position of a workpiece has been moved, performs vision correction on a predetermined movement; and a correction confirmation unit that confirms that the vision correction is appropriately performed on the predetermined movement.

The technology is directed to providing pick and place instructions to a robot. Sensor data including an image feed of a picking container in which at least one product is located may be output. An input indicating a selected product including at least one of the products located in the picking container may be received. A representation of the selected product and at least one image of the order container may be output for display. The representation of the product may be scaled relative to the at least one image of the order container. A place input corresponding to the position of the representation of the product at a packing location within the at least one image of the order container may be received and transmitted to a robot control system.

There is provided a method and an apparatus for controlling a robot arm. In this control scheme, a position error indicating a deviation between a command position, which is a control target position, and a current position, which is a position where the arm of the robot is currently located, is acquired. When the acquired position error exceeds a threshold, a new corrected command position between the current position and the command position is set. After the arm of the robot is moved to the corrected command position, a new corrected command position reset between the corrected command position serving as a new current position and the command position. Reconfiguration of a corrected command position is iterated until a current position of the robot arm becomes equal to the command position so that movement of the robot arm is achieved from the current position to the command position.

An electronic apparatus for a database construction and control of a robotic manipulator is provided. The electronic apparatus stores information associated with a task of a robotic manipulator. The electronic apparatus further receives a first plurality of signals from a first plurality of sensors associated with a wearable device. The electronic apparatus further applies a predefined model on a first set of signals of the first plurality of signals. The electronic apparatus further determines arrow direction information based on the application of the predefined model on the first set of signals. The electronic apparatus further aggregates the determined arrow direction information with information about the first set of signals to generate output information. The electronic apparatus further stores the generated output information for each of a first plurality of poses performed for the task using the wearable device.

Described is system for tele-robotic operations over time-delayed communication links. Sensor data is acquired from at least one sensor for sensing surroundings of a robot having at least one robotic arm for manipulating an object. A three-dimensional model of the sensed surroundings is generated, and the sensor data is fit to the three-dimensional model. Using the three-dimensional model, a user demonstrates a movement path for the at least one robotic arm. A flow field representing the movement path is generated and combined with obstacle-repellent forces to provide force feedback to the user through a haptic device. The flow field comprises a set of parameters, and the set of parameters are transmitted to the robot to execute a movement of the at least one robotic arm for manipulating the object.

The present invention relates to methods for learning a robot task and robots systems using the same. A robot system may include a robot configured to perform a task, and detect force information related to the task, a haptic controller configured to be manipulatable for teaching the robot, the haptic controller configured to output a haptic feedback based on the force information while teaching of the task to the robot is performed, a sensor configured to sense first information related to a task environment of the robot and second information related to a driving state of the robot, while the teaching is performed by the haptic controller for outputting the haptic feedback, and a computer configured to learn a motion of the robot related to the task, by using the first information and the second information, such that the robot autonomously performs the task.

A robot control device includes a memory part configured to store storing operational information for causing the robot to perform a given operation, and a processing part configured to control operation of the robot by using the storing operational information as automatic operational information for causing the robot to perform an automatic work. The processing part is adapted to accept manipulational information for correcting the operation of the robot performing the automatic work using the automatic operational information, from a manipulating device, control the robot to perform operation corrected from the operation related to the automatic operational information, and store in the memory part corrected operational information for causing the robot to perform the corrected operation as the storing operational information. The manipulating device outputs to the processing part the manipulational information based on first operational information indicative of the operation of the manipulating device.

In robot teaching programming, a robot teaching programming method, apparatus and system, and a computer-readable medium, can realize the programming of a robot simply, and are not restricted in terms of robot types. A robot teaching programming system includes a movable apparatus for imitating movement of an end effector of a robot in a working space of the robot; a robot teaching programming apparatus for recording first movement information of the movable apparatus in a first coordinate system and converting the same to second movement information in a second coordinate system of the robot, and then programming the robot according to the second movement information. Using a movable apparatus to simulate an end effector of a robot has the advantages of ease of operation, and no restrictions in terms of robot types. Teaching programming is accomplished through simple coordinate transformation, and there is no need for advanced programming skills.