A method for manipulating a multi-link robotic arm includes: at a first time, recording a first optical image through an optical sensor arranged proximal a distal end of the robotic arm proximal an end effector; detecting a global reference feature in a first position in the first optical image; virtually locating a global reference frame based on the first position of the global reference feature in the first optical image; calculating a first pose of the end effector within the global reference frame at approximately the first time based on the first position of the global reference feature in the first optical image; and driving a set of actuators within the robotic arm to move the end effector from the first pose toward an object keypoint, the object keypoint defined within the global reference frame and representing an estimated location of a target object within range of the end effector.

A detection device and a detection method are provided for robot-induced loads that can act upon the human body in contact with the robot during a working process. The robot-induced loads, in particular forces, are measured by the measuring device (16) of an external sensing device (2). The measuring device (16) is suitably positioned and oriented in this process in the working area of an industrial robot (3) by means of a positioning device (15).

A robot control device includes a learning control unit for calculating a learning correction amount, a position storage unit for storing a position of a leading end of a robot mechanism part during the learning control, and a speed storage unit for storing a speed of the leading end of the robot mechanism part during the learning control. The robot control device determines, while the robot mechanism part is operated by a position command after the learning control, whether or not the position and the speed of the leading end are in an abnormal state based on errors with respect to the position and the speed of the leading end stored during the learning control. The robot control device switches a determination as to whether the learning correction amount is applied in accordance with this determination result.

A robot control device includes a learning control unit for calculating a learning correction amount, a position storage unit for storing a position of a leading end of a robot mechanism part during the learning control, and a speed storage unit for storing a speed of the leading end of the robot mechanism part during the learning control. The robot control device determines, while the robot mechanism part is operated by a position command after the learning control, whether or not the position and the speed of the leading end are in an abnormal state based on errors with respect to the position and the speed of the leading end stored during the learning control. The robot control device switches a determination as to whether the learning correction amount is applied in accordance with this determination result.

A robot and method for controlling an industrial robot, which has a first robot arm, a second robot arm, a joint defining a kinematic pair between the first and second robot arms, an actuator for generating relative movement between the first and second robot arms, and a robot controller for controlling the movements of the actuator. The method includes the steps of: determining a presence of a first torque indication at the actuator to be interpreted as a first command to the robot controller; repeatedly obtaining an external torque value (.sub.ext) to obtain an external torque behaviour; comparing the external torque behaviour with the first torque indication; and executing a robot function corresponding to the first command upon detecting that the external torque behaviour corresponds to the first torque indication. The obtained external torque behaviour depends on a reference torque value (.sub.ref) obtained from a dynamic model of the robot

A robot and method for controlling an industrial robot, which has a first robot arm, a second robot arm, a joint defining a kinematic pair between the first and second robot arms, an actuator for generating relative movement between the first and second robot arms, and a robot controller for controlling the movements of the actuator. The method includes the steps of: determining a presence of a first torque indication at the actuator to be interpreted as a first command to the robot controller; repeatedly obtaining an external torque value (.sub.ext) to obtain an external torque behaviour; comparing the external torque behaviour with the first torque indication; and executing a robot function corresponding to the first command upon detecting that the external torque behaviour corresponds to the first torque indication. The obtained external torque behaviour depends on a reference torque value (.sub.ref) obtained from a dynamic model of the robot

In a workpiece loader device, a loader mechanism is configured to move a loader hand directly held by operator's hands to a desired position. A loader mechanism control part stores position coordinates as workpiece receiving position coordinates when the loader hand is moved by operator's hands to the workpiece receiving position and a completion of moving is confirmed.

In a workpiece loader device, a loader mechanism is configured to move a loader hand directly held by operator's hands to a desired position. A loader mechanism control part stores position coordinates as workpiece receiving position coordinates when the loader hand is moved by operator's hands to the workpiece receiving position and a completion of moving is confirmed.

A computing system identifies a trajectory example generated by a human operator. The trajectory example includes trajectory information of the human operator while performing a task to be learned by a control system of the computing system. Based on the trajectory example, the computing system trains the control system to perform the task exemplified in the trajectory example. Training the control system includes generating an output trajectory of a robot performing the task. The computing system identifies an updated trajectory example generated by the human operator based on the trajectory example and the output trajectory of the robot performing the task. Based on the updated trajectory example, the computing system continues to train the control system to perform the task exemplified in the updated trajectory example.

A computing system identifies a trajectory example generated by a human operator. The trajectory example includes trajectory information of the human operator while performing a task to be learned by a control system of the computing system. Based on the trajectory example, the computing system trains the control system to perform the task exemplified in the trajectory example. Training the control system includes generating an output trajectory of a robot performing the task. The computing system identifies an updated trajectory example generated by the human operator based on the trajectory example and the output trajectory of the robot performing the task. Based on the updated trajectory example, the computing system continues to train the control system to perform the task exemplified in the updated trajectory example.