An action teaching method is provided for teaching a robotic arm of a robotic arm system through a gesture teaching device. In a step (a), a touch condition of a user's finger is sensed by the touch sensing unit. In a step (b), a sensing result of the touch sensing unit is transmitted to an identification unit, so that a touch information is identified by the identification unit. In a step (c), the touch information is transmitted to a teaching unit, so that the teaching unit actuates a corresponding operation of the robotic arm system according to the touch information. In a step (d), an operating result of the robotic arm system is shown on a display unit, so that the user judges whether the operating result of the robotic arm system is successful through the display unit.

A robot manipulator including limbs moveable via bearings controlled by actuators; sensors to capture a bearing position and a bearing torque/bearing force; a first sensor to capture a force screw W; a housing downstream of the first sensor; a second sensor to capture a user force applied to the housing and/or a user torque; a computing unit to determine, using a dynamics model of the robot manipulator and based on particular bearing torque/bearing force, the force screw W, and the user force and/or the user torque, a first force and/or a first torque to shift the limbs and a second force and/or a second torque to apply to an external object via an effector, wherein the dynamics model includes at least gravitational forces and inertial forces based on the bearing position; and a storage unit to store the first and/or the second force, and/or the first and/or the second torque.

A skill transfer mechanical apparatus includes an operating part, a controller, a motion information detector and an operation apparatus. The controller includes a basic motion instructing module, a learning module, a motion correcting instruction generator, a motion correcting instruction, and a motion information storing module. The learning module carries out machine learning of the motion correcting instruction stored in the motion correcting instruction storing module by using the motion information stored in the motion information storing module, and after the machine learning is finished, accepts an input of the motion information during the operation of the operating part, and outputs the automatic motion correcting instruction. The operating part moves the working part according to an automatic motion instruction based on the basic motion instruction and the automatic motion correcting instruction, and the manual motion correction.

Provided is a robot system including a robot including at least one sensor that detects an applied external force and a controller that controls the robot. The controller changes an operation mode of the robot when performing lead-through teaching in accordance with a position and a pattern of the external force detected by the sensor.

A method for automatically ascertaining an input command for a robot, wherein the input command is entered by manually exerting an external force onto the robot. The input command is ascertained on the basis of the joint force component attempting to cause a movement of the robot in only one robot joint coordinate sub-space which is specific to the input command. The joint forces are imprinted with the external force.

An industrial robot having high operability for a user is provided. An industrial robot includes a manipulator, a controller which controls an operation of the manipulator, and a detection device attached to the manipulator and detecting a gesture input. The controller executes a process corresponding to the detected gesture input.

Robotic devices may be trained by a trainer guiding the robot along a target trajectory using physical contact with the robot. The robot may comprise an adaptive controller configured to generate control commands based on one or more of the trainer input, sensory input, and/or performance measure. The trainer may observe task execution by the robot. Responsive to observing a discrepancy between the target behavior and the actual behavior, the trainer may provide a teaching input via a haptic action. The robot may execute the action based on a combination of the internal control signal produced by a learning process of the robot and the training input. The robot may infer the teaching input based on a comparison of a predicted state and actual state of the robot. The robot's learning process may be adjusted in accordance with the teaching input so as to reduce the discrepancy during a subsequent trial.

A method for controlling a manipulator includes releasing the manipulator in reaction to a release request by an operator, wherein the recognition of the release request involves monitoring the variation over time of a measured value that is characteristic of a state of the manipulator. Increased robustness of the recognition of the release request results.

A sleeve worn on an arm allows detection of gestures by an array of sensors. Electromyography, inertial, and magnetic field sensors provide data that is processed to categorize gestures and translate the gestures into commands for robotic systems. Machine learning allows training of gestures to increase accuracy of detection for different users.

An action teaching method is provided for teaching a robotic arm of a robotic arm system through a gesture teaching device. In a step (a), a touch condition of a user's finger is sensed by the touch sensing unit. In a step (b), a sensing result of the touch sensing unit is transmitted to an identification unit, so that a touch information is identified by the identification unit. In a step (c), the touch information is transmitted to a teaching unit, so that the teaching unit actuates a corresponding operation of the robotic arm system according to the touch information. In a step (d), an operating result of the robotic arm system is shown on a display unit, so that the user judges whether the operating result of the robotic arm system is successful through the display unit.