A control device includes a processor that is configured to execute computer-executable instructions so as to control a robot, wherein the processor is configured to calculate an image processing parameter related to image processing on an image of a target object captured by a camera, by using machine learning, detect the target object on the basis of an image on which the image processing is performed by using the calculated image processing parameter, and control a robot on the basis of a detection result of the target object.

A control device includes a processor that is configured to execute computer-executable instructions so as to control a robot, wherein the processor is configured to calculate a force control parameter related to force control of a robot by using machine learning, and control the robot on the basis of the calculated force control parameter.

In order to stabilize control of a driving section, a robot control apparatus includes a control section that acquires a driving position of the driving section that drives a robot and an operation force that is a force operating on the robot, and performs first control of the driving section based on the driving position and second control of the driving section based on the operation force; and a changing section that changes a size of servo stiffness of the robot that is realized by the control of the control section.

A force detector provided between a robot arm and a robot hand detects forces Fx, Fy, and Fz. A robot controller performs a filtering process for the forces Fx and Fy by a first low-pass filter of a cutoff frequency F.sub.c1, moves the robot hand so that the forces Fx and Fy become smaller, corrects a trajectory of the robot arm, performs a filtering process for the force Fz by a second low-pass filter of a cutoff frequency F.sub.c2 having a frequency higher than the cutoff frequency F.sub.c1, performs a threshold value determination for the force Fz, and stops the movement of the robot hand when the force Fz exceeds a threshold value during a fitting operation.

A method includes determining, on a processor, a current movement phase for a compound joint function based on sensor data. The method further includes determining, on the processor, a respective robot state parameter trace for each movement phase in a normal and impaired subject. The method further includes determining, on the processor, a deficit parameter for each movement phase. The method further includes determining, on the processor, an adaptive magnitude for the robot applied torque based on the deficit parameter of the current movement phase. The method further includes applying, to the robot joint, the adaptive magnitude for the robot applied torque for the current movement phase, based on an adaptive timing for the current movement phase. An apparatus is also described for providing deficit-adjusted adaptive assistance during movement phases of the impaired joint.

A process of controlling an industrial robot includes the steps of calculating, in a calculation module, a control articular force setpoint of the axis controller module; calculating, in an articular converter, the articular conversion matrix from articular positions; providing the axis controller module with the multi-dimensional external forces exerted on the effector; calculating, in the axis controller module, the vector of the articular forces; calculating, in the axis controller module, the current loop control setpoints, taking into account the articular force vector and the articular force setpoint; and calculating, in the axis controller module, the control setpoints for the power units according to the control setpoints for the current loops.