Disclosed herein are an apparatus and method for generating robot interaction behavior. The method for generating robot interaction behavior includes generating co-speech gesture of a robot corresponding to utterance input of a user, generating a nonverbal behavior of the robot, that is a sequence of next joint positions of the robot, which are estimated from joint positions of the user and current joint positions of the robot based on a pre-trained neural network model for robot pose estimation, and generating a final behavior using at least one of the co-speech gesture and the nonverbal behavior.

A processing device with a processing head including a processing unit designed as a tool and/or applicator unit, in particular a printer unit, and a first positioning device for moving the processing head in order to position the processing unit with a first accuracy at a specified processing position. The processing head has a second positioning device and the processing device is adapted to position the processing unit at the specified processing position with a second accuracy using the second positioning device, the second accuracy being higher than the first accuracy, so that the lower accuracy of the first positioning device is compensated for by the positioning with the second positioning device.

An example method may include i) detecting a disturbance to a gait of a robot, where the gait includes a swing state and a step down state, the swing state including a target swing trajectory for a foot of the robot, and where the target swing trajectory includes a beginning and an end; and ii) based on the detected disturbance, causing the foot of the robot to enter the step down state before the foot reaches the end of the target swing trajectory.

A method of controlling a robot system includes measuring, capturing, and calculating. A measuring device attached to the robot measures a position and an orientation of a marker with respect to the robot. An imaging device captures an image of the marker and a workpiece. A control device calculates a relative positional relationship between the workpiece and the robot in accordance with a result obtained by the measuring device and with the image captured by the imaging device.

Systems and methods are provided for automatic intrinsic and extrinsic calibration for a robot optical sensor. An implementation includes an optical sensor; a robot arm; a calibration chart; one or more processors; and a memory storing instructions that cause the one or more processors to perform operations that includes: determining a set of poses for calibrating the first optical sensor; generating, based at least on the set of poses, pose data comprising three dimensional (3D) position and orientation data; moving, based at least on the pose data, the robot arm into a plurality of poses; at each pose of the plurality of poses, capturing a set of images of the calibration chart with the first optical sensor and recording a pose; calculating intrinsic calibration parameters, based at least on the set of captured images; and calculating extrinsic calibration parameters, based at least on the set of captured images.

A robot device includes: a robot; an operation control section; a sensor configured to detect a value related to a position of a control target portion of the robot; a low-speed position information acquisition section configured to acquire low-speed position information of the control target portion; a calibration section configured to perform calibration between the sensor and the robot by using the low-speed position information and a command position; and a learning control section configured to learn a correction amount that reduces a deviation between operation described in an operation program and an actual operation of the control target portion, by using a detection value of the sensor that is acquired when the robot is operated in accordance with the operation program, the low-speed position information or the command position, and calibration data acquired through the calibration.

A teaching method includes imaging a teaching jig to acquire a captured image, detecting a position and a posture of the teaching jig based on the captured image, changing a position and a posture of a robot arm of a robot based on a result obtained by the detection to maintain a positional relation between the teaching jig and a control point of the robot arm, and acquiring the position and the posture of the robot arm as teaching information when a control device, which controls operation of the robot, acquires a teaching signal.

A mechanism-parametric-calibration method for a robotic arm system is provided. The method includes controlling the robotic arm to perform a plurality of actions so that one end of the robotic arm moves toward corresponding predictive positioning-points; determining a predictive relative-displacement between each two of the predictive positioning-points; after the robotic arm performs each of the actions, sensing three-dimensional positioning information of the end of the robotic arm; determining, according to the three-dimensional positioning information, a measured relative-displacement moved by the end of the robotic arm when the robotic arm performs each two of the actions; deriving an equation corresponding to the robotic arm from the predictive relative-displacements and the measured relative-displacements; and utilizing a feasible algorithm to find the solution of the equation.

A calibration method for a coordinate system of a workpiece held by a robot manipulator, which includes the following steps: setting a predicted coordinate system on the workpiece; controlling the drive mechanism to drive the workpiece to move a specific distance along a coordinate axis in the predicted coordinate system and measuring the distance change of the workpiece in a direction perpendicular to the move; using the measured distance change to determine an orientation error between the predicted coordinate system and the actual coordinate system; correcting the orientation parameters of the predicted coordinate system; controlling the drive mechanism to drive the workpiece to rotate by a specific angle around a coordinate axis of the predicted coordinate system and measuring the distance change after being rotated; using the measured distance change to determine a position error; correcting the position parameters of the predicted coordinate system.

A method and system for piece-picking or piece put-away within a logistics facility. The system includes a central server and at least one mobile manipulation robot. The central server is configured to communicate with the robots to send and receive piece-picking data which includes a unique identification for each piece to be picked, a location within the logistics facility of the pieces to be picked, and a route for the robot to take within the logistics facility. The robots can then autonomously navigate and position themselves within the logistics facility by recognition of landmarks by at least one of a plurality of sensors. The sensors also provide signals related to detection, identification, and location of a piece to be picked or put-away, and processors on the robots analyze the sensor information to generate movements of a unique articulated arm and end effector on the robot to pick or put-away the piece.