The present invention relates to robotic device positioning. By extending the robotic arm into the surgical field, a system is provided that automatically aligns an instrument following a plan, e.g., surgical plan, using only instrument tracking feedback. No tracking markers on the robot are required.

According to one aspect of the present invention, disclosed is an automatic calibration method for a calibration device connected to a camera that is disposed the end effector of a robot and to a robot controller for controlling the robot. The method comprises the steps of: acquiring, from the camera and the robot controller, a robot-based coordinate system and an image of a marker marked in the work area of the robot (wherein the acquired image and robot-based coordinate system are recorded while the end effector is moved to a plurality of sample coordinates); and estimating the position of a robot coordinate system-based marker by using the acquired image and robot-based coordinate system.

An apparatus for constructing kinematic information of a robot manipulator is provided. The apparatus includes: a robot image acquisition part for acquiring a robot image containing shape information and coordinate information of the robot manipulator; a feature detection part for detecting the type of each of a plurality of joints of the robot manipulator and the three-dimensional coordinates of the joint using a feature detection model generated through deep learning based on the robot image containing shape information and coordinate information; and a variable derivation part for deriving Denavit-Hartenberg (DH) parameters based on the type of each of the plurality of joints of the robot manipulator and the three-dimensional coordinates of the joint.

A method of transporting an object using a system comprising a plurality of robots. The method comprises one or more robots of the plurality of robots arranging themselves to each exert a respective transporting force on the object. Each of the one or more robots evaluates whether it satisfies a transport criterion while arranged to exert the respective transporting force on the object. If all of the one or more robots satisfy the transport criterion, the one or more robots exert the respective transporting forces on the object to transport the object towards a destination. At least one of the one or more robots evaluates whether it satisfies the transport criterion based on observations of other robots of the plurality of robots within its vicinity.

A robot balance control method, a controller, and a computer readable storage medium are provided. The method includes: obtaining a desired motion trajectory matching a current motion status by performing a parameter adaptation adjustment on a current planned motion trajectory; determining, according to the motion status, a desired state parameter of each of soles, a centroid, and a waist of a humanoid robot for conforming to the desired motion trajectory; calculating, based on the motion status and the desired state parameter of each of the soles, the centroid, and the waist of the humanoid robot, a desired driving parameter of the humanoid robot for simultaneously meeting a robot dynamics requirement, a sole control requirement, a centroid control requirement, a waist control requirement, and force control parameter distribution constraint(s) at the current moment; and controlling, based on the desired driving parameter, a movement of the humanoid robot.

The present invention provides a method for monitoring a balanced state of a humanoid robot, comprising: acquiring state data of the robot falling in different directions and being stable, forming a support vector machine (SVM) training data set and obtaining, by training, an initial SVM classifier; inputting the state data of the robot to the trained SVM classifier, so that the SVM classifier outputs a classification result; taking statistics on a proportion of cycles judged to have an impending fall in the total number of control cycles within a judgment buffer time after the SVM classifier outputs the classification result, and finally determining a monitoring result of the balanced state of the robot according to the proportion and finally extracting state data of misjudged cycles within the buffer time, adding the state data to the current training data set and updating the SVM classifier, eventually enabling the classifier to achieve the effects of matching motion capabilities of the robot and monitoring the balanced state.

A system for moving items in a facility may be described herein. The system may instruct components of the system to move the items at different speeds or velocities based on an item's fragility rating. A fragility rating may indicate an amount of force that an item withstands prior to damaging the item. A fragility rating for an item may be determined based on known fragility ratings of items with similar item metrics.

A robot control system according to one or more embodiments may include a robot that performs a task in relation to a workpiece, a coordinate measuring machine that measures a three-dimensional shape of the workpiece, a control device that controls the robot in accordance with a measurement result from the coordinate measuring machine, and an image capturing apparatus that captures an image of the workpiece. An image capture interval for the image capturing apparatus is shorter than a measurement interval for the coordinate measuring machine. In a period after the coordinate measuring machine conducts a measurement and until the robot performs the task, the control device is configured to compute a position of the workpiece by referring to an image capture result from the image capturing apparatus.

A method for controlling a robotic device. The method includes providing demonstrations for carrying out a skill by the robot, each demonstration including a robot pose, an acting force as well as an object pose for each point in time of a sequence of points in time, ascertaining an attractor demonstration for each demonstration, training a task-parameterized robot trajectory model for the skill based on the attractor trajectories and controlling the robotic device according to the task-parameterized robot trajectory model.

A computing system including a processing circuit in communication with a camera having a field of view, The processing circuit obtains image information based on the objects in the field of view and defines a minimum viable region for a target open corner. Potential minimum viable regions are defined by identifying candidate edges of an object and determining potential intersection points based on the candidate edges. The minimum viable region may then be identified and validated from the potential minimum viable regions.