A modular configurable robot, comprising robot modules comprising a coupling mechanism including an electrical coupling member comprising a network communication signal connection, an arrangement forming upon coupling an orientation signal, an integrated circuit comprising a microcontroller circuit with unique identification code and I/O ports coupled to said electrical coupling to receive orientation electrical signal, a communication slave module comprising ports and registers storing state values of the ports, one port pre-designated as input, the ports being open or closed depending on the port state, the robot comprising a master communication module forming with said slave modules a master slave communication network topology, a server hosting a database of robot module parameters, accessible by unique identification code, said master module retrieving from said communication slave module the unique identification code, and from the database robot module parameters, and from said microcontroller circuit said information of a relative orientation.

A method to control a robot to perform at least one Cartesian or joint space task comprises using quadratic programming to determine joint forces, in particular joint torques, and/or joint accelerations of said robot based on at least one cost function which depends on said task.

A control device 1A mainly includes a display control means 15A and an operation sequence generation means 16A. The display control means 15A is configured to transmit display information S2 relating to a task to be executed by a robot to a display device 2A. The operation sequence generation means 16A is configured, in a case that the display control means 15A has received, from the display device 2A, task designation information that is input information which schematically specifies the task, to generate an operation sequence to be executed by the robot based on the task designation information Ia.

A control device 1A mainly includes an operation sequence generation means 17A. The operation sequence generation means 17A is configured to generate, based on recognition results Ra relating to types and states of objects present in a workspace where a robot which performs a task and another working body perform cooperative work, an operation sequence Sa to be executed by the robot.

Controlling a metallurgical plant, the plant has at least one plant part (1) operated with first and second operating parameters (BP 1, BP2) at a particular time, and an operating result (BE) is established on the basis of the operation of the plant part (1) according to the first and second operating parameters (BP1, BP2). The operating result (BE) is recorded. At least the operating result (BE) is transmitted from a control device (5) of the first plant part (1) to a computing unit (9). The computing unit (9) varies the second operating parameters (BP2), but not the first operating parameters (BP1), and thereby determines varied second operating parameters (BP2′) associated with the first operating parameters (BP 1). The computing unit (9) transmits the varied second operating parameters (BP2′) back to the control device (5) of the first plant part (1). The control device (5) of the first plant part (1) uses the varied second operating parameters (BP2′), after the transmission of the varied second operating parameters (BP2′), when the first operating parameters (BP1) are established.

Embodiments of the present disclosure are directed towards a robotic system. The system may include a robot configured to receive an initial constrained approach for performing a robot task. The system may further include a graphical user interface in communication with the robot. The graphical user interface may be configured to allow a user to interact with the robot to determine an allowable range of robot poses associated with the robot task. The allowable range of robot poses may include fewer constraints than the initial constrained approach. The allowable range of poses may be based upon, at least in part, one or more degrees of symmetry associated with a workpiece associated with the robot task or an end effector associated with the robot. The system may also include a processor configured to communicate the allowable range of robot poses to the robot.

Embodiments of the present disclosure are directed towards a robotic system. The system may include a robot configured to receive an initial constrained approach for performing a robot task. The system may further include a graphical user interface in communication with the robot. The graphical user interface may be configured to allow a user to interact with the robot to determine an allowable range of robot poses associated with the robot task. The allowable range of robot poses may include fewer constraints than the initial constrained approach. The allowable range of poses may be based upon, at least in part, one or more degrees of symmetry associated with a workpiece associated with the robot task or an end effector associated with the robot. The system may also include a processor configured to communicate the allowable range of robot poses to the robot.

A method for a multi-body controller receives steering commands for a robot to perform a given task. The robot includes an inverted pendulum body, a plurality of joints, an arm coupled to the inverted pendulum body, a leg coupled to the inverted pendulum body, and a drive wheel rotatably coupled to the leg. With the steering commands, the method generates a wheel torque and a wheel axle force to perform the given task. The method includes receiving movement constraints for the robot and manipulation inputs configured to manipulate the arm to perform the given task. For each joint, the method generates a corresponding joint torque having an angular momentum where the joint torque satisfies the movement constraints based on the manipulation inputs, the wheel torque, and the wheel axle force. The method further includes controlling the robot to perform the given task using the joint torques.

A method for controlling a kinematically redundant robot (100) in order to fulfill multiple tasks. At least one passivity-based first controller module (102) is used, at least one task target description and at least one associated task mapping are computed for the at least one first controller module (102), at least one weighting is computed for the tasks, and the at least one first controller module (102) is integrated into an overall controller (104), using the at least one weighting. Moreover, the invention relates to a computer program product that includes commands which, when the program is executed with the aid of at least one processor, prompt the processor to carry out such a method.

A method for determining a sparse Gaussian process model to be carried out in a solely hardware-based model calculation unit includes: providing supporting point data points, a parameter vector based thereon, and corresponding hyperparameters; determining or providing virtual supporting point data points for the sparse Gaussian process model; and determining a parameter vector Q.sub.y* for the sparse Gaussian process model with the aid of a Cholesky decomposition of a covariant matrix K.sub.M between the virtual supporting point data points and as a function of the supporting point data points, the parameter vector based thereon, and the corresponding hyperparameters, which define the sparse Gaussian process model.