Abstract: In one example, a robotic system is disclosed that includes a plurality of components coupled together, a plurality of motors operable to move the plurality of components, a controller in electrical communication with the plurality of motors to generate control signals to actuate movement of the plurality of components, wherein the controller is configured to: receive a first set of control signals operative to generate a defined motion for the plurality of components, analyze the first set of control signals to determine a second set of control signals operative to define a retargeted motion for the plurality of components, wherein the retargeted motion suppresses vibrations of the plurality of components as compared to the defined motion, and provide the second set of control signals to the plurality of motors to actuate the retargeted motion by the plurality of components.

Abstract: A robot control method, and associated robot controllers and robots operating with such methods and controllers, providing real-time vibration suppression. The control method involves learning to support real-time, vibration-suppressing control. The method uses state-of-the-art machine learning techniques in conjunction with a differentiable dynamics simulator to yield fast and accurate vibration suppression. Vibration suppression using offline simulation approaches that can be computationally expensive may be used to create training data for the controller, which may be provide by a variety of neural network configurations. In other cases, sensory feedback from sensors onboard the robot being controlled can be used to provide training data to account for wear of the robot's components.

Abstract: A robot control method, and associated robot controllers and robots operating with such methods and controllers, providing real-time vibration suppression. The control method involves learning to support real-time, vibration-suppressing control. The method uses state-of-the-art machine learning techniques in conjunction with a differentiable dynamics simulator to yield fast and accurate vibration suppression. Vibration suppression using offline simulation approaches that can be computationally expensive may be used to create training data for the controller, which may be provide by a variety of neural network configurations. In other cases, sensory feedback from sensors onboard the robot being controlled can be used to provide training data to account for wear of the robot's components.

Abstract: A computing device may be used to create a model that includes a block. The block may represent a function corresponding to a simulation and capable of operating in a fault operational mode. The computing device may also, or alternatively, associate a fault scenario, corresponding to the model, with the fault operational mode of the block. Additionally, or alternatively, the computing device may simulate the fault scenario based on the block diagram model.

Abstract: A computing device may create a time based block diagram. The time based block diagram may comprise a ladder logic diagram and at least one block. The at least one block may correspond to at least one of a differential equation system or a difference equation system. The computing device may also execute the time based block diagram to simulate behavior of a dynamic system, device, or process. Executing the time based block diagram may include executing the ladder logic diagram and the at least one block. The computing device may further output results of the simulation of the behavior of the system, device, or process based on executing the time based block diagram.