Abstract: A method and computing system comprising identifying one or more candidate objects for selection by a robot. A path to the one or more candidate objects may be determined based upon, at least in part, a robotic environment and at least one robotic constraint. A feasibility of grasping a first candidate object of the one or more candidate objects may be validated. If the feasibility is validated, the robot may be controlled to physically select the first candidate object. If the feasibility is not validated, at least one of a different grasping point of the first candidate object, a second path, or a second candidate object may be selected.

Abstract: Embodiments included herein are directed towards a system and method for robotic control. The system may include a graphical user interface configured to allow a user to access a data storage unit associated with a robot. The system may further include a configurable portion including the data storage unit, a manipulator controller, and a data transfer layer. The system may also include a generic portion configured to parse the configurable portion to generate a robotic controller.

Abstract: Embodiments included herein are directed towards a robotic system and method. Embodiments may include a transportation mechanism having at least three legs and a computing device configured to receive a plurality of optimization components. Each optimization component may include a plurality of variables and the computing device may be further configured to perform a randomized simulation based upon, at least in part, each of the plurality of optimization components. The computing device may be further configured to provide one or more results of the randomized simulation to the transportation mechanism to enable locomotion via the at least three legs.

Abstract: A method, computer program product, and computer system for configuring a stochastic simulation scenario, wherein the stochastic simulation scenario may include one or more variables without a complete probability distribution. The stochastic simulation scenario may be executed to generate one or more results of the stochastic simulation scenario. At least a portion of the one or more variables without the probability distribution may be optimized using one or more optimization metrics on the one or more results of the stochastic simulation scenario.

Abstract: Embodiments included herein are directed towards a system and method for robotic control. The system may include a graphical user interface configured to allow a user to access a data storage unit associated with a robot. The system may further include a configurable portion including the data storage unit, a manipulator controller, and a data transfer layer. The system may also include a generic portion configured to parse the configurable portion to generate a robotic controller.

Abstract: A method and computing system comprising identifying one or more candidate objects for selection by a robot. A path to the one or more candidate objects may be determined based upon, at least in part, a robotic environment and at least one robotic constraint. A feasibility of grasping a first candidate object of the one or more candidate objects may be validated. If the feasibility is validated, the robot may be controlled to physically select the first candidate object. If the feasibility is not validated, at least one of a different grasping point of the first candidate object, a second path, or a second candidate object may be selected.

Abstract: A method, computer program product, and computer system for configuring a stochastic simulation scenario, wherein the stochastic simulation scenario may include one or more variables, wherein at least a portion of the one or more variables may include agent behavior, and wherein the stochastic simulation scenario may be randomized and digital. The stochastic simulation scenario may be executed to generate one or more results of the stochastic simulation scenario. At least a portion of the one or more variables may be optimized using one or more optimization metrics on the one or more results of the stochastic simulation scenario, wherein at least the portion of the one or more variables may be modified based on game theory.

Abstract: A computer-implemented method, computer program product, and computing system is provided for a digital proxy simulation of robotic hardware. In an implementation, a method may include creating a digital proxy simulation for a robotic hardware wherein the digital simulation and the robotic hardware may share a network interface. a user may be provided with an option to switch between the robotic hardware and the digital proxy simulation. The switch may be executed, upon receiving a user selection, between the robotic hardware and the digital proxy simulation, wherein executing the switch includes transferring input and output signals between the digital proxy simulation and the robotic hardware.

Abstract: A method, computer program product, and computer system for configuring a stochastic simulation scenario, wherein the stochastic simulation scenario may include one or more variables, wherein at least a portion of the one or more variables may include agent behavior, and wherein the stochastic simulation scenario may be randomized and digital. The stochastic simulation scenario may be executed to generate one or more results of the stochastic simulation scenario. At least a portion of the one or more variables may be optimized using one or more optimization metrics on the one or more results of the stochastic simulation scenario, wherein at least the portion of the one or more variables may be modified based on game theory.

Abstract: A method, computer program product, and computer system for configuring a stochastic simulation scenario, wherein the stochastic simulation scenario may include one or more variables without a complete probability distribution. The stochastic simulation scenario may be executed to generate one or more results of the stochastic simulation scenario. At least a portion of the one or more variables without the probability distribution may be optimized using one or more optimization metrics on the one or more results of the stochastic simulation scenario.

Abstract: A computer-implemented method, computer program product, and computing system is provided for a digital proxy simulation of robotic hardware. In an implementation, a method may include creating a digital proxy simulation for a robotic hardware wherein the digital simulation and the robotic hardware may share a network interface. a user may be provided with an option to switch between the robotic hardware and the digital proxy simulation. The switch may be executed, upon receiving a user selection, between the robotic hardware and the digital proxy simulation, wherein executing the switch includes transferring input and output signals between the digital proxy simulation and the robotic hardware.

Abstract: Preferred embodiment systems of the invention use a scalable number of cameras that can image a volume of interest from plurality of angles. The volume can be large and illuminated with general non-coherent illumination. Synchronized cameras process data in real time to addresses particle overlap issues while also increasing observable volumes. Systems of the invention also use higher frame rate cameras to increase the amount of particle travel in each frame. Approaches of the invention alleviate the concerns with additional data accumulation by conducting image processing and segmentation at the time of acquisition prior to transfer from a camera to eliminate a data transfer bottleneck between camera and computer and eliminate a data storage problem.