To generate a more appropriate path, provided is a robot path generation device including circuitry configured to: hold a track planning module learning data set, in which a plurality of pieces of path data generated based on a motion constraint condition of a robot, and evaluation value data, which corresponds to each of the plurality of pieces path data and is a measure under a predetermined evaluation criterion, are associated with each other; and generate, based on a result of a machine learning process that is based on the track planning module learning data set, a path of the robot between a set start point and a set end point, which are freely set.

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.

A motion path generation device generates a motion path of a robot that has a plurality of joints and a plurality of shafts. Adjacent two of the plurality of shafts are connected by a corresponding one of the plurality of joints. The motion path generation device generates a via point candidate that is a candidate of a next via point to be connected to a parent via point. The motion path generation device adds the via point candidate as a new via point in response to determining that the via point candidate does not interfere with an obstacle.

Disclosed techniques for graph generation for online path planning offer multiple advantages, such as providing for high-quality motion during online operation of the robot, while reducing the computational burden of graph generation. Achieving these competing goals involves reducing the dimensionality of the graph generation problem by performing unconstrained sampling that defines partial robot poses that set values for fewer than all configuration parameters of the robot. The remaining configuration parameters for each sample are then determined in dependence on a distance function that relates the partial pose to one or more reference robot poses that are associated with one or more tasks to be performed by the robot and are provided as inputs to the graph generation. Reference robot poses may be determined automatically based on computer analysis of the robot application or may be user-input values.

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.

Disclosed techniques for graph generation for online path planning offer multiple advantages, such as providing for high-quality motion during online operation of the robot, while reducing the computational burden of graph generation. Achieving these competing goals involves reducing the dimensionality of the graph generation problem by performing unconstrained sampling that defines partial robot poses that set values for fewer than all configuration parameters of the robot. The remaining configuration parameters for each sample are then determined in dependence on a distance function that relates the partial pose to one or more reference robot poses that are associated with one or more tasks to be performed by the robot and are provided as inputs to the graph generation. Reference robot poses may be determined automatically based on computer analysis of the robot application or may be user-input values.