# US 11,897,134 B2 · Configuring a simulator for robotic machine learning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,897,134 B2 - Document ID: US11897134B2 - Status: Granted - Kind: B2 - Application number: 16/991,241 - Filed: 2020-08-12 - Published: 2022-02-17 - Granted: 2024-02-13 - Earliest claimed: 2020-08-12 - Assignees: General Electric Company - Applicants: General Electric Company - Inventors: Bradford Wayne Miller - Web page: https://patentgrep.com/patent/US11897134B2 ## CPC - B25J 9/163: {learning, adaptive, model based, rule based expert control} - G05B 13/0265: {the criterion being a learning criterion} - G05B 13/04: involving the use of models or simulators - B25J: MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES ({manipulators specially adapted for use in surgery A61B34/70; manipulators used in cleaning hollow articles B08B9/04}; manipulators associated with rolling mills B21B39/20; manipulators associated with forging machines B21J13/10; {manipulators associated with picking-up and placing mechanisms B23P19/007}; means for holding wheels or parts thereof B60B30/00; {vehicles with ground-engaging propulsion means, e.g. walking members B62D57/02, B62D57/032; devices for picking-up and depositing articles or materials between conveyors B65G47/90, B65G47/91; manipulators with gripping or holding means for transferring packages B65H67/065}; cranes B66C; {manipulators used in the protection or supervision of pipe-line installations F17D5/00; walking equipment adapted for nuclear steam-generators F22B37/006}; manipulators specially adapted for, or associated with, nuclear reactors G21C; {apparatus used for handling wafers during manufacture or treatment of semiconductor H10P72/50}) - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) ## Abstract Provided are systems and methods for configuring a robotic simulator that is used to train a robot via machine learning. In one example, a method may include storing a domain description which comprises information about an operating environment of a robot, generating, via an automated planner, a plurality configuration files for a robotic simulator based on the domain description, where each configuration file comprises different configurations of a simulation environment for training a machine learning algorithm of the robot, and storing the plurality of configuration files in a memory device. ## Claims ### Claim 1 A computing system comprising: a storage configured to store a domain description which comprises information about an operating environment of a robot; and a processor configured to generate, via an automated planner, a plurality of configuration files for a robotic simulator based on the domain description, where each configuration file comprises different configurations of a simulation environment for training a machine learning algorithm of the robot, and store the plurality of configuration files in a memory device. ### Claim 2 (depends on 1) The computing system of claim 1, wherein the processor is configured to train the robot by iteratively executing a plurality of simulations of the robot within the simulation environment, wherein the plurality of simulations are configured based on the plurality of different configurations of the simulation, respectively. ### Claim 3 (depends on 1) The computing system of claim 1, wherein the processor is further configured to receive a predetermined user criteria for the simulation environment, and generate parameters of the plurality of configuration files based on the predetermined user criteria. ### Claim 4 (depends on 1) The computing system of claim 1, wherein the processor generates a configuration file which includes an obstacle in a travel path of the robot within the simulation environment. ### Claim 5 (depends on 1) The computing system of claim 1, wherein each configuration file comprises at least one of a start location and an end location for the robot within the simulation environment, and an operation to be performed by the robot during the simulation. ### Claim 6 (depends on 1) The computing system of claim 1, wherein the processor is configured to sequentially increase a difficulty of an operation of the robot via an incremental sequence of configuration files. ### Claim 7 (depends on 1) The computing system of claim 1, wherein the processor is configured to upload the plurality of configuration files into a library of a robotic simulation application. ### Claim 8 A method comprising: storing a domain description which comprises information about an operating environment of a robot; generating, via an automated planner, a plurality of configuration files for a robotic simulator based on the domain description, where each configuration file comprises different configurations of a simulation environment for training a machine learning algorithm of the robot; and storing the plurality of configuration files in a memory device. ### Claim 9 (depends on 8) The method of claim 8, further comprising training the robot by iteratively executing a plurality of simulations of the robot within the simulation environment, wherein the plurality of simulations are configured based on the plurality of different configurations of the simulation, respectively. ### Claim 10 (depends on 8) The method of claim 8, further comprising receiving a predetermined user criteria for the simulation environment, and generating parameters of the plurality of configuration files based on the predetermined user criteria. ### Claim 11 (depends on 8) The method of claim 8, wherein the generating comprises generating a configuration file which includes an obstacle in a travel path of the robot within the simulation environment. ### Claim 12 (depends on 8) The method of claim 8, wherein each configuration file comprises at least one of a start location and an end location for the robot within the simulation environment, and an operation to be performed by the robot during the simulation. ### Claim 13 (depends on 8) The method of claim 8, wherein the generating comprises sequentially increasing a difficulty of an operation of the robot via an incremental sequence of configuration files. ### Claim 14 (depends on 8) The method of claim 8, further comprising uploading the plurality of configuration files into a library of a robotic simulation application. ### Claim 15 A non-transitory computer-readable medium comprising instructions which when executed by a processor cause a computer to perform a method comprising: storing a domain description which comprises information about an operating environment of a robot; generating, via an automated planner, a plurality of configuration files for a robotic simulator based on the domain description, where each configuration file comprises different configurations of a simulation environment for training a machine learning algorithm of the robot; and storing the plurality of configuration files in a memory device. ### Claim 16 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the method further comprises training the robot by iteratively executing a plurality of simulations of the robot within the simulation environment, wherein the plurality of simulations are configured based on the plurality of different configurations of the simulation, respectively. ### Claim 17 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the method further comprises receiving a predetermined user criteria for the simulation environment, and generating parameters of the plurality of configuration files based on the predetermined user criteria. ### Claim 18 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the generating comprises generating a configuration file which includes an obstacle in a travel path of the robot within the simulation environment. ### Claim 19 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein each configuration file comprises at least one of a start location and an end location for the robot within the simulation environment, and an operation to be performed by the robot during the simulation. ### Claim 20 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the generating comprises sequentially increasing a difficulty of an operation of the robot via an incremental sequence of configuration files.