Patents by Inventor Maksym Lefarov

Maksym Lefarov has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 12246450
    Abstract: A computer-implemented method for for learning a policy. The method includes: recording at least an episode of interactions of the agent with its environment following policy and adding the recorded episode to a set of training data; optimizing a transition dynamics model based on the training data such that the transition dynamics model predicts the next states of the environment depending on the states and actions contained in the training data; optimizing policy parameters based on the training data and the transition dynamics model by optimizing a reward. In the method, the transition dynamics model comprises a first model characterizing the global model and a second model characterizing a correction model, which is configured to correct outputs of the first model.
    Type: Grant
    Filed: March 1, 2022
    Date of Patent: March 11, 2025
    Assignee: Robert Bosch GmbH
    Inventors: Felix Berkenkamp, Lukas Froehlich, Maksym Lefarov, Andreas Doerr
  • Patent number: 12084073
    Abstract: A method and device parameterize a driving dynamics controller of a vehicle, which intervenes in a controlling manner in a driving dynamics of the vehicle. The driving dynamics controller ascertains an action depending on a vehicle state. The method includes providing a model for predicting a vehicle state. The model configured to predict a subsequent vehicle state depending on the vehicle state and the action. At least one data tuple is ascertained including a sequence of vehicle states and respectively associated actions. The vehicle states are ascertained by the driving dynamics controller using the model depending on an ascertained action. The parameters of the driving dynamics controller are changed/adjusted such that a cost function which ascertains costs of the trajectory depending on the vehicle states and on the ascertained actions of the respectively associated vehicle states and is dependent on the parameters of the driving dynamics controller is minimized.
    Type: Grant
    Filed: June 29, 2022
    Date of Patent: September 10, 2024
    Assignee: Robert Bosch GmbH
    Inventors: Andreas Doerr, Felix Berkenkamp, Maksym Lefarov, Valentin Loeffelmann
  • Publication number: 20230001940
    Abstract: A method and device parameterize a driving dynamics controller of a vehicle, which intervenes in a controlling manner in a driving dynamics of the vehicle. The driving dynamics controller ascertains an action depending on a vehicle state. The method includes providing a model for predicting a vehicle state. The model configured to predict a subsequent vehicle state depending on the vehicle state and the action. At least one data tuple is ascertained including a sequence of vehicle states and respectively associated actions. The vehicle states are ascertained by the driving dynamics controller using the model depending on an ascertained action. The parameters of the driving dynamics controller are changed/adjusted such that a cost function which ascertains costs of the trajectory depending on the vehicle states and on the ascertained actions of the respectively associated vehicle states and is dependent on the parameters of the driving dynamics controller is minimized.
    Type: Application
    Filed: June 29, 2022
    Publication date: January 5, 2023
    Inventors: Andreas Doerr, Felix Berkenkamp, Maksym Lefarov, Valentin Loeffelmann
  • Publication number: 20220297290
    Abstract: A computer-implemented method for for learning a policy. The method includes: recording at least an episode of interactions of the agent with its environment following policy and adding the recorded episode to a set of training data; optimizing a transition dynamics model based on the training data such that the transition dynamics model predicts the next states of the environment depending on the states and actions contained in the training data; optimizing policy parameters based on the training data and the transition dynamics model by optimizing a reward. In the method, the transition dynamics model comprises a first model characterizing the global model and a second model characterizing a correction model, which is configured to correct outputs of the first model.
    Type: Application
    Filed: March 1, 2022
    Publication date: September 22, 2022
    Inventors: Felix Berkenkamp, Lukas Froehlich, Maksym Lefarov, Andreas Doerr
  • Publication number: 20220011748
    Abstract: A method for an industrial system. The method includes: ascertaining a representation of the industrial system, the ascertainment of the representation including: selecting a first state of the representation, selecting, based on the first state, at least one machining order from a plurality of machining orders as a function of the first state of the representation and as a function of at least one previously ascertained recommendation, and ascertaining a second state as a subsequent state of the first state via a simulation of the second state as a function of the at least one selected machining order and as a function of the first state; and ascertaining a manufacturing schedule for the industrial system as a function of the ascertained representation.
    Type: Application
    Filed: July 1, 2021
    Publication date: January 13, 2022
    Inventors: Felix Milo Richter, Maksym Lefarov
  • Publication number: 20210312280
    Abstract: A method for scheduling a set of jobs for a plurality of machines. Each job is defined by at least one feature which characterizes a processing time of the job. If any of the machines is free, a job from of the set of jobs is selected to be carrying out by said machine and scheduled for said machine. The job is selected as follows: a Graph Neural Network receives as input the set of jobs and a current state of at least the machine which is free, the Graph Neural Network outputs a reward for the set of jobs if launched on the machines, which states are inputted into the Graph Neuronal Network, and the job for the free machine is selected depending on the Graph Neural Network output.
    Type: Application
    Filed: February 19, 2021
    Publication date: October 7, 2021
    Inventors: Ayal Taitler, Christian Daniel, Dotan Di Castro, Felix Milo Richter, Joel Oren, Maksym Lefarov, Nima Manafzadeh Dizbin, Zohar Feldman