Patents by Inventor Mark Graves

Mark Graves 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: 11934191
    Abstract: Methods and systems for predictive control of an autonomous vehicle are described. Predictions of lane centeredness and road angle are generated based on data collected by sensors on the autonomous vehicle and are combined to determine a state of the vehicle that are then used to generate vehicle actions for steering control and speed control of the autonomous vehicle.
    Type: Grant
    Filed: July 6, 2020
    Date of Patent: March 19, 2024
    Assignee: HUAWEI TECHNOLOGIES CO., LTD.
    Inventor: Daniel Mark Graves
  • Publication number: 20230202477
    Abstract: A method or system for adaptive vehicle spacing, including determining a current state of a vehicle based on sensor data captured by sensors of the vehicle; for each possible action in a set of possible actions: (i) predicting based on the current vehicle state a future state for the vehicle, and (ii) predicting, based on the current vehicle state a first zone future safety value corresponding to a first safety zone of the vehicle; and selecting, based on the predicted future states and first zone future safety values for each of the possible actions in the set, a vehicle action.
    Type: Application
    Filed: November 28, 2022
    Publication date: June 29, 2023
    Inventors: Daniel Mark Graves, Kasra Rezaee
  • Patent number: 11605026
    Abstract: Methods and systems are described for support policy learning in an agent of a robot. A general value function (GVF) is learned for a main policy, where the GVF represents future performance of the agent executing the main policy for a given state of the environment. A master policy selects an action based on the predicted accumulated success value received from the general value function. When the predicted accumulated success value is an acceptable value, the action selected by the master policy is execution of the main policy. When the predicted accumulated success value is not an acceptable value, the master action causes a support policy to be learned. The support policy generates a support action to be performed which causes the robot to transition from to a new state where the predicted accumulated success value has an acceptable value.
    Type: Grant
    Filed: May 15, 2020
    Date of Patent: March 14, 2023
    Assignee: Huawei Technologies Co. Ltd.
    Inventors: Daniel Mark Graves, Jun Jin, Jun Luo
  • Patent number: 11511745
    Abstract: A method or system for adaptive vehicle spacing, including determining a current state of a vehicle based on sensor data captured by sensors of the vehicle; for each possible action in a set of possible actions: (i) predicting based on the current vehicle state a future state for the vehicle, and (ii) predicting, based on the current vehicle state a first zone future safety value corresponding to a first safety zone of the vehicle; and selecting, based on the predicted future states and first zone future safety values for each of the possible actions in the set, a vehicle action.
    Type: Grant
    Filed: April 27, 2018
    Date of Patent: November 29, 2022
    Assignee: HUAWEI TECHNOLOGIES CO., LTD.
    Inventors: Daniel Mark Graves, Kasra Rezaee
  • Patent number: 11511413
    Abstract: A robot that includes an RL agent that is configured to learn a policy to maximize the cumulative reward of a task, to determine one or more features that are minimally correlated with each other. The features are then used as pseudo-rewards, called feature rewards, where each feature reward corresponds to an option policy, or skill, the RL agent learns to maximize. In an example, the RL agent is configured to select the most relevant features to learn respective option policies from. The RL agent is configured to, for each of the selected features, learn the respective option policy that maximizes the respective feature reward. Using the learned option policies, the RL agent is configured to learn a new (second) policy for a new (second) task that can choose from any of the learned option policies or actions available to the RL agent.
    Type: Grant
    Filed: June 12, 2020
    Date of Patent: November 29, 2022
    Assignee: Huawei Technologies Co. Ltd.
    Inventors: Borislav Mavrin, Daniel Mark Graves
  • Patent number: 11364936
    Abstract: A method or system for controlling safety of both an ego vehicle and social objects in an environment of the ego vehicle, comprising: receiving data representative of at least one social object and determining a current state of the ego vehicle based on sensor data; predicting an ego safety value corresponding to the ego vehicle, for each possible behavior action in a set of possible behavior actions, based on the current state; predicting a social safety value corresponding to the at least one social object in the environment of the ego vehicle, based on the current state, for each possible behavior action; and selecting a next behavior action for the ego vehicle, based on the ego safety values, the social safety values, and one or more target objectives for the ego vehicle.
    Type: Grant
    Filed: February 27, 2020
    Date of Patent: June 21, 2022
    Assignee: Huawei Technologies Co., Ltd.
    Inventor: Daniel Mark Graves
  • Publication number: 20220089079
    Abstract: A system and method for transporting building materials is provided. The system generally comprises end units and interior units having bolster assemblies and cradles. The bolster assembly preferably comprises a beam, D-rings, and a plurality of attachment plates configured to allow for the attachment of a cradle having a shape configured to securely fit the shape of a building material. Two or more end units of the system may be secured to a transport vehicle, such as a railcar, and building materials may be placed within the cradles, thus stabilizing the building materials and protecting them from damage. Two or more interior units may then be used to create layers of building materials before being topped with two or more end units. A securing member may be used to fasten the end units and interior units to one another, which secures the one or more layers of building materials to the transport vehicle in a way that will prevent shifting of the building materials during transport.
    Type: Application
    Filed: September 17, 2021
    Publication date: March 24, 2022
    Applicant: Performance Specialized Services, LLC
    Inventors: Matthew Roberts, Mark Graves
  • Publication number: 20210387330
    Abstract: A robot that includes an RL agent that is configured to learn a policy to maximize the cumulative reward of a task, to determine one or more features that are minimally correlated with each other. The features are then used as pseudo-rewards, called feature rewards, where each feature reward corresponds to an option policy, or skill, the RL agent learns to maximize. In an example, the RL agent is configured to select the most relevant features to learn respective option policies from. The RL agent is configured to, for each of the selected features, learn the respective option policy that maximizes the respective feature reward. Using the learned option policies, the RL agent is configured to learn a new (second) policy for a new (second) task that can choose from any of the learned option policies or actions available to the RL agent.
    Type: Application
    Filed: June 12, 2020
    Publication date: December 16, 2021
    Inventors: Borislav MAVRIN, Daniel Mark GRAVES
  • Publication number: 20210357782
    Abstract: Methods and systems are described for support policy learning in an agent of a robot. A general value function (GVF) is learned for a main policy, where the GVF represents future performance of the agent executing the main policy for a given state of the environment. A master policy selects an action based on the predicted accumulated success value received from the general value function. When the predicted accumulated success value is an acceptable value, the action selected by the master policy is execution of the main policy. When the predicted accumulated success value is not an acceptable value, the master action causes a support policy to be learned. The support policy generates a support action to be performed which causes the robot to transition from to a new state where the predicted accumulated success value has an acceptable value.
    Type: Application
    Filed: May 15, 2020
    Publication date: November 18, 2021
    Inventors: Daniel Mark GRAVES, Jun JIN, Jun LUO
  • Publication number: 20210004006
    Abstract: Methods and systems for predictive control of an autonomous vehicle are described. Predictions of lane centeredness and road angle are generated based on data collected by sensors on the autonomous vehicle and are combined to determine a state of the vehicle that are then used to generate vehicle actions for steering control and speed control of the autonomous vehicle.
    Type: Application
    Filed: July 6, 2020
    Publication date: January 7, 2021
    Inventor: Daniel Mark GRAVES
  • Publication number: 20200276988
    Abstract: A method or system for controlling safety of both an ego vehicle and social objects in an environment of the ego vehicle, comprising: receiving data representative of at least one social object and determining a current state of the ego vehicle based on sensor data; predicting an ego safety value corresponding to the ego vehicle, for each possible behavior action in a set of possible behavior actions, based on the current state; predicting a social safety value corresponding to the at least one social object in the environment of the ego vehicle, based on the current state, for each possible behavior action; and selecting a next behavior action for the ego vehicle, based on the ego safety values, the social safety values, and one or more target objectives for the ego vehicle.
    Type: Application
    Filed: February 27, 2020
    Publication date: September 3, 2020
    Inventor: Daniel Mark GRAVES
  • Publication number: 20190329772
    Abstract: A method or system for adaptive vehicle spacing, including determining a current state of a vehicle based on sensor data captured by sensors of the vehicle; for each possible action in a set of possible actions: (i) predicting based on the current vehicle state a future state for the vehicle, and (ii) predicting, based on the current vehicle state a first zone future safety value corresponding to a first safety zone of the vehicle; and selecting, based on the predicted future states and first zone future safety values for each of the possible actions in the set, a vehicle action.
    Type: Application
    Filed: April 27, 2018
    Publication date: October 31, 2019
    Inventors: Daniel Mark Graves, Kasra Rezaee
  • Publication number: 20140058782
    Abstract: An integrated scientific research environment includes a method and system of conducting a standardized, collaborative scientific research project. Researchers utilize the integrated scientific research environment to conduct experiments in which information is generated, analyzed, verified, and published in a distributed computing infrastructure that allows for multiple users to work together on experiments and scientific research projects. Multiple modules create a literature review and a research protocol, enable verification and analysis of experiment results, and prepare results for publication to add to existing knowledge in the field of the experiments conducted.
    Type: Application
    Filed: August 22, 2012
    Publication date: February 27, 2014
    Inventor: MARK GRAVES, JR.
  • Patent number: 6546071
    Abstract: An apparatus comprises a source of penetrating radiation, a detector for that radiation, a sample container, and a stage between the source and the detector for supporting the sample container, wherein the sample container includes a data storage element and the apparatus includes a reader for that data storage element, the reader being connected to a control means adapted to control the apparatus on the basis of the content of the data storage element. Thus, by including information in the data storage element relating to the nature of the sample, the apparatus can be tuned to that type or class of sample and more reliable results obtained. A suitable data storage element is a bar code, and a suitable sample container is a tray.
    Type: Grant
    Filed: May 23, 2001
    Date of Patent: April 8, 2003
    Assignee: Spectral Fusion Technologies Limited
    Inventor: Mark Graves
  • Publication number: 20020012419
    Abstract: An apparatus comprises a source of penetrating radiation, a detector for that radiation, a sample container, and a stage between the source and the detector for supporting the sample container, wherein the sample container includes a data storage element and the apparatus includes a reader for that data storage element, the reader being connected to a control means adapted to control the apparatus on the basis of the content of the data storage element. Thus, by including information in the data storage element relating to the nature of the sample, the apparatus can be tuned to that type or class of sample and more reliable results obtained. A suitable data storage element is a bar code, and a suitable sample container is a tray.
    Type: Application
    Filed: May 23, 2001
    Publication date: January 31, 2002
    Inventor: Mark Graves