Patents by Inventor Fabio Tozeto Ramos

Fabio Tozeto Ramos 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).

  • Publication number: 20250073897
    Abstract: Apparatuses, systems, and techniques to determine a trajectory of an object along a path. In at least one embodiment, one or more path signatures are used to identify one or more actions to be performed by an object to track a reference path.
    Type: Application
    Filed: September 5, 2023
    Publication date: March 6, 2025
    Inventors: Motoya Ohnishi, Iretiayo Akinola, Ajay Uday Mandlekar, Jie Xu, Fabio Tozeto Ramos
  • Publication number: 20250010475
    Abstract: Apparatuses, systems, and techniques to identify at least one physical characteristic of materials from computer simulations of manipulations of materials. In at least one embodiment, physical characteristics are determined by comparing measured statistics of observed manipulations to simulations of manipulations using a simulator trained with a likelihood-free inference engine.
    Type: Application
    Filed: September 17, 2024
    Publication date: January 9, 2025
    Inventors: Carolyn Linjon Chen, Yashraj Shyam Narang, Fabio Tozeto Ramos, Dieter Fox
  • Patent number: 12122053
    Abstract: Apparatuses, systems, and techniques to identify at least one physical characteristic of materials from computer simulations of manipulations of materials. In at least one embodiment, physical characteristics are determined by comparing measured statistics of observed manipulations to simulations of manipulations using a simulator trained with a likelihood-free inference engine.
    Type: Grant
    Filed: June 29, 2020
    Date of Patent: October 22, 2024
    Assignee: NVIDIA CORPORATION
    Inventors: Carolyn Linjon Chen, Yashraj Shyam Narang, Fabio Tozeto Ramos, Dieter Fox
  • Publication number: 20240338598
    Abstract: One embodiment of a method for generating simulation data to train a machine learning model includes generating a plurality of simulation environments based on a user input, and for each simulation environment included in the plurality of simulation environments: generating a plurality of tasks for a robot to perform within the simulation environment, performing one or more operations to determine a plurality of robot trajectories for performing the plurality of tasks, and generating simulation data for training a machine learning model by performing one or more operations to simulate the robot moving within the simulation environment according to the plurality of trajectories.
    Type: Application
    Filed: March 15, 2024
    Publication date: October 10, 2024
    Inventors: Caelan Reed GARRETT, Fabio TOZETO RAMOS, Iretiayo AKINOLA, Alperen DEGIRMENCI, Clemens EPPNER, Dieter FOX, Tucker Ryer HERMANS, Ajay Uday MANDLEKAR, Arsalan MOUSAVIAN, Yashraj Shyam NARANG, Rowland Wilde O'FLAHERTY, Balakumar SUNDARALINGAM, Wei YANG
  • Patent number: 12109701
    Abstract: A robot is controlled using a combination of model-based and model-free control methods. In some examples, the model-based method uses a physical model of the environment around the robot to guide the robot. The physical model is oriented using a perception system such as a camera. Characteristics of the perception system may be are used to determine an uncertainty for the model. Based at least in part on this uncertainty, the system transitions from the model-based method to a model-free method where, in some embodiments, information provided directly from the perception system is used to direct the robot without reliance on the physical model.
    Type: Grant
    Filed: February 3, 2020
    Date of Patent: October 8, 2024
    Assignee: NVIDIA Corporation
    Inventors: Jonathan Tremblay, Dieter Fox, Michelle Lee, Carlos Florensa, Nathan Donald Ratliff, Animesh Garg, Fabio Tozeto Ramos
  • Publication number: 20240300099
    Abstract: One embodiment of a method for training a machine learning model to control a robot includes causing a model of the robot to move within a simulation based on one or more outputs of the machine learning model, computing an error within the simulation, computing at least one of a reward or an observation based on the error, and updating one or more parameters of the machine learning model based on the at least one of a reward or an observation.
    Type: Application
    Filed: October 18, 2023
    Publication date: September 12, 2024
    Inventors: Bingjie TANG, Yashraj Shyam NARANG, Dieter FOX, Fabio TOZETO RAMOS
  • Publication number: 20240300100
    Abstract: One embodiment of a method for controlling a robot includes receiving sensor data indicating a state of the robot, generating an action based on the sensor data and a trained machine learning model, computing a target state of the robot based on the action and a previous target state of the robot, and causing the robot to move based on the target state of the robot.
    Type: Application
    Filed: October 19, 2023
    Publication date: September 12, 2024
    Inventors: Yashraj Shyam NARANG, Ankur HANDA, Karl VAN WYK, Dieter FOX, Michael Andres LIN, Fabio TOZETO RAMOS
  • Publication number: 20240184291
    Abstract: Apparatuses, systems, and techniques to perform inference to determine a trajectory based at least in part on a loss function including a cost associated with an amount of divergence between a set of terminal states and a set of goal states within a goal region.
    Type: Application
    Filed: March 13, 2023
    Publication date: June 6, 2024
    Inventors: Tucker Ryer Hermans, Jana Pavlasek, Fabio Tozeto Ramos
  • Publication number: 20240131706
    Abstract: Apparatuses, systems, and techniques to perform collision-free motion generation (e.g., to operate a real-world or virtual robot). In at least one embodiment, at least a portion of the collision-free motion generation is performed in parallel.
    Type: Application
    Filed: May 22, 2023
    Publication date: April 25, 2024
    Inventors: Balakumar Sundaralingam, Siva Kumar Sastry Hari, Adam Harper Fishman, Caelan Reed Garrett, Alexander James Millane, Elena Oleynikova, Ankur Handa, Fabio Tozeto Ramos, Nathan Donald Ratliff, Karl Van Wyk, Dieter Fox
  • Patent number: 11958529
    Abstract: A framework for offline learning from a set of diverse and suboptimal demonstrations operates by selectively imitating local sequences from the dataset. At least one embodiment recovers performant policies from large manipulation datasets by decomposing the problem into a goal-conditioned imitation and a high-level goal selection mechanism.
    Type: Grant
    Filed: August 20, 2020
    Date of Patent: April 16, 2024
    Assignee: NVIDIA CORPORATION
    Inventors: Ajay Uday Mandlekar, Fabio Tozeto Ramos, Byron Boots, Animesh Garg, Dieter Fox
  • Patent number: 11941899
    Abstract: Apparatuses, systems, and techniques generate poses of an object based on image data of the object obtained from a first viewpoint of the object and a second viewpoint of the object. The poses can be evaluated to determine a portion of the image data usable by an estimator to generate a pose of the object.
    Type: Grant
    Filed: May 26, 2021
    Date of Patent: March 26, 2024
    Assignee: NVIDIA Corporation
    Inventors: Jonathan Tremblay, Fabio Tozeto Ramos, Yuke Zhu, Anima Anandkumar, Guanya Shi
  • Patent number: 11931909
    Abstract: Apparatuses, systems, and techniques generate poses of an object based on data of the object observed from a first viewpoint and a second viewpoint. The poses can be evaluated to determine a portion of the data usable by an estimator to generate a pose of the object.
    Type: Grant
    Filed: May 26, 2021
    Date of Patent: March 19, 2024
    Assignee: NVIDIA Corporation
    Inventors: Jonathan Tremblay, Fabio Tozeto Ramos, Yuke Zhu, Anima Anandkumar, Guanya Shi
  • Publication number: 20240037367
    Abstract: Apparatuses, systems, and techniques to infer a sequence of actions to perform using one or more neural networks trained, at least in part, by optimizing a probability distribution function using a cost function, wherein the probability distribution represents different sequences of actions that can be performed. In at least one embodiment, a model predictive control problem is formulated as a Bayesian inference task to infer a set of solutions.
    Type: Application
    Filed: April 12, 2023
    Publication date: February 1, 2024
    Inventors: Alexander Conrad Lambert, Adam Harper Fishman, Dieter Fox, Byron Boots, Fabio Tozeto Ramos
  • Publication number: 20230405820
    Abstract: Apparatuses, systems, and techniques to generate a predicted outcome of an object resulting from a robotic component applying a force. In at least one embodiment, a predicted outcome of an object resulting from a robotic component applying a force is generated based on, for example, a neural network.
    Type: Application
    Filed: June 12, 2023
    Publication date: December 21, 2023
    Inventors: Isabella Huang, Yashraj Narang, Tucker Ryer Hermans, Fabio Tozeto Ramos, Ankur Handa, Miles Andrew Macklin, Dieter Fox
  • Publication number: 20230398686
    Abstract: Apparatuses, systems, and techniques to update a machine learning model associated with an object. In at least one embodiment, the machine learning model is updated based at least in part on, for example, one or more distributions associated with the machine learning model.
    Type: Application
    Filed: February 24, 2023
    Publication date: December 14, 2023
    Inventors: Fabio Tozeto Ramos, Animesh Garg, Krishna Murthy Jatavallabhula, Miles Macklin
  • Publication number: 20230169329
    Abstract: Systems and methods related to incorporating uncertain inputs into a neural network are described herein. A distribution is obtained and processed by a Reproducing Kernel Hilbert Space (RKHS) module to generate an embedding that represents the distribution. The features of the embedding may correspond to a number of Random Fourier Features (RFFs). The embedding can be added to additional features to form an aggregate input for the neural network. The neural network then processes the aggregate input to generate an output based on, at least in part, the embedding of the distribution. In some embodiments, a simulation can be run to generate a distribution for a feature, where each simulator instance generates a different sample for the feature over a plurality of time steps of the simulation. In some embodiments, the output neural network can be used to control robotic systems, vehicles, or other systems.
    Type: Application
    Filed: December 1, 2021
    Publication date: June 1, 2023
    Inventors: Fabio Tozeto Ramos, Rika Antonova, Ankur Handa, Dieter Fox
  • Patent number: 11645492
    Abstract: Apparatuses, systems, and techniques to infer a sequence of actions to perform using one or more neural networks trained, at least in part, by optimizing a probability distribution function using a cost function, wherein the probability distribution represents different sequences of actions that can be performed. In at least one embodiment, a model predictive control problem is formulated as a Bayesian inference task to infer a set of solutions.
    Type: Grant
    Filed: April 28, 2020
    Date of Patent: May 9, 2023
    Assignee: NVIDIA Corporation
    Inventors: Alexander Conrad Lambert, Adam Harper Fishman, Dieter Fox, Byron Boots, Fabio Tozeto Ramos
  • Publication number: 20220382246
    Abstract: A differentiable simulator for simulating the cutting of soft materials by a cutting instrument is provided. In accordance with one aspect of the disclosure, a method for simulating a cutting operation includes: receiving a mesh for an object, modifying the mesh to add virtual nodes associated with a predefined cutting plane, optimizing a set of parameters associated with a simulator based on ground-truth data, and running a simulation via the simulator to generate outputs that include trajectories associated with a cutting instrument. Optimizing the set of parameters can include performing inference based on a set of ground-truth trajectories captured using sensors to measure real-world cutting operations. The inference techniques can employ stochastic gradient descent, stochastic gradient Langevin dynamics, or a Bayesian approach. In an embodiment, the simulator can be utilized to generate control signals for a robot based on the simulated trajectories.
    Type: Application
    Filed: April 28, 2022
    Publication date: December 1, 2022
    Inventors: Eric Heiden, Fabio Tozeto Ramos, Yashraj Narang, Miles Macklin, Dieter Fox, Animesh Garg, Mike Skolones
  • Publication number: 20220383019
    Abstract: Apparatuses, systems, and techniques generate poses of an object based on image data of the object obtained from a first viewpoint of the object and a second viewpoint of the object. The poses can be evaluated to determine a portion of the image data usable by an estimator to generate a pose of the object.
    Type: Application
    Filed: May 26, 2021
    Publication date: December 1, 2022
    Inventors: Jonathan Tremblay, Fabio Tozeto Ramos, Yuke Zhu, Anima Anandkumar, Guanya Shi
  • Publication number: 20220379484
    Abstract: Apparatuses, systems, and techniques generate poses of an object based on data of the object observed from a first viewpoint and a second viewpoint. The poses can be evaluated to determine a portion of the data usable by an estimator to generate a pose of the object.
    Type: Application
    Filed: May 26, 2021
    Publication date: December 1, 2022
    Inventors: Jonathan Tremblay, Fabio Tozeto Ramos, Yuke Zhu, Anima Anandkumar, Guanya Shi