Patents by Inventor Dieter Fox

Dieter Fox 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: 20240157557
    Abstract: Apparatuses, systems, and techniques to control a real-world and/or virtual device (e.g., a robot). In at least one embodiment, the device is controlled based, at least in part on, for example, one or more neural networks. Parameter values for the neural network(s) may be obtained by training the neural network(s) to control movement of a first agent with respect to at least one first target while avoiding collision with at least one stationary first holder of the at least one first target, and updating the parameter values by training the neural network(s) to control movement of a second agent with respect to at least one second target while avoiding collision with at least one non-stationary second holder of the at least one second target.
    Type: Application
    Filed: March 23, 2023
    Publication date: May 16, 2024
    Inventors: Sammy Joe Christen, Wei Yang, Claudia Perez D'Arpino, Dieter Fox, Yu-Wei Chao
  • Publication number: 20240153196
    Abstract: Apparatuses, systems, and techniques to generate an image of one or more objects. In at least one embodiment, an image of one or more objects is generated using a neural network based on, for example, a representation of a scene.
    Type: Application
    Filed: July 5, 2023
    Publication date: May 9, 2024
    Inventors: Valts Blukis, Taeyeop Lee, Jonathan Tremblay, Bowen Wen, Dieter Fox, Stanley Thomas Birchfield
  • Publication number: 20240149447
    Abstract: Apparatuses, systems, and techniques to generate a motion plan. In at least one embodiment, a motion plan is generated using a neural network based, at least in part, on a demonstration of a task.
    Type: Application
    Filed: September 7, 2023
    Publication date: May 9, 2024
    Inventors: Albert Wu, Clemens Eppner, Dieter Fox
  • 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
  • Publication number: 20240123620
    Abstract: Apparatuses, systems, and techniques to generate and select grasp proposals. In at least one embodiment, grasp proposals are generated and selected using one or more neural networks, based on, for example, a latent code corresponding to an object.
    Type: Application
    Filed: July 6, 2023
    Publication date: April 18, 2024
    Inventors: Jonathan Tremblay, Stanley Thomas Birchfield, Valts Blukis, Bowen Wen, Dieter Fox, Taeyeop Lee
  • 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
  • Publication number: 20240104831
    Abstract: One embodiment of a method for generating representations of scenes includes assigning each image included in a set of images of a scene to one or more clusters of images based on a camera pose associated with the image, and performing one or more operations to generate, for each cluster included in the one or more clusters, a corresponding three-dimensional (3D) representation of the scene based on one or more images assigned to the cluster.
    Type: Application
    Filed: June 6, 2023
    Publication date: March 28, 2024
    Inventors: Yen-Chen LIN, Valts BLUKIS, Dieter FOX, Alexander KELLER, Thomas MUELLER-HOEHNE, Jonathan TREMBLAY
  • Publication number: 20240095527
    Abstract: Systems and techniques are described related to training one or more machine learning models for use in control of a robot. In at least one embodiment, one or more machine learning models are trained based at least on simulations of the robot and renderings of such simulations—which may be performed using one or more ray tracing algorithms, operations, or techniques.
    Type: Application
    Filed: August 10, 2023
    Publication date: March 21, 2024
    Inventors: Ankur HANDA, Gavriel STATE, Arthur David ALLSHIRE, Dieter FOX, Jean-Francois Victor LAFLECHE, Jingzhou LIU, Viktor MAKOVIICHUK, Yashraj Shyam NARANG, Aleksei Vladimirovich PETRENKO, Ritvik SINGH, Balakumar SUNDARALINGAM, Karl VAN WYK, Alexander ZHURKEVICH
  • Publication number: 20240096074
    Abstract: Apparatuses, systems, and techniques are presented to identify one or more objects. In at least one embodiment, one or more neural networks can be used to identify one or more objects based, at least in part, on one or more descriptors of one or more segments of the one or more objects.
    Type: Application
    Filed: January 21, 2022
    Publication date: March 21, 2024
    Inventors: Brian Okorn, Arsalan Mousavian, Lucas Manuelli, Dieter Fox
  • Publication number: 20240095077
    Abstract: Apparatuses, systems, and techniques to generate a prompt for one or more machine learning processes. In at least one embodiment, the machine learning process(es) generate(s) a plan to perform a task (identified in the prompt) that is to be performed by an agent (real world or virtual).
    Type: Application
    Filed: March 16, 2023
    Publication date: March 21, 2024
    Inventors: Ishika Singh, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Animesh Garg, Valts Blukis
  • Patent number: 11893468
    Abstract: Apparatuses, systems, and techniques to identify a goal of a demonstration. In at least one embodiment, video data of a demonstration is analyzed to identify a goal. Object trajectories identified in the video data are analyzed with respect to a task predicate satisfied by a respective object trajectory, and with respect to motion predicate. Analysis of the trajectory with respect to the motion predicate is used to assess intentionality of a trajectory with respect to the goal.
    Type: Grant
    Filed: July 16, 2020
    Date of Patent: February 6, 2024
    Assignee: NVIDIA Corporation
    Inventors: Yu-Wei Chao, De-An Huang, Christopher Jason Paxton, Animesh Garg, Dieter Fox
  • 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: 20240009851
    Abstract: Apparatuses, systems, and techniques determine a set of grasp poses that would allow a robot to successfully grasp an object that is proximate to at least one additional object. In at least one embodiment, the set of grasp poses is modified based on a determination that at least one of the grasp poses in the set of grasp poses would interfere with at least one additional object that is proximate to the object.
    Type: Application
    Filed: August 14, 2023
    Publication date: January 11, 2024
    Inventors: Arsalan Mousavian, Clemens Eppner, Dieter Fox, Adithyavairavan Murali
  • 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
  • Patent number: 11798183
    Abstract: Apparatuses, systems, and techniques to estimate or predict depth information for image data. In at least one embodiment, depth information is predicted based at least in part on color information and geometry information associated with an image.
    Type: Grant
    Filed: March 8, 2021
    Date of Patent: October 24, 2023
    Assignee: NVIDIA Corporation
    Inventors: Luyang Zhu, Arsalan Mousavian, Yu Xiang, Dieter Fox
  • Publication number: 20230321822
    Abstract: One embodiment of a method for controlling a robot includes performing a plurality of simulations of a robot interacting with one or more objects represented by one or more signed distance functions (SDFs), where performing the plurality of simulations comprises reducing a number of contacts between the one or more objects that are being simulated, and updating one or more parameters of a machine learning model based on the plurality of simulations to generate a trained machine learning model.
    Type: Application
    Filed: December 2, 2022
    Publication date: October 12, 2023
    Inventors: Yashraj Shyam NARANG, Kier STOREY, Iretiayo AKINOLA, Dieter FOX, Kelly GUO, Ankur HANDA, Fengyun LU, Miles MACKLIN, Adam MORAVANSZKY, Philipp REIST, Gavriel STATE, Lukasz WAWRZYNIAK
  • Publication number: 20230297074
    Abstract: Approaches provide for performance of a complex (e.g., compound) task that may involve multiple discrete tasks not obvious from an instruction to perform the complex task. A set of conditions for an environment can be determined using captured image data, and the instruction analyzed to determine a set of final conditions to exist in the environment after performance of the instruction. These initial and end conditions are used to determine a sequence of discrete tasks to be performed to cause a robot or automated device to perform the instruction. This can involve use of a symbolic or visual planner in at least some embodiments, as well as a search of possible sequences of actions available for the robot or automated device. A robot can be caused to perform the sequence of discrete tasks, and feedback provided such that the sequence of tasks can be modified as appropriate.
    Type: Application
    Filed: March 17, 2022
    Publication date: September 21, 2023
    Inventors: Christopher Jason Paxton, Shagan Sah, Ratin Kumar, Dieter Fox
  • Publication number: 20230294276
    Abstract: Approaches presented herein provide for simulation of human motion for human-robot interactions, such as may involve a handover of an object. Motion capture can be performed for a hand grasping and moving an object to a location and orientation appropriate for a handover, without a need for a robot to be present or an actual handover to occur. This motion data can be used to separately model the hand and the object for use in a handover simulation, where a component such as a physics engine may be used to ensure realistic modeling of the motion or behavior. During a simulation, a robot control model or algorithm can predict an optimal location and orientation to grasp an object, and an optimal path to move to that location and orientation, using a control model or algorithm trained, based at least in part, using the motion models for the hand and object.
    Type: Application
    Filed: December 30, 2022
    Publication date: September 21, 2023
    Inventors: Yu-Wei Chao, Yu Xiang, Wei Yang, Dieter Fox, Chris Paxton, Balakumar Sundaralingam, Maya Cakmak
  • Publication number: 20230294277
    Abstract: Approaches presented herein provide for predictive control of a robot or automated assembly in performing a specific task. A task to be performed may depend on the location and orientation of the robot performing that task. A predictive control system can determine a state of a physical environment at each of a series of time steps, and can select an appropriate location and orientation at each of those time steps. At individual time steps, an optimization process can determine a sequence of future motions or accelerations to be taken that comply with one or more constraints on that motion. For example, at individual time steps, a respective action in the sequence may be performed, then another motion sequence predicted for a next time step, which can help drive robot motion based upon predicted future motion and allow for quick reactions.
    Type: Application
    Filed: June 30, 2022
    Publication date: September 21, 2023
    Inventors: Wei Yang, Balakumar Sundaralingam, Christopher Jason Paxton, Maya Cakmak, Yu-Wei Chao, Dieter Fox, Iretiayo Akinola
  • Patent number: 11745347
    Abstract: Candidate grasping models of a deformable object are applied to generate a simulation of a response of the deformable object to the grasping model. From the simulation, grasp performance metrics for stress, deformation controllability, and instability of the response to the grasping model are obtained, and the grasp performance metrics are correlated with robotic grasp features.
    Type: Grant
    Filed: March 19, 2021
    Date of Patent: September 5, 2023
    Assignee: NVIDIA CORP.
    Inventors: Isabella Huang, Yashraj Shyam Narang, Clemens Eppner, Balakumar Sundaralingam, Miles Macklin, Tucker Ryer Hermans, Dieter Fox