Nvidia Corporation

Oracle International Corporation

Ankur Handa

Dieter Fox

Miles Macklin

Stan Birchfield

Yashraj Shyam Narang

Iretiayo Akinola

Fabio Tozeto Ramos

Wei Yang

Yu-Wei Chao

Yevgen Chebotar

Balakumar Sundaralingam

Nathan Donald Ratliff

Tucker Ryer Hermans

Isabella Huang

Karl Van Wyk

Viktor Makoviichuk

Byron Boots

Jagdesh Veerapandian

Vijay Pandya

Xiaojun Chen

Qian Wan

Jan Issac

Venkat Srinivas

Ramrajesh Ravichandran

Alexander Lambert

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.

Predicting grasp outcomes

A technique for training a neural network, including generating a plurality of input vectors based on a first plurality of task demonstrations associated with a first robot performing a first task in a simulated environment, wherein each input vector included in the plurality of input vectors specifies a sequence of poses of an end-effector of the first robot, and training the neural network to generate a plurality of output vectors based on the plurality of input vectors. Another technique for generating a task demonstration, including generating a simulated environment that includes a robot and at least one object, causing the robot to at least partially perform a task associated with the at least one object within the simulated environment based on a first output vector generated by a trained neural network, and recording demonstration data of the robot at least partially performing the task within the simulated environment.

Neural networks to generate robotic task demonstrations

A human pilot controls a robotic arm and gripper by simulating a set of desired motions with the human hand. In at least one embodiment, one or more images of the pilot's hand are captured and analyzed to determine a set of hand poses. In at least one embodiment, the set of hand poses is translated to a corresponding set of robotic-gripper poses. In at least one embodiment, a set of motions is determined that perform the set of robotic-gripper poses, and the robot is directed to perform the set of motions.

Vision-based teleoperation of dexterous robotic system

A machine-learning control system is trained to perform a task using a simulation. The simulation is governed by parameters that, in various embodiments, are not precisely known. In an embodiment, the parameters are specified with an initial value and expected range. After training on the simulation, the machine-learning control system attempts to perform the task in the real world. In an embodiment, the results of the attempt are compared to the expected results of the simulation, and the parameters that govern the simulation are adjusted so that the simulated result matches the real-world attempt. In an embodiment, the machine-learning control system is retrained on the updated simulation. In an embodiment, as additional real-world attempts are made, the simulation parameters are refined and the control system is retrained until the simulation is accurate and the control system is able to successfully perform the task in the real world.

Simulation of tasks using neural networks

Embodiments of the invention provide systems and methods for managing seed data in a computing system (e.g., middleware computing system). A disclosed server computer may include a processor and a memory coupled with and readable by the processor and storing therein a set of instructions which, when executed by the processor, cause the processor to perform a method. The method may include obtaining first input data via a graphical interface. The first input data indicates a first memory storage location of seed data. The seed data comprises data to initialize an application for operation. The method further includes accessing the seed data from the first memory storage location based on the first input data. The method includes storing data based on the seed data to a second memory storage location.

Managing seed data

Growing community of inventors

Seattle, WA, United States of America

Ankur Handa