公开(公告)号：US20230271330A1

公开(公告)日：2023-08-31

申请号：US18055569

申请日：2022-11-15

Applicant: Nvidia Corporation

Inventor： Balakumar Sundaralingam ,  Pratyusha Sharma ,  Christopher Jason Paxton ,  Valts Blukis ,  Tucker Hermans ,  Dieter Fox

IPC: B25J13/00 ,  B25J9/16 ,  B25J19/02

CPC classification number: B25J13/003 ,  B25J9/1664 ,  B25J9/163 ,  B25J9/161 ,  B25J19/023

Abstract: Approaches presented herein provide for a framework to integrate human provided feedback in natural language to update a robot planning cost or value. The natural language feedback may be modeled as a cost or value associated with completing a task assigned to the robot. This cost or value may then be added to an initial task cost or value to update one or more actions to be performed by the robot. The framework can be applied to both real work and simulated environments where the robot may receive instructions, in natural language, that either provide a goal, modify an existing goal, or provide constraints to actions to achieve an existing goal.

公开(公告)号：US20220318459A1

公开(公告)日：2022-10-06

申请号：US17213062

申请日：2021-03-25

Applicant: NVIDIA Corporation

Inventor： Yashraj Shyam Narang ,  Balakumar Sundaralingam ,  Karl Van Wyk ,  Arsalan Mousavian ,  Miles Macklin ,  Dieter Fox

IPC: G06F30/23 ,  G06F30/27 ,  G06N3/08

Abstract: Apparatuses, systems, and techniques to model a tactile force sensor. In at least one embodiment, output of tactile sensor is predicted from a modeled force and shape imposed on the sensor. In at least one embodiment, a shape of the surface of the tactile sensor is determined based at least in part on electrical signals received from the sensor.

公开(公告)号：US12017352B2

公开(公告)日：2024-06-25

申请号：US17176672

申请日：2021-02-16

Applicant: NVIDIA Corporation

Inventor： Visak Chadalavada Vijay Kumar ,  David Hoeller ,  Balakumar Sundaralingam ,  Jonathan Tremblay ,  Stanley Thomas Birchfield

IPC: B25J9/02 ,  B25J9/16

CPC classification number: B25J9/023 ,  B25J9/163 ,  B25J9/1664 ,  G05B2219/39064

Abstract: Apparatuses, systems, and techniques to map coordinates in task space to a set of joint angles of an articulated robot. In at least one embodiment, a neural network is trained to map task-space coordinates to joint space coordinates of a robot by simulating a plurality of robots at various joint angles, and determining the position of their respective manipulators in task space.

公开(公告)号：US20230294277A1

公开(公告)日：2023-09-21

申请号：US17854730

申请日：2022-06-30

Applicant: Nvidia Corporation

Inventor： Wei Yang ,  Balakumar Sundaralingam ,  Christopher Jason Paxton ,  Maya Cakmak ,  Yu-Wei Chao ,  Dieter Fox ,  Iretiayo Akinola

IPC: B25J9/16 ,  G05B19/4155

CPC classification number: B25J9/1612 ,  G05B19/4155 ,  B25J9/1666 ,  B25J9/1605 ,  G05B2219/50391 ,  G05B2219/40269

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.

公开(公告)号：US20230294276A1

公开(公告)日：2023-09-21

申请号：US18148548

申请日：2022-12-30

Applicant: Nvidia Corporation

Inventor： Yu-Wei Chao ,  Yu Xiang ,  Wei Yang ,  Dieter Fox ,  Chris Paxton ,  Balakumar Sundaralingam ,  Maya Cakmak

IPC: B25J9/16

CPC classification number: B25J9/1605 ,  B25J9/163 ,  G05B2219/39001

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.

公开(公告)号：US20220134537A1

公开(公告)日：2022-05-05

申请号：US17176672

申请日：2021-02-16

Applicant: NVIDIA Corporation

Inventor： Visak Chadalavada Vijay Kumar ,  David Hoeller ,  Balakumar Sundaralingam ,  Jonathan Tremblay ,  Stanley Thomas Birchfield

IPC: B25J9/02 ,  G05B13/02 ,  B25J9/16

Abstract: Apparatuses, systems, and techniques to map coordinates in task space to a set of joint angles of an articulated robot. In at least one embodiment, a neural network is trained to map task-space coordinates to joint space coordinates of a robot by simulating a plurality of robots at various joint angles, and determining the position of their respective manipulators in task space.

公开(公告)号：US20240131706A1

公开(公告)日：2024-04-25

申请号：US18200347

申请日：2023-05-22

Applicant: NVIDIA Corporation

Inventor： 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

IPC: B25J9/16

CPC classification number: B25J9/1664

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.

公开(公告)号：US20230145208A1

公开(公告)日：2023-05-11

申请号：US17982401

申请日：2022-11-07

Applicant: NVIDIA Corporation

Inventor： Andreea Bobu ,  Balakumar Sundaralingam ,  Christopher Jason Paxton ,  Maya Cakmak ,  Wei Yang ,  Yu-Wei Chao ,  Dieter Fox

IPC: G06N20/00 ,  G06N5/04

CPC classification number: G06N20/00 ,  G06N5/04

Abstract: Apparatuses, systems, and techniques to train a machine learning model. In at least one embodiment, a first machine learning model is trained to infer a concept based on first information, training data is labeled using the first machine learning model, and a second machine learning model is trained to infer the concept using the labeled training data.

公开(公告)号：US20200301510A1

公开(公告)日：2020-09-24

申请号：US16358485

申请日：2019-03-19

Applicant: NVIDIA Corporation

Inventor： Stan Birchfield ,  Byron Boots ,  Dieter Fox ,  Ankur Handa ,  Nathan Ratliff ,  Balakumar Sundaralingam ,  Alexander Lambert

IPC: G06F3/01 ,  G01L5/22 ,  G06N3/08 ,  G06T11/00

Abstract: A computer system generates a tactile force model for a tactile force sensor by performing a number of calibration tasks. In various embodiments, the calibration tasks include pressing the tactile force sensor while the tactile force sensor is attached to a pressure gauge, interacting with a ball, and pushing an object along a planar surface. Data collected from these calibration tasks is used to train a neural network. The resulting tactile force model allows the computer system to convert signals received from the tactile force sensor into a force magnitude and direction with greater accuracy than conventional methods. In an embodiment, force on the tactile force sensor is inferred by interacting with an object, determining the motion of the object, and estimating the forces on the object based on a physical model of the object.