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：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.