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This is a record of a document published elsewhere, first published by the United States Patent and Trademark Office, as US-2026097489-A1, on 9 April 2026. OpenTechnical did not first disclose it. The identifier, hashes and timestamps below record what this library served and when, not when the material became public. View the original.

OT-2026-000000972

Training for unified neural motion control in robotics systems and applications

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Classification
Robotics & Automation › Robot Architectures
Inventors
FAN LINXI, HE TAIRAN, JIANG ZHENYU, KAUTZ JAN, Lin toru, LUO ZHENGYI, WANG XIAOLONG, XIAO WENLI, XU ZHENJIA, ZHU YUKE
Assignee
NVIDIA CORP
Original source
US-2026097489-A1, the United States Patent and Trademark Office, 9 April 2026
Document SHA-256
3d898163ccab6f780d53870ec83e0d9a03ae81893d58273a749a5d3cf12562cd

Abstract

In various examples, a technique for neural motion control includes applying a plurality of masks to a plurality of goal state attributes for an articulated object to produce one or more masked goal state attributes. The technique also includes generating, via execution of a machine learning model, one or more actions based at least on the one or more masked goal state attributes. The technique further includes updating one or more parameters of the machine learning model based at least on the one or more actions and one or more reference actions for the articulated object to produce a trained machine learning model.

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Citation

OpenTechnical Publication OT-2026-000000972, “Training for unified neural motion control in robotics systems and applications,” published September 15, 2026 at 19:03:40 UTC, https://opentechnical.org/p/OT-2026-000000972.

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This record reproduces a document published elsewhere and is provided for reference and study. Rights in the original remain with its owner. See the publication policy.