OpenTechnical
This is a record of a document published elsewhere, first published by the United States Patent and Trademark Office, as US-2026091496-A1, on 2 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-2026-000001164

Machine learning model for task and motion planning

Published
Permanent URL
Classification
Robotics & Automation › Robot Architectures
Inventors
BIRCHFIELD STANLEY THOMAS, TREMBLAY JONATHAN, ZHU YIFENG, ZHU YUKE
Assignee
NVIDIA CORP
Original source
US-2026091496-A1, the United States Patent and Trademark Office, 2 April 2026
Document SHA-256
0631a6552ac4a2f6fddf6acebbfa0a61d427a06b15a5fa0aa2ea4e49f63d3f47

Abstract

Apparatuses, systems, and techniques are described that solve task and motion planning problems. In at least one embodiment, a task and motion planning problem is modeled using a geometric scene graph that records positions and orientations of objects within a playfield, and a symbolic scene graph that represents states of objects within context of a task to be solved. In at least one embodiment, task planning is performed using symbolic scene graph, and motion planning is performed using a geometric scene graph.

Publication information

Plain textMarkdownManifestIntegrity record

Citation

OpenTechnical Publication OT-2026-000001164, “Machine learning model for task and motion planning,” published September 15, 2026 at 19:03:57 UTC, https://opentechnical.org/p/OT-2026-000001164.

To cite specific content, add the paragraph or figure numbers.

Terms

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.