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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-2026042205-A1, on 12 February 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-2026-000002989

Object-centric diffusion policy for efficient imitation learning

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Permanent URL
Classification
Robotics & Automation › Robot Architectures
Inventors
BIRCHFIELD STANLEY THOMAS, BISWAS JOYDEEP, HSU CHENG-CHUN, NARANG YASHRAJ SHYAM, WANG XIAOLONG, WEN BOWEN, XU JIE, ZHU YUKE
Assignee
NVIDIA CORP
Original source
US-2026042205-A1, the United States Patent and Trademark Office, 12 February 2026
Document SHA-256
f9f6083d4cba0ad9ae8326733e03aa08c777ce1b44bfb35d33d43c86303214bb

Abstract

Robotic control systems that include a diffusion model configured by training on demonstration videos of tasks performed by humans, the diffusion model configured to transform a noise pattern and pose of an object manipulated in a task into a prediction of a next pose of the object in the task, and the system configured to generate an ending pose prediction for the task.

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Citation

OpenTechnical Publication OT-2026-000002989, “Object-centric diffusion policy for efficient imitation learning,” published September 15, 2026 at 19:16:35 UTC, https://opentechnical.org/p/OT-2026-000002989.

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