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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-2026091493-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-2026-000001178

Machines learning assembly tasks using pre-trained skill libraries

Published
Permanent URL
Classification
Robotics & Automation › Robot Architectures
Inventors
AKINOLA IRETIAYO, FOX DIETER, GUO YIJIE, GUPTA ABHISHEK, NARANG YASHRAJ, TANG BINGJIE
Assignee
NVIDIA CORP
Original source
US-2026091493-A1, the United States Patent and Trademark Office, 2 April 2026
Document SHA-256
2423addca98161657a27f2761099d9c0b471eecd1976eef5d81c28b94a2ff50c

Abstract

To determine the actions of a machine assembly system (e.g., robotic assembly system), a relevant skill policy can be selected from a library of skill policies. The target task can be used to determine target task information, specifically, the geometry of one or more objects (e.g., parts) of the assembly task, one or more dynamic parameters of the objects, and expert actions that are specified. These parameters can be used as input into the skill library to predict the most relevant skill policy. The target task and the source task (e.g., a potential skill policy) can be compared on the metrics of geometry, dynamics, and expert actions. The best match skill policy can be returned as the relevant skill policy. This policy can then be modified, such as fine-tuning, to improve the real-world assembly outcomes.

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Citation

OpenTechnical Publication OT-2026-000001178, “Machines learning assembly tasks using pre-trained skill libraries,” published September 15, 2026 at 19:03:58 UTC, https://opentechnical.org/p/OT-2026-000001178.

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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.