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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-2026091502-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-2026-000001173

Vision-language model for detecting and reasoning over failures in robotic manipulation

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Permanent URL
Classification
Robotics & Automation › Robot Architectures
Inventors
Duan Jiafei, FOX DIETER, GUO YIJIE, MANDLEKAR AJAY
Assignee
NVIDIA CORP
Original source
US-2026091502-A1, the United States Patent and Trademark Office, 2 April 2026
Document SHA-256
9dbdfe762f659f98885c0766e71f0df03a5923cdaf1bcf018f23201e8ae1e7df

Abstract

Foundation models such as VLMs and LLMs are increasingly being used to address open-world tasks in robotics. While these models excel at task execution, they often face challenges in detecting and reasoning over failures, which are skills that are crucial for navigating dynamic and complex environments. The present disclosure provides a VLM that detects and reasons about failures in robotic manipulation using natural language, where the natural language feedback can then be used for improving downstream task performance through error correction.

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Citation

OpenTechnical Publication OT-2026-000001173, “Vision-language model for detecting and reasoning over failures in robotic manipulation,” published September 15, 2026 at 19:03:58 UTC, https://opentechnical.org/p/OT-2026-000001173.

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