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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-2026103368-A1, on 16 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-2026-000000524

Continual proactive learning for autonomous robot agents

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Classification
Lifting, Positioning & Manipulation › Lift Tables and Positioners
Original source
US-2026103368-A1, the United States Patent and Trademark Office, 16 April 2026
Document SHA-256
fe4edb03387292534adb9758dbfe25df92c53fd83882828dbed55b31c7037f4f

Abstract

A robot agent ( 102 ) includes an electro-mechanical subsystem ( 202 ), a sensor subsystem ( 204 ) having one or more sensors, and a computer hardware subsystem ( 206 ) to execute one or more sets of executable instructions ( 212, 214, 216, 218, 220 ). The one or more sets of executable instructions manipulate the robot agent to predict an action to be implemented by the robot agent in performing a task ( 112 ) and predict whether the robot agent will fail in performing the action. The one or more sets of executable instructions further manipulate the robot agent to, responsive to predicting the robot agent will fail in performing the action, obtain guidance input ( 116 ) for the first action from at least one guidance source, the guidance input representing guidance for performing the action by the robot agent, and manipulate the electro-mechanical subsystem to perform the action using the guidance input.

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Citation

OpenTechnical Publication OT-2026-000000524, “Continual proactive learning for autonomous robot agents,” published September 15, 2026 at 19:03:01 UTC, https://opentechnical.org/p/OT-2026-000000524.

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