Article

Toward Zero-Human-Intervention Autonomous Robot Learning: A Continuous Result-Driven Self-Reward and Correction Framework

2026-04-14

Abstract excerpt

<title>Abstract</title> <p>Autonomous robots operating in complex real-world environments require the ability to continuously improve their behavior without human-provided reward annotation or online intervention. However, robot actions often produce delayed and multi-factor consequences, making it difficult to correctly associate later outcomes with earlier actions and to perform reliable autonomous self-reward...

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Literature Corpus work: a93a955c-c002-5444-b2ad-92c4ba1cec56
DOI: 10.21203/rs.3.rs-9398282/v1

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