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Intrinsic Robot Rewarding: Reusing VLA Representations for Autonomous Evaluation and Policy Improvement

arXiv · AI, language, vision and robotics · article · Sep 15, 2026 · UTC

Vision-language-action (VLA) systems already bring together two valuable resources for robot learning: rich visual representations and demonstrations of successful task execution. Intrinsic Robot Rewarding (IRR) proposes to use these resources for a second, complementary purpose: evaluating the robot's own outcomes and providing feedback for policy improvement. Successful demonstration endpoints define task-specific references, and the policy's frozen visual encoder provides the feature space in which new outcomes are assessed. The core reward mechanism adds a reference bank and a scoring oper

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Evidence & attribution

arXiv · AI, language, vision and robotics · 2026-09-15T12:42:17.000Z

First collected: 2026-09-20T08:40:59.508Z. This is not the publication date.