SOURCE-LINKED INTELLIGENCE
Self-Evolving AI for Humanoids: Mechanisms, Safety, and Evaluation of Post-Deployment Self-Improvement
Humanoid robots are becoming an important part of embodied artificial intelligence, driven by advances in reinforcement learning for locomotion, world models for prediction, and vision-language-action models for general control. However, most of these systems remain static after deployment. A policy is trained offline for a fixed objective and then frozen, even though the tasks, environments, and robot bodies keep drifting over time. An emerging paradigm of self-evolving agents aims to address this problem by allowing systems to improve from their own post-deployment experience. Since most exi
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-02T13:38:43.000Z
First collected: 2026-09-21T05:32:15.665Z. This is not the publication date.