[Submitted on 2 Sep 2026]
Title:Self-Evolving AI for Humanoids: Mechanisms, Safety, and Evaluation of Post-Deployment Self-Improvement
View a PDF of the paper titled Self-Evolving AI for Humanoids: Mechanisms, Safety, and Evaluation of Post-Deployment Self-Improvement, by Loc X. Nguyen and 5 other authors
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Abstract: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 existing studies focus on disembodied software agents, this survey examines how self-evolution changes when an agent has a physical body. We first define self-evolution for humanoids and represent a deployed robot using a state tuple that includes its policy, perception, memory, workflow, and body. This state is updated by an evolution operator in a slow outer loop with a lifelong objective. We then organize the literature into four complementary mechanisms of self-evolution, presented in increasing order of autonomy: self-learning, self-adaptation, self-optimization, and self-generation. Since changes to a humanoid can introduce physical hazards, we treat safety and uncertainty as key design dimensions of the evolution operator, and further formulate admissible evolution as a constraint enforced by a world-model verification gate within a human-oversight envelope. Finally, we present that evaluation should track the robot's evolving trajectory rather than a fixed checkpoint, and we identify the lack of a benchmark designed specifically for self-evolving humanoids. Moreover, we outline open challenges spanning AI algorithms, on-board systems, and governance.
Comments: The paper includes 30 pages, 9 figures, 5 tables, and is considered for publication
Subjects:
Robotics (cs.RO); Distributed, Parallel, and Cluster Computing (cs.DC)
Cite as: arXiv:2609.13236 [cs.RO]
(or arXiv:2609.13236v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.13236
arXiv-issued DOI via DataCite
Submission history
From: Loc Nguyen [view email] [v1] Wed, 2 Sep 2026 13:38:43 UTC (7,930 KB)
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