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Self-Evolving AI for Humanoids: Mechanisms, Safety, and Evaluation of Post-Deployment Self-Improvement — arXiv:2609.13236

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.13236
License (as archived): CC0-1.0
Archived: 2026-10-15T23:17:44.712Z
SHA-256 of the archived text: e590e1a238de6f6c3fe37c7f94f95716319e8316c078206ae38b93386be7ac3d
Stable URL of this page: https://metraipolis.com/documento/dc_792eb5ad7a67

How to cite

Self-Evolving AI for Humanoids: Mechanisms, Safety, and Evaluation of Post-Deployment Self-Improvement — arXiv:2609.13236 — arXiv (en), https://arxiv.org/abs/2609.13236 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_792eb5ad7a67 (Metraipolis archive dc_792eb5ad7a67, sha256 e590e1a238de6f6c3fe37c7f94f95716319e8316c078206ae38b93386be7ac3d)

BibTeX

@misc{dc792eb5ad7a67,
  title = {Self-Evolving AI for Humanoids: Mechanisms, Safety, and Evaluation of Post-Deployment Self-Improvement — arXiv:2609.13236},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.13236},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_792eb5ad7a67; sha256 e590e1a238de6f6c3fe37c7f94f95716319e8316c078206ae38b93386be7ac3d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.13236. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-09-15 in cs.DC).
Categorie arXiv: Robotics (cs.RO) ; Distributed, Parallel, and Cluster Computing (cs.DC).
Pagina del lavoro: https://arxiv.org/abs/2609.13236 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T22:54:13.004Z dalla pagina https://arxiv.org/catchup/cs.DC/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).