# Self-Evolving AI for Humanoids: Mechanisms, Safety, and Evaluation of Post-Deployment Self-Improvement arXiv: https://arxiv.org/abs/2609.13236 ยท HTML: https://mlsanity.com/paper/2609.13236/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Placed in World models for navigation, one of 11 directions in World models. - Builds on 43 of its 174 references that are in the map. - First posted on arXiv 2 Sep 2026. ## Placed in - World models / [World models for navigation](https://mlsanity.com/wm/world-models-for-navigation/) - Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/) ## Builds on (most cited first, 30 of 43) - [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017) - [World Models](https://mlsanity.com/paper/1803.10122/) (2018) - [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016) - [GR00T N1: An Open Foundation Model for Generalist Humanoid Robots](https://mlsanity.com/paper/2503.14734/) (2025) - [Unified Video Action Model](https://mlsanity.com/paper/2503.00200/) (2025) - [BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion](https://mlsanity.com/paper/2508.08241/) (2025) - [Expressive Whole-Body Control for Humanoid Robots](https://mlsanity.com/paper/2402.16796/) (2024) - [Natural Emergent Misalignment from Reward Hacking in Production RL](https://mlsanity.com/paper/2511.18397/) (2025) - [ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills](https://mlsanity.com/paper/2502.01143/) (2025) - [SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control](https://mlsanity.com/paper/2511.07820/) (2025) - [Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real Transfer](https://mlsanity.com/paper/2404.05695/) (2024) - [Humanoid-VLA: Towards Universal Humanoid Control with Visual Integration](https://mlsanity.com/paper/2502.14795/) (2025) - [Building Self-Evolving Agents via Experience-Driven Lifelong Learning: A Framework and Benchmark](https://mlsanity.com/paper/2508.19005/) (2025) - [RoboFAC: A Comprehensive Framework for Robotic Failure Analysis and Correction](https://mlsanity.com/paper/2505.12224/) (2025) - [Self-Improving LLM Agents at Test-Time](https://mlsanity.com/paper/2510.07841/) (2025) - [EVOLVE-VLA: Test-Time Training from Environment Feedback for Vision-Language-Action Models](https://mlsanity.com/paper/2512.14666/) (2025) - [Eurekaverse: Environment Curriculum Generation via Large Language Models](https://mlsanity.com/paper/2411.01775/) (2024) - [Learning Sim-to-Real Humanoid Locomotion in 15 Minutes](https://mlsanity.com/paper/2512.01996/) (2025) - [A Step Toward World Models: A Survey on Robotic Manipulation](https://mlsanity.com/paper/2511.02097/) (2025) - [CycleVLA: Proactive Self-Correcting Vision-Language-Action Models via Subtask Backtracking and Minimum Bayes Risk Decoding](https://mlsanity.com/paper/2601.02295/) (2026) - [Towards Long-Lived Robots: Continual Learning VLA Models via Reinforcement Fine-Tuning](https://mlsanity.com/paper/2602.10503/) (2026) - [Self-Evolving Embodied Agents via Skill-Harness Evolution](https://mlsanity.com/paper/2608.11350/) (2026) - [Generative World Modelling for Humanoids: 1X World Model Challenge Technical Report](https://mlsanity.com/paper/2510.07092/) (2025) - [Reflection-Based Task Adaptation for Self-Improving VLA](https://mlsanity.com/paper/2510.12710/) (2025) - [GentleHumanoid: Learning Upper-body Compliance for Contact-rich Human and Object Interaction](https://mlsanity.com/paper/2511.04679/) (2025) - [Ego-Vision World Model for Humanoid Contact Planning](https://mlsanity.com/paper/2510.11682/) (2025) - [MOSAIC: Bridging the Sim-to-Real Gap in Generalist Humanoid Motion Tracking and Teleoperation with Rapid Residual Adaptation](https://mlsanity.com/paper/2602.08594/) (2026) - [Humanoid World Models: Open World Foundation Models for Humanoid Robotics](https://mlsanity.com/paper/2506.01182/) (2025) - [Hierarchical Vision-Language Planning for Multi-Step Humanoid Manipulation](https://mlsanity.com/paper/2506.22827/) (2025) - [TTT-Parkour: Rapid Test-Time Training for Perceptive Robot Parkour](https://mlsanity.com/paper/2602.02331/) (2026) ## Built on by (most cited first, 1 of 1) - [Evolutionary Safety of Recursive Self-Improving AI: Taxonomy, Risk Discovery, and Evaluation](https://mlsanity.com/paper/2609.31186/) (2026) ## Abstract (arXiv) 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.