Self-adapting Robotic Agents through Online Continual Reinforcement Learning with World Model Feedback

• URL: http://arxiv.org/abs/2603.04029v1
• Date: Wed, 04 Mar 2026 13:07:42 GMT
• Title: Self-adapting Robotic Agents through Online Continual Reinforcement Learning with World Model Feedback
• Authors: Fabian Domberg, Georg Schildbach,
• Abstract summary: This work presents a framework for online Continual Reinforcement Learning that enables automated adaptation during deployment.<n>The proposed method leverages world model prediction residuals to detect out-of-distribution events and automatically trigger finetuning.<n>The approach is validated on a variety of contemporary continuous control problems, including a quadruped robot in high-fidelity simulation.
• Score: 2.165723322157105
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As learning-based robotic controllers are typically trained offline and deployed with fixed parameters, their ability to cope with unforeseen changes during operation is limited. Biologically inspired, this work presents a framework for online Continual Reinforcement Learning that enables automated adaptation during deployment. Building on DreamerV3, a model-based Reinforcement Learning algorithm, the proposed method leverages world model prediction residuals to detect out-of-distribution events and automatically trigger finetuning. Adaptation progress is monitored using both task-level performance signals and internal training metrics, allowing convergence to be assessed without external supervision and domain knowledge. The approach is validated on a variety of contemporary continuous control problems, including a quadruped robot in high-fidelity simulation, and a real-world model vehicle. Relevant metrics and their interpretation are presented and discussed, as well as resulting trade-offs described. The results sketch out how autonomous robotic agents could once move beyond static training regimes toward adaptive systems capable of self-reflection and -improvement during operation, just like their biological counterparts.

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