Self-adapting Robotic Agents through Online Continual Reinforcement Learning with World Model Feedback
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...
| Authors | Fabian Domberg, Georg Schildbach |
|---|---|
| Primary category | cs.RO |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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