CRONOS: Benchmarking Non-Episodic Reinforcement Learning for Robotic Manipulation

Abstract:Reinforcement learning (RL) is promising for adapting robot policies to unstructured real-world environments. However, standard RL pipelines rely on episodic training with frequent scene resets, which is impractical for real-world deployment due to the need for substantial human intervention. While prior work has studied non-episodic RL in relatively small policies trained from scratch, its behavior when fine-tuning modern large-scale pre-trained vision-language-action (VLA) models remains underexplored. In this work, we introduce CRONOS, a simulation benchmark for RL fine-tuning of VLAs under non-episodic scene resets. CRONOS leverages high-fidelity physics simulators and adopts shared-scene multi-task settings. Using CRONOS, we show that naively fine-tuning pre-trained policies fails in non-episodic settings due to shifts in object configurations and end-effector poses; however, these challenges can be mitigated through targeted reset allocation and by addressing biases in pre-trained models. Moreover, we find that non-episodic training enhances long-horizon manipulation and improves generalization to previously unseen object configurations and task sequences. Project page: this https URL

Robotics (cs.RO); Machine Learning (cs.LG)

arXiv:2611.05080 [cs.RO]

(or arXiv:2611.05080v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2611.05080 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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