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Posterior Optimization with Clipped Objective for Bridging Efficiency and Stability in Generative Policy Learning
Yuhui Chen, Haoran Li, Zhennan Jiang +4
Expressive generative models have advanced robotic manipulation by capturing complex, multi-modal action distributions over temporally extended trajectories. However, fine-tuning t…
Dual-Granularity Contrastive Reward via Generated Episodic Guidance for Efficient Embodied RL
Xin Liu, Yixuan Li, Yuhui Chen +3
Designing suitable rewards poses a significant challenge in reinforcement learning (RL), especially for embodied manipulation. Trajectory success rewards are suitable for human jud…
WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL
Zhennan Jiang, Shangqing Zhou, Yutong Jiang +11
Reinforcement learning (RL) promises to unlock capabilities beyond imitation learning for Vision--Language--Action (VLA) models, but its requirement for massive real-world interact…
Towards Long-Lived Robots: Continual Learning VLA Models via Reinforcement Fine-Tuning
Yuan Liu, Haoran Li, Shuai Tian +5
Pretrained on large-scale and diverse datasets, VLA models demonstrate strong generalization and adaptability as general-purpose robotic policies. However, Supervised Fine-Tuning (…