全球强化学习+VLA范式，PI*0.6背后都有这家公司技术伏笔

Core Insights - The article discusses the advancements in embodied intelligence, particularly focusing on the VLA (Vision-Language-Action) model and its integration with reinforcement learning (RL) to enhance robotic capabilities [2][4][50]. Group 1: Importance of VLA and RL - VLA models are crucial for applying powerful visual-language models in robotic control, moving beyond mere imitation learning to achieve robust performance in novel situations [6][8]. - Traditional imitation learning is limited, as robots struggle in unfamiliar scenarios, necessitating the use of RL for continuous improvement through trial and error [8][12]. Group 2: Challenges in Applying RL to VLA - There are three main challenges in applying RL to VLA: environmental differences, model instability, and computational demands [12][13]. - Directly applying RL to large VLA models can lead to catastrophic forgetting and training collapse, making it difficult to maintain performance [12][13]. Group 3: iRe-VLA Model Design - The iRe-VLA model features a two-stage iterative learning process, combining exploration through online RL and consolidation via supervised learning [16][21]. - The architecture includes a VLM backbone for understanding and an Action Head for executing control signals, optimized using LoRA technology to reduce computational load [17][18]. Group 4: Experimental Results - Experiments in simulated environments (MetaWorld, Franka Kitchen) and real-world scenarios demonstrated that iRe-VLA significantly outperformed traditional methods, with success rates improving from 43% to 83% in certain tasks [38][39]. - In real-world applications, the model's success rate for grasping previously unseen objects increased from 35% to 80% after training, showcasing its enhanced generalization capabilities [40][43]. Group 5: Conclusion and Future Directions - The iRe-VLA approach presents a viable solution for deploying large models in robotic control, highlighting the potential for ongoing research in efficient exploration and stable RL algorithms [48][50]. - The model's design allows for effective resource allocation, with local robots handling lightweight tasks while cloud servers manage heavier computations, aligning with practical deployment scenarios [54].