一文尽览！2025年多篇VLA与RL融合的突破方向

Core Viewpoint - The article discusses a significant revolution in the field of robotic embodied intelligence, focusing on the integration of Vision-Language-Action (VLA) models with Reinforcement Learning (RL) to address core challenges in real-world robotic decision-making and task execution [2][57]. Group 1: GRAPE - The GRAPE framework enhances the generalization of robot policies through preference alignment, addressing the limitations of VLA models in task adaptability and generalization [4][5]. - GRAPE improves the success rate of in-domain tasks by 51.79% and out-of-domain tasks by 58.20%, while also reducing collision rates by 37.44% under safety objectives [7][8]. Group 2: VLA-RL - The VLA-RL framework utilizes trajectory-level RL expressions to model operation trajectories and fine-tune reward models to tackle sparse rewards, enhancing task performance and demonstrating signs of reasoning expansion [10][12]. - In evaluations across 40 challenging robotic tasks, VLA-RL significantly outperformed existing models, indicating its potential for scalable applications [14]. Group 3: ReWiND - The ReWiND framework allows for the adaptation of robot policies to unseen tasks using a pre-trained language-based reward function, improving generalization and sample efficiency without the need for new demonstrations [17][18]. - ReWiND shows a 2.4 times improvement in reward generalization and a 5 times increase in performance for pre-trained dual-arm strategies in real-world scenarios [20]. Group 4: ConRFT - The ConRFT method employs a two-phase reinforcement fine-tuning approach to stabilize the supervision of VLA models, significantly increasing the success rate of practical tasks to 96.3% with a 144% improvement over previous methods [23][28]. - The model requires only 45 to 90 minutes of online fine-tuning to achieve these results, demonstrating its efficiency [28]. Group 5: RLDG - The RLDG method enhances the performance of generalist robot policies by generating high-quality training data through reinforcement learning, addressing the limitations of human demonstration data [32][33]. - In practical experiments, RLDG achieved a 40% increase in success rates for precise operation tasks, showcasing its effectiveness in improving generalization capabilities [38]. Group 6: TGRPO - The TGRPO method integrates trajectory-level group relative policy optimization to enhance the robustness and efficiency of VLA model fine-tuning in new environments [39][43]. - TGRPO consistently outperformed various baseline methods across ten operational tasks, validating its effectiveness in improving VLA model adaptability [43]. Group 7: iRe-VLAd - The iRe-VLAd framework optimizes VLA models through iterative reinforcement and supervised learning, addressing the instability and computational burden of direct online RL applications [44][48]. - This approach has been validated in multiple simulated and real-world scenarios, proving its capability to enhance performance in interactive settings [50]. Group 8: RIPT-VLA - The RIPT-VLA method introduces interactive post-training for VLA models, utilizing sparse binary success rewards to improve adaptability in low-data environments [51][54]. - This framework has shown significant improvements in compatibility, efficiency, and generalization, achieving a 97% success rate with minimal supervision [56]. Conclusion - The eight studies collectively represent a pivotal advancement in robotic intelligence, focusing on overcoming industry challenges such as task generalization, adaptability to dynamic environments, and multimodal information integration, with practical applications in home automation, industrial assembly, and robotic manipulation [57].