FlowVLA：破解 VLA 模型 “物理失真” 难题，机器人世界建模再升级

Core Viewpoint - The article discusses the limitations of traditional Vision-Language-Action (VLA) models and introduces FlowVLA, a new framework that addresses these issues by implementing a Visual Chain of Thought (Visual CoT) principle, enhancing the model's ability to predict future frames through structured physical reasoning rather than mere pixel replication [5][8][36]. Group 1: Background and Current State - VLA models, particularly those pre-trained as world models, show significant potential in the field of general robotics, primarily through large self-regressive Transformers that learn environmental dynamics from vast video data [6][7]. - Existing models face critical flaws, including task confusion leading to prediction failures, knowledge transfer inefficiencies between passive observation and active control, and entangled learning of dynamics and appearance [7]. Group 2: Contributions of FlowVLA - FlowVLA introduces a new learning framework that emphasizes structured physical reasoning by requiring the model to infer motion dynamics before predicting future frames [8][10]. - The model is designed to unify appearance and motion reasoning within a single self-regressive Transformer, maintaining parameter efficiency and architectural simplicity [9][10]. - Experimental results validate FlowVLA's superior performance across various robotic operation benchmarks, demonstrating enhanced sample efficiency and bridging the gap between pre-training and policy fine-tuning [10][20]. Group 3: Research Content - The Visual CoT reasoning process decomposes the frame prediction into a causal chain of "current frame → optical flow → future frame," allowing the model to separate dynamic and appearance learning [12][14]. - The two-phase training paradigm consists of a pre-training phase focused on world model learning and a fine-tuning phase for adapting to control tasks [15][16]. Group 4: Experimental Analysis - FlowVLA outperforms existing methods in the LIBERO dataset across all task sets, particularly excelling in long-term tasks, showcasing its robust understanding of physical dynamics [20][21]. - In the SimplerEnv dataset, FlowVLA demonstrates strong adaptability to visual domain shifts, achieving significant performance improvements in tasks where other models struggle [22][23]. - The model's sample efficiency is validated, requiring only one-third of the training steps to reach peak performance compared to baseline models, with a 55% higher peak success rate in low-data scenarios [30][32]. Group 5: Key Component Validation - Ablation studies on the LIBERO-10 benchmark highlight the importance of the Visual CoT structure, flow loss, and interleaved sequence format, confirming their critical roles in the model's performance [33][34]. Group 6: Comparison with Related Work - FlowVLA distinguishes itself from traditional VLA models by prioritizing dynamic understanding and establishing a robust world model before adapting to control tasks, thus laying a solid foundation for physical knowledge [35].