穆尧团队最新！Discrete Diffusion VLA离散扩散引入VLA，支持精确动作建模和一致性训练

Core Insights - The article discusses the introduction of the Discrete Diffusion VLA model, which integrates discrete diffusion techniques into the Vision-Language-Action (VLA) framework, enhancing the efficiency and accuracy of robotic action decoding [4][7][8]. Group 1: Model Overview - The Discrete Diffusion VLA model addresses the limitations of existing VLA frameworks by utilizing a single Transformer to unify visual, language, and action modalities, eliminating the need for additional training modules [6][12]. - The model achieves an average success rate of 96.3% in the LIBERO task with the Franka Panda robotic arm, outperforming traditional autoregressive and continuous diffusion models [2][8][21]. Group 2: Performance Metrics - In various environments, the model demonstrated superior performance: 96.3% in LIBERO, 64.1% in SimplerEnv-Fractal, and 49.3% in real-simulation transfer scenarios [2][8][25]. - The model's visual matching rate reached 71.2%, significantly higher than competitors, indicating robustness to scene changes [23][24]. Group 3: Innovation and Contributions - The introduction of a "first easy, then difficult" adaptive decoding strategy allows for parallel decoding and error correction, enhancing accuracy within a unified architecture [7][11]. - The model's training process aligns with existing VLM frameworks, allowing for seamless integration and optimization without the need for specialized training processes [12][14]. Group 4: Experimental Validation - Extensive experiments validated the model's performance across multiple scenarios, showing significant improvements over baseline models, including a 19.8% increase compared to traditional autoregressive models [21][27]. - Ablation studies confirmed the effectiveness of the decoding strategy and temperature selection, with the "maximum confidence" adaptive strategy yielding the highest success rates [27][28].