机器人操控新范式：一篇VLA模型系统性综述

Core Insights - The article discusses the emergence of Vision-Language-Action (VLA) models based on large Vision-Language Models (VLMs) as a transformative paradigm in robotic manipulation, addressing the limitations of traditional methods in unstructured environments [1][4][5] - It highlights the need for a structured classification framework to mitigate research fragmentation in the rapidly evolving VLA field [2] Group 1: New Paradigm in Robotic Manipulation - Robotic manipulation is a core challenge at the intersection of robotics and embodied AI, requiring deep understanding of visual and semantic cues in complex environments [4] - Traditional methods rely on predefined control strategies, which struggle in unstructured real-world scenarios, revealing limitations in scalability and generalization [4][5] - The advent of large VLMs has provided a revolutionary approach, enabling robots to interpret high-level human instructions and generalize to unseen objects and scenes [5][10] Group 2: VLA Model Definition and Classification - VLA models are defined as systems that utilize a large VLM to understand visual observations and natural language instructions, followed by a reasoning process that generates robotic actions [6][7] - VLA models are categorized into two main types: Monolithic Models and Hierarchical Models, each with distinct architectures and functionalities [7][8] Group 3: Monolithic Models - Monolithic VLA models can be implemented in single-system or dual-system architectures, integrating perception and action generation into a unified framework [14][15] - Single-system models process all modalities together, while dual-system models separate reflective reasoning from reactive behavior, enhancing efficiency [15][16] Group 4: Hierarchical Models - Hierarchical models consist of a planner and a policy, allowing for independent operation and modular design, which enhances flexibility in task execution [43] - These models can be further divided into Planner-Only and Planner+Policy categories, with the former focusing solely on planning and the latter integrating action execution [43][44] Group 5: Advancements in VLA Models - Recent advancements in VLA models include enhancements in perception modalities, such as 3D and 4D perception, as well as the integration of tactile and auditory information [22][23][24] - Efforts to improve reasoning capabilities and generalization abilities are crucial for enabling VLA models to perform complex tasks in diverse environments [25][26] Group 6: Performance Optimization - Performance optimization in VLA models focuses on enhancing inference efficiency through architectural adjustments, parameter optimization, and inference acceleration techniques [28][29][30] - Dual-system models have emerged to balance deep reasoning with real-time action generation, facilitating smoother deployment in real-world scenarios [35] Group 7: Future Directions - Future research directions include the integration of memory mechanisms, 4D perception, efficient adaptation, and multi-agent collaboration to further enhance VLA model capabilities [1][6]