Core Insights - The article provides a comprehensive overview of the emerging field of Vision-Language-Action (VLA), highlighting its rapid growth and significance in AI and robotics [1][5]. Summary by Sections VLA Overview - VLA models have seen a dramatic increase in submissions, rising from single digits to 164, marking an 18-fold growth [5]. - A model qualifies as VLA if it uses a pre-trained backbone on large-scale visual-language data, emphasizing capabilities in language understanding, visual generalization, and task transfer [5][6]. Key Trends in VLA - Trend 1: Efficient Architecture Paradigm Discrete diffusion models are emerging as a new paradigm, allowing for parallel generation of action sequences, enhancing efficiency and integrating reasoning with actions [7][10]. - Trend 2: Embodied Chain-of-Thought (ECoT) ECoT emphasizes generating intermediate reasoning steps before actions, improving planning and interpretability, although it relies heavily on high-quality annotated data [11][12]. - Trend 3: Action Tokenizer The action tokenizer converts continuous robot actions into discrete tokens that VLMs can understand, bridging the gap between the robot's actions and the VLM's processing [14][16]. - Trend 4: Reinforcement Learning (RL) RL is reintroduced to fine-tune VLA strategies, addressing limitations of imitation learning in extreme scenarios, with notable successes in recent studies [17][18]. - Trend 5: Efficiency Optimization Efforts are being made to reduce the hardware requirements for VLA models, making the field more accessible to smaller research labs [19]. - Trend 6: Video Prediction for Physical Intuition Video generation models provide inherent understanding of temporal dynamics and physical laws, enhancing robot control capabilities [20][23]. - Trend 7: Realistic Evaluation Benchmarks New evaluation frameworks are being developed to overcome the limitations of existing benchmarks, focusing on meaningful generalization capabilities [24][26]. Challenges and Future Directions - The article highlights the "performance ceiling" issue in mainstream simulation evaluations, where high scores do not necessarily translate to real-world capabilities [30]. - Two critical areas needing more attention are data quality and in-context learning, which could be pivotal for advancing VLA research [31].
最火VLA，看这一篇综述就够了
3 6 Ke·2025-10-31 08:22