点击下方 卡片 ，关注" 具身智能之心 "公众号 >>直播和内容获取转到 → 具身智能之心知识星球 由无界智慧（Spatialtemporal AI）团队推出的A0模型，是首个基于空间可供性感知的通用机器人分层扩散 模型，通过具身无关的可供性表征 （Embodiment-Agnostic Affordance Representation） 实现了跨平台的通 用操作能力，模型框架和代码等已经开源。 论文链接：https://arxiv.org/abs/2504.12636 项目主页：https://a-embodied.github.io/A0/ 机器人操作面临的核心挑战 在机器人技术快速发展的今天，通用化操作能力始终是制约行业发展的关键瓶颈。想象一下，当你让机器 人"擦干净白板"时，它需要准确理解应该在何处施力（"where"），以及如何移动抹布（"how"）。这正是 当前机器人操作面临的核心挑战——空间可供性感知理解不足。 现有方法主要分为两类：基于模块化的方法和端到端的视觉-语言-动作（VLA）大模型。前者虽然能利用视 觉基础模型进行空间理解，但对物体可供性的捕捉有限；后者虽能直接生成动作，却缺乏对空间 ...

点击下方 卡片 ，关注" 具身智能之心 "公众号 >>直播和内容获取转到 → 具身智能之心知识星球 由无界智慧（Spatialtemporal AI）团队推出的A0模型，是首个基于空间可供性感知的通用机器人分层扩散 模型，通过具身无关的可供性表征 （Embodiment-Agnostic Affordance Representation） 实现了跨平台的通 用操作能力，模型框架和代码等已经开源。 论文链接：https://arxiv.org/abs/2504.12636 项目主页：https://a-embodied.github.io/A0/ 机器人操作面临的核心挑战 在机器人技术快速发展的今天，通用化操作能力始终是制约行业发展的关键瓶颈。想象一下，当你让机器 人"擦干净白板"时，它需要准确理解应该在何处施力（"where"），以及如何移动抹布（"how"）。这正是 当前机器人操作面临的核心挑战——空间可供性感知理解不足。 现有方法主要分为两类：基于模块化的方法和端到端的视觉-语言-动作（VLA）大模型。前者虽然能利用视 觉基础模型进行空间理解，但对物体可供性的捕捉有限；后者虽能直接生成动作，却缺乏对空间 ...

Core Insights - The article discusses the π0, π0.5, and A0 models, focusing on their architectures, advantages, and functionalities in robotic control and task execution [3][12][21]. π0 Model Structure - The π0 model is based on a pre-trained Vision-Language Model (VLM) and Flow Matching technology, integrating seven types of robots and over 68 tasks with more than 10,000 hours of data [3]. - It utilizes a VLM backbone, an Action Expert, and Cross-Embodiment Training to handle different robot action spaces [3]. π0 Advantages and Functions - The model can execute tasks directly from language prompts without additional fine-tuning, achieving a 20%-30% higher accuracy in task execution compared to baseline models [4][6]. - It supports complex task decomposition and high-frequency precise operations, generating continuous actions at a control frequency of up to 50Hz [4][6]. π0.5 Model Structure - The π0.5 model employs a two-stage training framework and a hierarchical architecture to learn from diverse data sources and generalize to new environments [7][9]. - It integrates a Vision-Language-Action (VLA) model that encodes multi-modal inputs into a unified sequence for decision-making [9]. π0.5 Advantages and Functions - The π0.5 model shows a 25%-40% higher success rate in tasks compared to π0, with a training speed improvement of three times due to mixed discrete-continuous action training [12][13]. - It effectively handles long-duration tasks and demonstrates zero-shot semantic understanding, allowing it to recognize and act on previously unseen objects [13][16]. A0 Model Structure - The A0 model features a layered architecture that focuses on Affordance understanding and action execution, utilizing a diffusion model for predicting contact points and trajectories [21][25]. - It integrates multi-source data to create a unified Affordance representation, enhancing its ability to perform complex tasks [26]. A0 Advantages and Functions - The A0 model exhibits cross-platform generalization capabilities, allowing deployment across various robotic platforms with high efficiency in spatial reasoning [26][27]. - It achieves an average success rate of 62.5% in tasks, with specific tasks like drawer opening reaching a 75% success rate [27].

Core Insights - The article discusses the π0, π0.5, and A0 models, focusing on their architectures, advantages, and functionalities in robotic control and task execution [3][11][29]. Group 1: π0 Model Structure and Functionality - The π0 model is based on a pre-trained Vision-Language Model (VLM) and Flow Matching technology, integrating seven robots and over 68 tasks with more than 10,000 hours of data [3]. - It allows zero-shot task execution through language prompts, enabling direct control of robots without additional fine-tuning for covered tasks [4]. - The model supports complex task decomposition and multi-stage fine-tuning, enhancing the execution of intricate tasks like folding clothes [5]. - It achieves high-frequency precise operations, generating continuous action sequences at a control frequency of up to 50Hz [7]. Group 2: π0 Performance Analysis - The π0 model shows a 20%-30% higher accuracy in following language instructions compared to baseline models in tasks like table clearing and grocery bagging [11]. - For similar pre-trained tasks, it requires only 1-5 hours of data fine-tuning to achieve high success rates, and it performs twice as well on new tasks compared to training from scratch [11]. - In multi-stage tasks, π0 achieves an average task completion rate of 60%-80% through a "pre-training + fine-tuning" process, outperforming models trained from scratch [11]. Group 3: π0.5 Model Structure and Advantages - The π0.5 model employs a two-stage training framework and hierarchical architecture, enhancing its ability to generalize from diverse data sources [12][18]. - It demonstrates a 25%-40% higher success rate in tasks compared to π0, with a training speed improvement of three times due to mixed discrete-continuous action training [17]. - The model effectively handles long-duration tasks and can execute complex operations in unfamiliar environments, showcasing its adaptability [18][21]. Group 4: A0 Model Structure and Performance - The A0 model features a layered architecture that integrates high-level affordance understanding and low-level action execution, enhancing its spatial reasoning capabilities [29]. - It shows continuous performance improvement with increased training environments, achieving success rates close to baseline models when trained on 104 locations [32]. - The model's performance is significantly impacted by the removal of cross-entity and web data, highlighting the importance of diverse data sources for generalization [32]. Group 5: Overall Implications and Future Directions - The advancements in these models indicate a significant step towards practical applications of robotic systems in real-world environments, with potential expansions into service robotics and industrial automation [21][32]. - The integration of diverse data sources and innovative architectures positions these models to overcome traditional limitations in robotic task execution [18][32].