A0：一种面向通用机器人操作的感知意识分层模型
A0: An Affordance-Aware Hierarchical Model for General Robotic Manipulation
AI总结：
A0提出了一种分层的可及性感知扩散模型，通过分解操作任务为高层空间可及性理解和低层动作执行，提升机器人操作的效率和灵活性。
摘要：
机器人操作在理解空间可及性方面面临关键挑战——即物体相互作用的"在哪里"和"如何"，这对于复杂的操作任务如擦除黑板或堆叠物体至关重要。现有方法，包括模块化和端到端方法，往往缺乏强大的空间推理能力。与最近基于点和流的可及性方法只关注密集的空间表示或轨迹建模不同，我们提出了A0，一种分层的可及性感知扩散模型，将操作任务分解为高层的空间可及性理解和低层的动作执行。A0利用了无身体感知的可及性表示，通过预测接触点和接触后的轨迹来捕捉以物体为中心的空间可及性。A0在100万接触点数据上进行预训练，并在标注轨迹上进行微调，使模型能够跨平台泛化。关键组件包括位置偏移注意力用于运动感知的特征提取和空间信息聚合层用于精确坐标映射。模型的输出由动作执行模块执行。在多个机器人系统（Franka、Kinova、Realman和Dobot）上的实验表明，A0在复杂任务中的表现优于其他方法，展示了其效率、灵活性和现实应用价值。
英文摘要：
Robotic manipulation faces critical challenges in understanding spatial affordances--the "where" and "how" of object interactions--essential for complex manipulation tasks like wiping a board or stacking objects. Existing methods, including modular-based and end-to-end approaches, often lack robust spatial reasoning capabilities. Unlike recent point-based and flow-based affordance methods that focus on dense spatial representations or trajectory modeling, we propose A0, a hierarchical affordance-aware diffusion model that decomposes manipulation tasks into high-level spatial affordance understanding and low-level action execution. A0 leverages the Embodiment-Agnostic Affordance Representation, which captures object-centric spatial affordances by predicting contact points and post-contact trajectories. A0 is pre-trained on 1 million contact points data and fine-tuned on annotated trajectories, enabling generalization across platforms. Key components include Position Offset Attention for motion-aware feature extraction and a Spatial Information Aggregation Layer for precise coordinate mapping. The model's output is executed by the action execution module. Experiments on multiple robotic systems (Franka, Kinova, Realman, and Dobot) demonstrate A0's superior performance in complex tasks, showcasing its efficiency, flexibility, and real-world applicability.
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