RelAfford6D：用于约束驱动机器人操作的关系型6D可操作图
RelAfford6D: Relational 6D Affordance Graphs for Constraint-Driven Robotic Manipulation
AI总结：
提出RelAfford6D框架，通过构建关系型6D可操作图将语义指令转化为SE(3)位姿约束，并利用运动学约束求解实现零样本操作，在仿真和真实环境中优于数据驱动方法。
摘要：
在开放世界机器人操作中，连接抽象语义与精确物理控制仍是一个基本挑战。尽管近期数据驱动的策略展现出潜力，但它们依赖孤立的接触点或潜在的可操作嵌入，缺乏复杂铰接物体所需的严格运动学约束。为克服这一限制，我们提出RelAfford6D，一种新颖的无训练框架，核心是关系型6D可操作图。给定自由形式的指令，我们的系统推导出连接主要交互部件及其物理锚点的语义拓扑。通过视觉基础模型将这些拓扑节点提升为精确的度量$SE(3)$位姿，我们将下游执行解析地形式化为运动学约束满足问题。机器人通过严格跟踪定义的物理流形（例如旋转或棱柱轨道）来合成连续轨迹。结合用于对抗干扰的动态重规划的闭环跟踪机制，我们的物理基础方法在仿真和真实世界环境中实现了优越的零样本成功率、跨类别泛化能力和执行鲁棒性，优于现有的数据驱动基线。
英文摘要：
Bridging abstract semantics and precise physical control remains a fundamental challenge in open-world robotic manipulation. While recent data-driven policies show promise, their reliance on isolated contact points or latent affordance embeddings lacks the rigorous kinematic constraints necessary for complex articulated objects.To overcome the limitation, we introduce RelAfford6D, a novel training-free framework centered on a Relational 6D Affordance Graph. Given a free-form instruction, our system deduces a semantic topology linking a primary interacting part to its physical anchor. By elevating these topological nodes into precise metric $SE(3)$ poses via vision foundation models, we analytically formulate downstream execution as a kinematic constraint satisfaction problem. The robot synthesizes continuous trajectories by tracking strictly defined physical manifolds (e.g., revolute or prismatic orbits). Coupled with a closed-loop tracking mechanism for dynamic replanning against disturbances, our physically grounded approach achieves superior zero-shot success rates, cross-category generalization and execution robustness in both simulation and the real world environments, outperforming existing data-driven baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
