RelAfford6D: Relational 6D Affordance Graphs for Constraint-Driven Robotic Manipulation

• URL: http://arxiv.org/abs/2606.27036v1
• Date: Thu, 25 Jun 2026 13:42:26 GMT
• Title: RelAfford6D: Relational 6D Affordance Graphs for Constraint-Driven Robotic Manipulation
• Abstract summary: We introduce RelAfford6, a training-free framework for complex articulated objects.<n>Our system deduces a semantic topology linking a primary interacting part to its physical anchor.<n>We analytically formulate downstream execution as a satisfaction problem.
• Score: 15.618446008590409
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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