GCNGrasp-VP: Affordance-Guided View Planning for Efficient Task-Oriented Grasping

• URL: http://arxiv.org/abs/2606.19091v1
• Date: Wed, 17 Jun 2026 13:59:19 GMT
• Title: GCNGrasp-VP: Affordance-Guided View Planning for Efficient Task-Oriented Grasping
• Abstract summary: GCNGrasp-VP is an efficient framework integrating affordance field prediction with active view planning.<n>Our method significantly outperforms scene-uncertainty-driven baselines with only one view adjustment.
• Score: 5.388304847250797
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Task-oriented grasping performance degrades significantly when object views suffer from occlusions. Existing task-oriented grasping methods typically assume task-relevant regions are visible in the initial frame, while view planning approaches enable active perception but often ignore task semantics and rely on time-consuming scene reconstruction. To address these limitations, we present GCNGrasp-VP, an efficient framework integrating affordance field prediction with active view planning. Central to this framework is GCNGrasp-v2, a task-oriented grasp model that simultaneously supports grasp evaluation and affordance field prediction, achieving constant-time inference complexity. Leveraging this capability, our Affordance-guided View Planner (Affordance-VP) utilizes the affordance field as an information gain metric to guide camera observation of task-relevant regions without requiring scene reconstruction. View planning results show that our method significantly outperforms scene-uncertainty-driven baselines with only one view adjustment. Real-world validation further confirms substantial improvements in grasp success rates for single-object scenarios while maintaining millisecond-level computational latency. Code and models are available at https://github.com/Instinct323/GCNGrasp-VP.

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