用物体功能信息指导相机视角，高效实现抓取任务
GCNGrasp-VP: Affordance-Guided View Planning for Efficient Task-Oriented Grasping
- 基于物体功能场预测，实时规划最优观察视角
- 仅需一次视角调整，抓取成功率提升显著
- 适合需要快速响应的机器人抓取场景
当物体视角受遮挡时，任务导向抓取性能会大幅下降。现有方法通常假设任务相关区域在初始帧中可见，而视角规划方法虽能主动感知，却常忽略任务语义且依赖耗时的场景重建。为此，我们提出GCNGrasp-VP框架，将功能场预测与主动视角规划结合。核心是GCNGrasp-v2模型，可同时完成抓取评估与功能场预测，实现常数时间推理复杂度。利用此能力，我们的功能引导视角规划器（Affordance-VP）以功能场作为信息增益指标，无需场景重建即可引导相机观测任务相关区域。实验表明，本方法在仅进行一次视角调整的情况下，显著优于基于场景不确定性的基线方法。真实世界验证显示，在单物体场景中抓取成功率明显提升，同时保持毫秒级计算延迟。代码与模型已开源：https://github.com/Instinct323/GCNGrasp-VP。
原文摘要 · Abstract (English)
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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