用于机器人操作的3D affordance关键点检测
3D Affordance Keypoint Detection for Robotic Manipulation
AI总结：
本文提出FAKP-Net，通过3D关键点四元组提升机器人操作中物体功能理解与执行精度。
摘要：
本文提出了一种新颖的基于affordance的机器人操作方法，通过引入3D关键点来增强对物体部分功能性的理解。所提出的方法提供了关于物体潜在用途的直接信息，以及关于机器人应在哪里和如何参与的指导，而传统方法将affordance检测视为语义分割任务，仅回答'是什么'的问题。为解决这一差距，我们提出了一种基于融合的affordance关键点网络（FAKP-Net），通过引入3D关键点四元组，利用RGB和深度图像的协同潜力，提供执行位置、方向和范围的信息。基准测试表明，FAKP-Net在affordance分割任务和关键点检测任务中显著优于现有模型。实际实验也展示了我们的方法在完成以前未见过的物体操作任务中的可靠性。
英文摘要：
This paper presents a novel approach for affordance-informed robotic manipulation by introducing 3D keypoints to enhance the understanding of object parts' functionality. The proposed approach provides direct information about what the potential use of objects is, as well as guidance on where and how a manipulator should engage, whereas conventional methods treat affordance detection as a semantic segmentation task, focusing solely on answering the what question. To address this gap, we propose a Fusion-based Affordance Keypoint Network (FAKP-Net) by introducing 3D keypoint quadruplet that harnesses the synergistic potential of RGB and Depth image to provide information on execution position, direction, and extent. Benchmark testing demonstrates that FAKP-Net outperforms existing models by significant margins in affordance segmentation task and keypoint detection task. Real-world experiments also showcase the reliability of our method in accomplishing manipulation tasks with previously unseen objects.
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。
