RPMArt：面向关节式物体的鲁棒感知与操作
RPMArt: Towards Robust Perception and Manipulation for Articulated Objects
AI总结：
本文提出RPMArt框架，通过RoArtNet从噪声点云中鲁棒估计关节参数与可负担点，并结合关节感知分类实现零样本仿真到真实迁移，在关节式物体操作任务中取得最先进性能。
摘要：
关节式物体在日常生活中随处可见。在真实世界的机器人应用中，机器人能够对关节式物体展现出鲁棒的感知与操作技能至关重要。然而，现有的关节式物体方法未能充分应对点云中的噪声，并且难以弥合仿真与现实之间的差距，从而限制了其在真实场景中的实际部署。为应对这些挑战，我们提出了一个面向关节式物体的鲁棒感知与操作框架（RPMArt），该框架学习从含噪声的点云中估计关节参数并操作关节部件。我们的主要贡献是一个鲁棒关节网络（RoArtNet），它能够通过局部特征学习和点元组投票，鲁棒地同时预测关节参数和可负担点。此外，我们引入了一种关节感知分类方案，以增强其从仿真到现实迁移的能力。最后，借助估计得到的可负担点和关节约束，机器人能够生成鲁棒的动作来操作关节式物体。仅从合成数据中学习后，RPMArt 能够零样本迁移到真实世界的关节式物体上。实验结果证实了我们方法的有效性，我们的框架在加噪仿真和真实世界环境中均取得了最先进的性能。代码、数据和更多结果可在项目网站 https://r-pmart.github.io 上找到。
英文摘要：
Articulated objects are commonly found in daily life. It is essential that robots can exhibit robust perception and manipulation skills for articulated objects in real-world robotic applications. However, existing methods for articulated objects insufficiently address noise in point clouds and struggle to bridge the gap between simulation and reality, thus limiting the practical deployment in real-world scenarios. To tackle these challenges, we propose a framework towards Robust Perception and Manipulation for Articulated Objects (RPMArt), which learns to estimate the articulation parameters and manipulate the articulation part from the noisy point cloud. Our primary contribution is a Robust Articulation Network (RoArtNet) that is able to predict both joint parameters and affordable points robustly by local feature learning and point tuple voting. Moreover, we introduce an articulation-aware classification scheme to enhance its ability for sim-to-real transfer. Finally, with the estimated affordable point and articulation joint constraint, the robot can generate robust actions to manipulate articulated objects. After learning only from synthetic data, RPMArt is able to transfer zero-shot to real-world articulated objects. Experimental results confirm our approach's effectiveness, with our framework achieving state-of-the-art performance in both noise-added simulation and real-world environments. Code, data and more results can be found on the project website at https://r-pmart.github.io.
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。
