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. The code and data will be open-sourced
for reproduction. More results are published on the project website at
this https URL .

通过学习估计接合参数并处理噪点云，我们提出了一个用于关节式物体的稳健感知和操纵的框架，通过局部特征学习和点元组投票的方式，我们的主要贡献是一种稳健的关节网络（RoArtNet），有效地预测关节参数和可负担点，此外，我们引入了一种关节感知的分类方案来增强其模拟到真实场景的能力，最终，通过估计可负担点和关节约束，机器人可以生成稳健的动作来操纵关节式物体，实验证实我们的方法在噪声添加的模拟和真实环境中达到了最新水平的性能。