FlowBot3D：学习3D关节运动流以操纵关节式物体
FlowBot3D: Learning 3D Articulation Flow to Manipulate Articulated Objects
AI总结：
FlowBot3D通过仿真训练神经网络预测点云稠密关节运动向量场，并结合解析运动规划器实现零微调的真实机器人关节式操纵，在仿真与真实实验中均达到最先进水平。
摘要：
我们探索了一种感知和操纵3D关节式物体的新方法，该方法能够泛化，使机器人可以关节式地操纵未见过的物体类别。我们提出了一个基于视觉的系统，该系统学习预测各种关节式物体部件的潜在运动，以指导系统的下游运动规划，从而关节式地操纵这些物体。为了预测物体运动，我们训练了一个神经网络，输出一个稠密向量场，表示点云中的点在关节运动下的逐点运动方向。然后，我们基于该向量场部署了一个解析运动规划器，以实现产生最大关节运动的策略。我们完全在仿真中训练该视觉系统，并展示了我们的系统在仿真和真实世界中泛化到未见物体实例和新类别的能力，将我们的策略部署在Sawyer机器人上且无需微调。结果表明，我们的系统在仿真和真实世界实验中都达到了最先进的性能。
英文摘要：
We explore a novel method to perceive and manipulate 3D articulated objects that generalizes to enable a robot to articulate unseen classes of objects. We propose a vision-based system that learns to predict the potential motions of the parts of a variety of articulated objects to guide downstream motion planning of the system to articulate the objects. To predict the object motions, we train a neural network to output a dense vector field representing the point-wise motion direction of the points in the point cloud under articulation. We then deploy an analytical motion planner based on this vector field to achieve a policy that yields maximum articulation. We train the vision system entirely in simulation, and we demonstrate the capability of our system to generalize to unseen object instances and novel categories in both simulation and the real world, deploying our policy on a Sawyer robot with no finetuning. Results show that our system achieves state-of-the-art performance in both simulated and real-world experiments.
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。
