FlowBot++：通过关节投影学习广义关节物体操控
FlowBot++: Learning Generalized Articulated Objects Manipulation via Articulation Projection
AI总结：
FlowBot++ 提出一种基于深度 3D 视觉的机器人系统，通过融合逐点关节运动与关节参数预测，在无需事先交互的情况下实现对新颖关节物体的泛化操控，并在仿真和真实实验中验证了其有效性。
摘要：
理解和操控诸如门和抽屉之类的关节物体，对于在人类环境中工作的机器人至关重要。我们希望开发一种系统，该系统能够在其他关节物体上训练之后，无需事先交互即可学会操控新颖的关节物体。先前的关节物体操控方法要么依赖于脆弱的模块化方法，要么依赖于缺乏泛化能力的端到端方法。本文提出了 FlowBot++，这是一种基于深度 3D 视觉的机器人系统，可预测关节物体的逐点稠密运动和稠密关节参数，以辅助下游操控任务。FlowBot++ 引入了一种新颖的关节运动和关节参数的逐点表示，将二者结合可产生比任一单独方法更准确的估计。在 PartNet-Mobility 数据集上的仿真实验验证了我们的系统在操控各种物体方面的性能，而在真实物体点云和 Sawyer 机器人上的真实世界实验则证明了我们的系统在真实世界场景中的泛化能力和可行性。
英文摘要：
Understanding and manipulating articulated objects, such as doors and drawers, is crucial for robots operating in human environments. We wish to develop a system that can learn to articulate novel objects with no prior interaction, after training on other articulated objects. Previous approaches for articulated object manipulation rely on either modular methods which are brittle or end-to-end methods, which lack generalizability. This paper presents FlowBot++, a deep 3D vision-based robotic system that predicts dense per-point motion and dense articulation parameters of articulated objects to assist in downstream manipulation tasks. FlowBot++ introduces a novel per-point representation of the articulated motion and articulation parameters that are combined to produce a more accurate estimate than either method on their own. Simulated experiments on the PartNet-Mobility dataset validate the performance of our system in articulating a wide range of objects, while real-world experiments on real objects' point clouds and a Sawyer robot demonstrate the generalizability and feasibility of our system in real-world scenarios.
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。
