ArticuBot：通过大规模仿真学习通用铰接物体操作策略
ArticuBot: Learning Universal Articulated Object Manipulation Policy via Large Scale Simulation
AI总结：
提出ArticuBot系统，通过大规模仿真生成42.3k演示并蒸馏成分层点云策略，实现零样本迁移至真实机器人，成功打开各类未见铰接物体。
摘要：
本文提出了ArticuBot，其中单一的学习策略使机器人系统能够在现实世界中打开各种类别的未见过的铰接物体。由于此类物体在几何形状、尺寸和铰接类型上存在巨大差异，这项任务长期以来对机器人技术来说极具挑战性。我们的系统ArticuBot包含三个部分：在基于物理的仿真中生成大量演示，通过模仿学习将所有生成的演示蒸馏成基于点云的神经策略，并执行向真实机器人系统的零样本sim2real迁移。利用基于采样的抓取和运动规划，我们的演示泛化流程快速且有效，在322个训练铰接物体上共生成42.3k个演示。对于策略学习，我们提出了一种新颖的分层策略表示，其中高层策略学习末端执行器的子目标，低层策略学习在预测目标条件下如何移动末端执行器。我们证明，与非分层版本相比，这种分层方法实现了更好的物体级泛化。我们进一步为高层策略提出了一种新颖的加权位移模型，将预测锚定到场景现有的3D结构中，优于其他策略表示。我们展示我们学习的策略可以零样本迁移到三种不同的真实机器人设置：位于两个不同实验室的固定桌面Franka机械臂，以及位于移动底座上的X-Arm，在两个实验室、真实休息室和厨房中打开多个未见过的铰接物体。视频和代码可在我们的项目网站找到：https://articubot.github.io/。
英文摘要：
This paper presents ArticuBot, in which a single learned policy enables a robotics system to open diverse categories of unseen articulated objects in the real world. This task has long been challenging for robotics due to the large variations in the geometry, size, and articulation types of such objects. Our system, Articubot, consists of three parts: generating a large number of demonstrations in physics-based simulation, distilling all generated demonstrations into a point cloud-based neural policy via imitation learning, and performing zero-shot sim2real transfer to real robotics systems. Utilizing sampling-based grasping and motion planning, our demonstration generalization pipeline is fast and effective, generating a total of 42.3k demonstrations over 322 training articulated objects. For policy learning, we propose a novel hierarchical policy representation, in which the high-level policy learns the sub-goal for the end-effector, and the low-level policy learns how to move the end-effector conditioned on the predicted goal. We demonstrate that this hierarchical approach achieves much better object-level generalization compared to the non-hierarchical version. We further propose a novel weighted displacement model for the high-level policy that grounds the prediction into the existing 3D structure of the scene, outperforming alternative policy representations. We show that our learned policy can zero-shot transfer to three different real robot settings: a fixed table-top Franka arm across two different labs, and an X-Arm on a mobile base, opening multiple unseen articulated objects across two labs, real lounges, and kitchens. Videos and code can be found on our project website: https://articubot.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。
