论文标题
Reorientbot：学习对象重新定位特定位置
ReorientBot: Learning Object Reorientation for Specific-Posed Placement
论文作者
论文摘要
机器人需要将物体放置在任意，具体姿势中以重新排列世界并完成各种有价值的任务的能力。对象的重新定位在此中起着至关重要的作用，因为最初可能不会定向对象，以使机器人可以掌握，然后立即将它们放在特定的目标姿势中。在这项工作中，我们提出了一个基于视觉的操纵系统Reorientbot，其中包括1）使用板载RGB-D摄像头的姿势估计和体积重建的视觉场景理解； 2）学习了成功有效的运动产生重新定位的Waypoint选择； 3）传统运动计划从所选航路点产生无冲突的轨迹。我们在模拟和现实世界中使用YCB对象评估了我们的方法，与启发式方法相比，取得93％的总体成功，成功率提高了81％，执行时间提高了22％。我们演示了扩展的多对象重排，显示了系统的一般能力。
Robots need the capability of placing objects in arbitrary, specific poses to rearrange the world and achieve various valuable tasks. Object reorientation plays a crucial role in this as objects may not initially be oriented such that the robot can grasp and then immediately place them in a specific goal pose. In this work, we present a vision-based manipulation system, ReorientBot, which consists of 1) visual scene understanding with pose estimation and volumetric reconstruction using an onboard RGB-D camera; 2) learned waypoint selection for successful and efficient motion generation for reorientation; 3) traditional motion planning to generate a collision-free trajectory from the selected waypoints. We evaluate our method using the YCB objects in both simulation and the real world, achieving 93% overall success, 81% improvement in success rate, and 22% improvement in execution time compared to a heuristic approach. We demonstrate extended multi-object rearrangement showing the general capability of the system.
