论文标题
针对目标驱动的闭环次数最佳浏览计划
Closed-Loop Next-Best-View Planning for Target-Driven Grasping
论文作者
论文摘要
从混乱中挑选特定对象是许多操纵任务的重要组成部分。部分观察通常要求机器人在尝试掌握之前收集场景的其他观点。本文提出了一个闭环的下一次最佳策划者，该计划者根据遮挡的对象零件驱动探索。通过不断从最新场景重建中预测抓地力，我们的政策可以在线决定最终确定执行或适应机器人的轨迹以进行进一步探索。我们表明，与常见的相机位置和处理固定基线失败的情况相比，我们的反应性方法降低了执行时间而不会丢失掌握成功率。视频和代码可在https://github.com/ethz-asl/active_grasp上找到。
Picking a specific object from clutter is an essential component of many manipulation tasks. Partial observations often require the robot to collect additional views of the scene before attempting a grasp. This paper proposes a closed-loop next-best-view planner that drives exploration based on occluded object parts. By continuously predicting grasps from an up-to-date scene reconstruction, our policy can decide online to finalize a grasp execution or to adapt the robot's trajectory for further exploration. We show that our reactive approach decreases execution times without loss of grasp success rates compared to common camera placements and handles situations where the fixed baselines fail. Video and code are available at https://github.com/ethz-asl/active_grasp.
