基于机器人手内RGB-D传感器可感知的受限空间物体取出的神经重排规划
Neural Rearrangement Planning for Object Retrieval from Confined Spaces Perceivable by Robot's In-hand RGB-D Sensor
AI总结：
提出一种基于手内摄像头感知的神经物体检索框架，通过重排非目标物体保持路径同伦，高效解决受限空间物体取出问题，计算速度比基线快两个数量级。
摘要：
从受限空间中取出物体的重排规划是一个具有挑战性的问题，主要原因是缺乏供机器人运动的开放空间且感知受限。现有几种解决物体取出任务的传统方法，但它们需要顶置摄像头进行感知，并通过耗时的穷举搜索来寻找解决方案，且通常做出不切实际的假设，例如环境中存在几何形状相同的简单物体。本文提出了一种神经物体检索框架，能够高效地对受限空间中的未知任意物体进行重排规划，以利用给定的机器人抓取来取出目标物体。该方法利用机器人的手内摄像头主动感知环境，随后选择并重新定位非目标物体，使其不阻挡通往目标物体的机器人路径同伦，从而辅助底层路径规划器快速找到机器人运动序列。此外，我们在包括具有任意家居物体的现实世界柜子环境等挑战性场景中演示了该框架。结果表明，该框架在所有展示的方法中取得了最佳性能，且平均计算速度比表现最佳的基线方法快两个数量级。
英文摘要：
Rearrangement planning for object retrieval tasks from confined spaces is a challenging problem, primarily due to the lack of open space for robot motion and limited perception. Several traditional methods exist to solve object retrieval tasks, but they require overhead cameras for perception and a time-consuming exhaustive search to find a solution and often make unrealistic assumptions, such as having identical, simple geometry objects in the environment. This paper presents a neural object retrieval framework that efficiently performs rearrangement planning of unknown, arbitrary objects in confined spaces to retrieve the desired object using a given robot grasp. Our method actively senses the environment with the robot's in-hand camera. It then selects and relocates the non-target objects such that they do not block the robot path homotopy to the target object, thus also aiding an underlying path planner in quickly finding robot motion sequences. Furthermore, we demonstrate our framework in challenging scenarios, including real-world cabinet-like environments with arbitrary household objects. The results show that our framework achieves the best performance among all presented methods and is, on average, two orders of magnitude computationally faster than the best-performing baselines.
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。
