Abstract
We propose a novel pipeline for unknown object grasping in shared robotic autonomy scenarios. State-of-the-art methods for fully autonomous scenarios are typically learning-based approaches optimised for a specific end-effector, that generate grasp poses directly from sensor input. In the domain of assistive robotics, we seek instead to utilise the user's cognitive abilities for enhanced satisfaction, grasping performance, and alignment with their high level task-specific goals. Given a pair of stereo images, we perform unknown object instance segmentation and generate a 3D reconstruction of the object of interest. In shared control, the user then guides the robot end-effector across a virtual hemisphere centered around the object to their desired approach direction. A physics-based grasp planner finds the most stable local grasp on the reconstruction, and finally the user is guided by shared control to this grasp. In experiments on the DLR EDAN platform, we report a grasp success rate of 87% for 10 unknown objects, and demonstrate the method's capability to grasp objects in structured clutter and from shelves.
Abstract (translated)
我们提出了一个在共享机器人自主场景中解决未知物体抓取的新流程。在先进的全自动驾驶场景中，通常采用基于学习的优化方法，针对特定的末端设备生成直接的抓取姿态。在辅助机器人领域，我们寻求利用用户的认知能力来提高满足感、抓取性能以及与高层次任务目标的对齐。 给定一对立体图像，我们进行未知物体实例分割并生成物体感兴趣的3D复原。在共享控制下，用户 then 导引机器人末端Effector 穿越围绕物体的虚拟半球，以到达期望的接近方向。基于物理的抓取规划器找到重构中最具稳定性的局部抓取，最后用户通过共享控制找到这个抓取。 在德国 Frauncese 实验室的 EDAN 平台实验中，我们报告了10个未知物体的抓取成功率为87%，并展示了该方法在结构混乱和货架上的物体抓取能力。
URL
https://arxiv.org/abs/2404.15001
