面向机器人操作的高效视觉-触觉物体形状补全
Efficient Visuo-Haptic Object Shape Completion for Robot Manipulation
AI总结：
本文提出闭环视觉-触觉形状补全方法，利用隐式表面网络选择高不确定区域并以两指夹持器直接探索，真实机器人抓取成功率经五次触碰由63.3%提升至82.7%。
摘要：
对于机器人操作而言，完整且准确的物体形状是十分理想的。本文提出一种在闭环流程中结合视觉与触觉重建的方法。从初始视角出发，使用隐式表面深度神经网络重建物体形状。系统选择不确定性最高的位置进行触觉探索，触碰物体，并加入来自触觉的新信息以及来自相机的新点云，重新估计物体位置，然后重复该循环。我们扩展了 Rustler 等人（2022）的工作，采用一种具有理论依据的新方法来确定不确定性最高的点；同时，我们不仅将接触点加入点云，还把机器人向物体运动过程中所确定的空闲空间纳入其中，从而提高每次触觉探索的收益。此外，该方案结构紧凑，因为闭合的两指夹持器的夹爪可直接用于探索。每次机器人动作后都会重新估计物体位置，并且桌面上可以同时存在多个物体。在三种不同指标下，我们都实现了随每次触碰而稳定提升的效果，并在真实机器人上的抓取实验中展示了更优形状重建的实用性。平均而言，仅进行一次探索性触碰后，抓取成功率便从 63.3% 提升至 70.4%，经过五次触碰后提升至 82.7%。所收集的数据和代码均已公开（https://osf.io/j6rkd/，https://github.com/ctu-vras/vishac）。
英文摘要：
For robot manipulation, a complete and accurate object shape is desirable. Here, we present a method that combines visual and haptic reconstruction in a closed-loop pipeline. From an initial viewpoint, the object shape is reconstructed using an implicit surface deep neural network. The location with highest uncertainty is selected for haptic exploration, the object is touched, the new information from touch and a new point cloud from the camera are added, object position is re-estimated and the cycle is repeated. We extend Rustler et al. (2022) by using a new theoretically grounded method to determine the points with highest uncertainty, and we increase the yield of every haptic exploration by adding not only the contact points to the point cloud but also incorporating the empty space established through the robot movement to the object. Additionally, the solution is compact in that the jaws of a closed two-finger gripper are directly used for exploration. The object position is re-estimated after every robot action and multiple objects can be present simultaneously on the table. We achieve a steady improvement with every touch using three different metrics and demonstrate the utility of the better shape reconstruction in grasping experiments on the real robot. On average, grasp success rate increases from 63.3% to 70.4% after a single exploratory touch and to 82.7% after five touches. The collected data and code are publicly available (https://osf.io/j6rkd/, https://github.com/ctu-vras/vishac)
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。
