论文标题
X射线：通过最大程度地减少对学习的占用分布的支持，机械搜索被阻塞的对象
X-Ray: Mechanical Search for an Occluded Object by Minimizing Support of Learned Occupancy Distributions
论文作者
论文摘要
对于电子商务，仓库，医疗保健和家庭服务中的应用程序，通常需要机器人搜索对象堆以掌握特定的目标对象。对于机械搜索，我们引入了X射线，这是一种基于学习的占用分布的算法。我们使用RGBD堆图像的合成数据集训练神经网络，该数据集标记为一组具有不同纵横比的标准边界框目标。 X射线最小化对学习分布的支持，作为模拟和真实环境中机械搜索策略的一部分。在目标对象被部分或完全遮住的模拟中，我们对1,000个对象的1,000个对象的两个基线策略进行基准测试。结果表明，X射线在82％的时间中成功提取目标对象的效率要高得多，比表现最佳的基线要多15％。在ABB Yumi机器人上进行了20个堆的25个家庭对象的实验表明，学到的政策很容易转移到物理系统，在该系统中，它的成功率超过了基线政策，而动作较少17％。数据集，视频和实验可在https://sites.google.com/berkeley.edu/x-ray上找到。
For applications in e-commerce, warehouses, healthcare, and home service, robots are often required to search through heaps of objects to grasp a specific target object. For mechanical search, we introduce X-Ray, an algorithm based on learned occupancy distributions. We train a neural network using a synthetic dataset of RGBD heap images labeled for a set of standard bounding box targets with varying aspect ratios. X-Ray minimizes support of the learned distribution as part of a mechanical search policy in both simulated and real environments. We benchmark these policies against two baseline policies on 1,000 heaps of 15 objects in simulation where the target object is partially or fully occluded. Results suggest that X-Ray is significantly more efficient, as it succeeds in extracting the target object 82% of the time, 15% more often than the best-performing baseline. Experiments on an ABB YuMi robot with 20 heaps of 25 household objects suggest that the learned policy transfers easily to a physical system, where it outperforms baseline policies by 15% in success rate with 17% fewer actions. Datasets, videos, and experiments are available at https://sites.google.com/berkeley.edu/x-ray.
