Multi-Stage Learning of Selective Dual-Arm Grasping Based on Obtaining and Pruning Grasping Points Through the Robot Experience in the Real World
Multi-Stage Learning of Selective Dual-Arm Grasping Based on Obtaining and Pruning Grasping Points Through the Robot Experience in the Real World
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DOI：
10.1109/iros.2018.8593752
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
Shingo Kitagawa;Kentaro Wada;Shun Hasegawa;K. Okada;M. Inaba
中科院分区：
文献类型：
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作者：
Shingo Kitagawa;Kentaro Wada;Shun Hasegawa;K. Okada;M. Inaba
近年来，自监督方法已成为机器人抓取的常用方法。该方法虽然提高了成功率，但需要长时间执行多次抓取试验，且只考虑单臂抓取。然而，机器人可以用两只手臂抓取更多的物体，人形机器人等双臂机器人有望实现双臂操作，克服单臂的限制。在本文中，我们引入双臂抓取作为另一种可能的策略，并提出了一种使用卷积神经网络（CNN）进行抓取点预测和语义分割的选择性双臂抓取的多阶段学习方法。在第一阶段，网络通过自动标注学习抓取点。虽然机器人可以通过标注有效地学习单臂和双臂抓取，但由于标注算法是人为设计的，机器人可能无法抓取。因此，在第二阶段，机器人采用两种抓取策略对不同的抓取点进行采样，学习如何在现实世界中抓取。在此阶段，机器人通过经验获取新的可能抓取点，并对两种抓取策略的不成功点进行修剪。在现实世界的实验中，经过90次试验，自适应网络的成功率达到了76.7%。由于没有经过自适应阶段训练的网络成功率较低，为56.7%，这一结果也说明该网络在抓取采样次数少于250次的情况下得到了细化。作为我们方法的一个应用，我们证明了我们的系统在仓库拣选任务中工作良好。
Recently, self-supervised approach is common for robot grasping. Although this approach improves success rate, it requires a long time to execute a number of grasp trials, and single-arm grasping is only considered. However, robots can grasp more various objects with two arms, and dual-arm robots such as humanoid robots are expected to execute dual-arm manipulation and overcome the single-arm limitation. In this paper, we introduce dual-arm grasping as another possible strategy and propose a multi-stage learning method for selective dual-arm grasping using Convolutional Neural Networks (CNN)for grasping point prediction and semantic segmentation. In the first stage, the network learns grasping points with the automatic annotation. Although a robot learns both single-arm and dual-arm grasping efficiently with the annotation, the robot may not be able to grasp it because the annotation algorithm is designed by human. Therefore, for the second stage, the robot samples various grasping points with both grasping strategies and learns how to grasp in the real world. In this stage, the robot obtains new possible grasping points and prunes unsuccessful ones for both grasping strategies through the robot experience. In the experiments in the real world, the adapted network achieved high success rate 76.7% in 90 trials. Since the network trained with no adaptation stage resulted in lower success rate 56.7%, this result also shows the network was refined with less than 250 times of grasp sampling. As an application of our method, we demonstrated that our system worked well in warehouse picking task.