论文标题
学习将物体放在直立方向上的平坦表面上
Learning to Place Objects onto Flat Surfaces in Upright Orientations
论文作者
论文摘要
我们研究将握住的物体放在空的平坦表面上的问题，例如将杯子放在其底部而不是将杯子放在其侧面。我们的目的是找到所需的物体旋转，以便当对象与表面接触后打开抓地力时，将稳定地将物体放置在直立方向上。我们迭代使用两个神经网络。在每次迭代中，我们都会使用卷积神经网络来估计由机器人执行所需的对象旋转，然后估算一个单独的卷积神经网络来估计其当前方向的位置质量。我们的方法在自由空间中以98.1％的成功率将以前看不见的物体置于直立方向，而模拟机器人臂的成功率为98.1％，在模拟实验中使用了50个日常对象的数据集。进行了现实世界的实验，达到了88.0％的成功率，这是直接SIM到现实转移的概念验证。
We study the problem of placing a grasped object on an empty flat surface in an upright orientation, such as placing a cup on its bottom rather than on its side. We aim to find the required object rotation such that when the gripper is opened after the object makes contact with the surface, the object would be stably placed in the upright orientation. We iteratively use two neural networks. At every iteration, we use a convolutional neural network to estimate the required object rotation, which is executed by the robot, and then a separate convolutional neural network to estimate the quality of a placement in its current orientation. Our approach places previously unseen objects in upright orientations with a success rate of 98.1% in free space and 90.3% with a simulated robotic arm, using a dataset of 50 everyday objects in simulation experiments. Real-world experiments were performed, which achieved an 88.0% success rate, which serves as a proof-of-concept for direct sim-to-real transfer.
