论文标题
使用均衡模型样本有效的学习
Sample Efficient Grasp Learning Using Equivariant Models
论文作者
论文摘要
在Planar Grasp检测中，目标是从场景的图像中学习一个功能，以$ \ Mathrm {Se}（2）$中的一组可行的抓握姿势。在本文中，我们认识到，最佳掌握函数为$ \ mathrm {se}（2）$ - equivariant，可以使用Equivariant卷积神经网络进行建模。结果，我们能够显着提高GRASP学习的样本效率，仅在600次抓取尝试后获得掌握函数的近似值。这足以使我们可以在大约1.5小时内学会完全掌握物理机器人。
In planar grasp detection, the goal is to learn a function from an image of a scene onto a set of feasible grasp poses in $\mathrm{SE}(2)$. In this paper, we recognize that the optimal grasp function is $\mathrm{SE}(2)$-equivariant and can be modeled using an equivariant convolutional neural network. As a result, we are able to significantly improve the sample efficiency of grasp learning, obtaining a good approximation of the grasp function after only 600 grasp attempts. This is few enough that we can learn to grasp completely on a physical robot in about 1.5 hours.
