论文标题
adagrasp：学习自适应抓地力的抓地力政策
AdaGrasp: Learning an Adaptive Gripper-Aware Grasping Policy
论文作者
论文摘要
本文旨在通过允许使用多种最终效果工具并迅速适应新工具来提高机器人的多功能性和适应性。我们提出了Adagrasp，这是一种学习单个掌握策略的方法，该政策将其推广到新颖的抓手。通过对大量握手的培训，我们的算法能够获取有关如何在各种任务中使用不同抓手的普遍知识。鉴于对场景和抓地力的视觉观察，Adagrasp通过计算抓地力和场景的形状编码之间的交叉卷积来侵入可能的掌握姿势及其掌握分数。直观地，这种交叉卷积操作可以被视为一种有效的方式，可以将场景几何形状与不同的掌握姿势（即翻译和方向）详尽匹配，其中3D几何形状的“匹配”将导致成功的掌握。我们在仿真和现实环境中验证我们的方法。我们的实验表明，Adagrasp的表现明显优于现有的多部门握把策略方法，尤其是在处理混乱的环境和部分观察时。视频可从https://youtu.be/kkntytborfs获得
This paper aims to improve robots' versatility and adaptability by allowing them to use a large variety of end-effector tools and quickly adapt to new tools. We propose AdaGrasp, a method to learn a single grasping policy that generalizes to novel grippers. By training on a large collection of grippers, our algorithm is able to acquire generalizable knowledge of how different grippers should be used in various tasks. Given a visual observation of the scene and the gripper, AdaGrasp infers the possible grasp poses and their grasp scores by computing the cross convolution between the shape encodings of the gripper and scene. Intuitively, this cross convolution operation can be considered as an efficient way of exhaustively matching the scene geometry with gripper geometry under different grasp poses (i.e., translations and orientations), where a good "match" of 3D geometry will lead to a successful grasp. We validate our methods in both simulation and real-world environments. Our experiment shows that AdaGrasp significantly outperforms the existing multi-gripper grasping policy method, especially when handling cluttered environments and partial observations. Video is available at https://youtu.be/kknTYTbORfs
