Grasp planning and most specifically the grasp space exploration is still an open issue in robotics. This article presents a data-driven oriented methodology to model the grasp space of a multi-fingered adaptive gripper for known objects. This method relies on a limited dataset of manually specified expert grasps, and uses variational autoencoder to learn grasp intrinsic features in a compact way from a computational point of view. The learnt model can then be used to generate new non-learnt gripper configurations to explore the grasp space.
翻译:格拉斯普(Grasp)规划和更具体地说,掌握空间探索仍然是机器人中一个未决问题。本文章介绍了一种以数据为导向的方法,用于模拟已知物体多指适应性抓抓器的抓取空间。该方法依靠手动指定专家抓取的有限数据集,并使用变式自动编码器从计算角度以紧凑的方式从计算角度学习内在特征。随后,所学的模型可用于生成新的非单向抓抓抓器配置,以探索抓抓空间。