Modeling Grasp Type Improves Learning-Based Grasp Planning
Modeling Grasp Type Improves Learning-Based Grasp Planning
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DOI：
10.1109/lra.2019.2893410
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发表时间：
2019-04-01
影响因子：
5.2
通讯作者：
Hermans, Tucker
中科院分区：
文献类型：
--
作者：
Lu, Qingkai;Hermans, Tucker
不同的操作任务需要不同类型的抓握。例如，拿着一个沉重的工具，如锤子，需要一个多指的力量把握提供稳定性，而拿着一支笔写需要一个多指的精确把握赋予灵巧的对象。在本文中，我们提出了一个概率把握规划器，明确模型把握类型规划高品质的精度和权力掌握在真实的时间。我们采取的学习方法，以计划把握不同类型的以前看不见的对象时，只有部分视觉信息可用。这封信展示了第一种监督学习方法来进行抓取规划，可以明确地规划给定对象的力量和精度。此外，我们比较了我们的学习掌握模型与不编码类型的模型，并表明建模把握类型提高了生成的把握的成功率。此外，我们展示了学习一个先验的优势，以提高掌握配置与学习分类器的把握推理。
Different manipulation tasks require different types of grasps. For example, holding a heavy tool like a hammer requires a multifingered power grasp offering stability, while holding a pen to write requires a multifingered precision grasp to impart dexterity on the object. In this paper, we propose a probabilistic grasp planner that explicitly models grasp type for planning high-quality precision and power grasps in real time. We take a learning approach in order to plan grasps of different types for previously unseen objects when only partial visual information is available. This letter demonstrates the first supervised learning approach to grasp planning that can explicitly plan both power and precision grasps for a given object. Additionally, we compare our learned grasp model with a model that does not encode type and show that modeling grasp type improves the success rate of generated grasps. Furthermore, we show the benefit of learning a prior over grasp configurations to improve grasp inference with a learned classifier.