论文标题
基于类别关联的相似性匹配新颖的对象接地任务
Category-Association Based Similarity Matching for Novel Object Pick-and-Place Task
论文作者
论文摘要
很长一段时间以来，已经研究了机器人的拾取地点，以应对新型物体和可变环境的不确定性。过去的作品主要集中在基于学习的方法上，以实现高精度。但是，对于指定培训模型的局限性，它们很难被概括。为了解决基于学习的方法的这一弊端，我们介绍了基于类别 - 求解的新颖对象与已知数据库之间相似性匹配的新观点，以实现高精度和稳定性的选择。我们使用Word嵌入来计算类别名称相似性，以量化已知模型类别和目标现实世界对象之间的语义相似性。通过通过相似性预测函数确定的相似模型，我们准备了一系列强大的抓地力，并模仿它们计划对现实世界目标对象的新grasps。我们还提出了一种基于距离的方法来推断物体的姿势并调整小旋转以在不确定性下实现稳定的位置。通过实际的机器人拾取实验实验，具有十几个类别内和分类外的小说对象，我们的方法的平均成功率分别达到90.6％和75.9％，从而验证了对各种对象的概括能力。
Robotic pick-and-place has been researched for a long time to cope with uncertainty of novel objects and changeable environments. Past works mainly focus on learning-based methods to achieve high precision. However, they have difficulty being generalized for the limitation of specified training models. To break through this drawback of learning-based approaches, we introduce a new perspective of similarity matching between novel objects and a known database based on category-association to achieve pick-and-place tasks with high accuracy and stabilization. We calculate the category name similarity using word embedding to quantify the semantic similarity between the categories of known models and the target real-world objects. With a similar model identified by a similarity prediction function, we preplan a series of robust grasps and imitate them to plan new grasps on the real-world target object. We also propose a distance-based method to infer the in-hand posture of objects and adjust small rotations to achieve stable placements under uncertainty. Through a real-world robotic pick-and-place experiment with a dozen of in-category and out-of-category novel objects, our method achieved an average success rate of 90.6% and 75.9% respectively, validating the capacity of generalization to diverse objects.
