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.
翻译:为了应对新天体和可变环境的不确定性,长期以来一直在对机器人选址和地点进行了研究,以应对新天体和可变环境的不确定性。过去的工作主要侧重于以学习为基础的方法,以实现高精度的精确度。然而,由于对特定培训模式的限制,它们很难被普遍采用。要打破学习方法的这一缺陷,我们引入了新天体和基于类别关联的已知数据库之间的相似性匹配新视角,以便实现选址和地点任务的高度精度和稳定性。我们计算了类别名称的相似性,用词嵌入来量化已知模型类别和目标真实世界对象之间的语义相似性。有了类似模型的类似模型,我们预先规划了一系列强力捕捉,并模仿了这些模型,以规划对现实世界目标对象的新捕捉。我们还提出了一种远程方法,用以推导物体的手表姿势,并调整小型旋转,以在不确定性下实现稳定的定位。我们采用的方法,通过实际的机器人选址实验,对数十个类内和类外新天体进行了数类新天体进行量性试验,我们的方法实现了平均成功率率分别为90.6%和75.9%。