论文标题
使用本体感受和触觉反馈同时进行联系位置和对象姿势估算
Simultaneous Contact Location and Object Pose Estimation Using Proprioception and Tactile Feedback
论文作者
论文摘要
抓握物体姿势和外部接触的联合估计对于稳健和灵活的操纵至关重要。在本文中，我们提出了一种新型的状态估计算法，该算法共同估算了使用本体感受和触觉反馈的3D接触位置和对象姿势。我们的方法利用了两个互补的粒子过滤器：一个用于估计接触位置（CPFGRASP），另一个用于估计对象姿势（范围）。我们对现实世界单臂和双臂机器人系统实施并评估我们的方法。我们证明，通过将两个对象融入接触，机器人可以同时推断联系人位置并同时提出对象。我们提出的方法可以应用于需要精确姿势估计（例如工具使用和组装）的许多下游任务。代码和数据可以在https://github.com/mmintlab/scope上找到。
Joint estimation of grasped object pose and extrinsic contacts is central to robust and dexterous manipulation. In this paper, we propose a novel state-estimation algorithm that jointly estimates contact location and object pose in 3D using exclusively proprioception and tactile feedback. Our approach leverages two complementary particle filters: one to estimate contact location (CPFGrasp) and another to estimate object poses (SCOPE). We implement and evaluate our approach on real-world single-arm and dual-arm robotic systems. We demonstrate that by bringing two objects into contact, the robots can infer contact location and object poses simultaneously. Our proposed method can be applied to a number of downstream tasks that require accurate pose estimates, such as tool use and assembly. Code and data can be found at https://github.com/MMintLab/scope.
