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.
翻译:联合估计被捕获的物体的外形和外形接触是稳健和巧妙操纵的核心。在本文件中,我们建议采用一种新的国家估计算法,利用完全自行感知和触觉反馈,共同估计接触位置和物体在三维中的位置。我们的方法利用两个互补的粒子过滤器:一个是估计接触位置(CPFGrasp),另一个是估计物体的外形(SCOPE),我们实施和评估我们关于现实世界单臂和双臂机器人系统的方法。我们证明,通过将两个物体连接到接触中,机器人可以推断接触位置和物体同时构成。我们提议的方法可以适用于一些需要准确估计的下游任务,例如工具的使用和组装。代码和数据可在https://github.com/MmintLab/scorm上找到。