In this paper, we propose a method for estimating in-hand object poses using proprioception and tactile feedback from a bimanual robotic system. Our method addresses the problem of reducing pose uncertainty through a sequence of frictional contact interactions between the grasped objects. As part of our method, we propose 1) a tool segmentation routine that facilitates contact location and object pose estimation, 2) a loss that allows reasoning over solution consistency between interactions, and 3) a loss to promote converging to object poses and contact locations that explain the external force-torque experienced by each arm. We demonstrate the efficacy of our method in a task-based demonstration both in simulation and on a real-world bimanual platform and show significant improvement in object pose estimation over single interactions. Visit www.mmintlab.com/multiscope/ for code and videos.

翻译：摘要：本文提出一种方法，利用双臂机器人系统的本体感知与触觉反馈来估计手中物体的位姿。该方法通过被抓取物体间的摩擦接触交互序列来降低位姿不确定性。作为方法的一部分，我们提出：1）一种便于接触位置与物体位姿估计的工具分割程序；2）一种能够推理交互间解一致性的损失函数；3）一种促进收敛到可解释各机械臂所受外力矩的物体位姿与接触位置的损失函数。我们通过在仿真环境及真实世界双臂平台上的任务导向演示验证了该方法的有效性，并证明其相比单次交互在物体位姿估计方面有显著提升。相关代码与视频请访问 www.mmintlab.com/multiscope/。