During in-hand manipulation, robots must be able to continuously estimate the pose of the object in order to generate appropriate control actions. The performance of algorithms for pose estimation hinges on the robot's sensors being able to detect discriminative geometric object features, but previous sensing modalities are unable to make such measurements robustly. The robot's fingers can occlude the view of environment- or robot-mounted image sensors, and tactile sensors can only measure at the local areas of contact. Motivated by fingertip-embedded proximity sensors' robustness to occlusion and ability to measure beyond the local areas of contact, we present the first evaluation of proximity sensor based pose estimation for in-hand manipulation. We develop a novel two-fingered hand with fingertip-embedded optical time-of-flight proximity sensors as a testbed for pose estimation during planar in-hand manipulation. Here, the in-hand manipulation task consists of the robot moving a cylindrical object from one end of its workspace to the other. We demonstrate, with statistical significance, that proximity-sensor based pose estimation via particle filtering during in-hand manipulation: a) exhibits 50% lower average pose error than a tactile-sensor based baseline; b) empowers a model predictive controller to achieve 30% lower final positioning error compared to when using tactile-sensor based pose estimates.

翻译：在手内操作过程中，机器人需持续估计物体的位姿以生成合适的控制动作。位姿估计算法的性能取决于机器人传感器能否检测到具有判别性的几何物体特征，但先前的传感模态无法稳健地进行此类测量。机器人的手指可能遮挡环境或机器人搭载的图像传感器视野，而触觉传感器仅能在局部接触区域进行测量。基于指尖嵌入式接近传感器对遮挡的鲁棒性及其超越局部接触区域的测量能力，我们首次评估了基于接近传感器的位姿估计在手内操作中的应用。我们开发了一种新型双指手，其指尖嵌入光学飞行时间接近传感器，作为平面手内操作中位姿估计的测试平台。手内操作任务包括机器人将圆柱形物体从其工作空间一端移动到另一端。我们通过统计显著性证明，基于粒子滤波的接近传感器位姿估计在手内操作中：a) 相较于基于触觉传感器的基线，平均位姿误差降低50%；b) 使模型预测控制器相较于使用基于触觉传感器的位姿估计，最终定位误差降低30%。