Soft robotic shape estimation and proprioception are challenging because of soft robot's complex deformation behaviors and infinite degrees of freedom. A soft robot's continuously deforming body makes it difficult to integrate rigid sensors and to reliably estimate its shape. In this work, we present Proprioceptive Omnidirectional End-effector (POE), which has six embedded microphones across the tendon-driven soft robot's surface. We first introduce novel applications of previously proposed 3D reconstruction methods to acoustic signals from the microphones for soft robot shape proprioception. To improve the proprioception pipeline's training efficiency and model prediction consistency, we present POE-M. POE-M first predicts key point positions from the acoustic signal observations with the embedded microphone array. Then we utilize an energy-minimization method to reconstruct a physically admissible high-resolution mesh of POE given the estimated key points. We evaluate the mesh reconstruction module with simulated data and the full POE-M pipeline with real-world experiments. We demonstrate that POE-M's explicit guidance of the key points during the mesh reconstruction process provides robustness and stability to the pipeline with ablation studies. POE-M reduced the maximum Chamfer distance error by 23.10 % compared to the state-of-the-art end-to-end soft robot proprioception models and achieved 4.91 mm average Chamfer distance error during evaluation.

翻译：软体机器人的形状估计与本体感知因其复杂的形变行为和无限自由度而极具挑战性。软体机器人连续变形的躯体使得集成刚性传感器并可靠估计其形状变得困难。本文提出了本体感知全向末端执行器（POE），该执行器在肌腱驱动的软体机器人表面嵌入六个麦克风。我们首先将先前提出的三维重建方法创新性地应用于麦克风声学信号，以实现软体机器人形状本体感知。为提升本体感知管道的训练效率与模型预测一致性，我们进一步提出POE-M。POE-M首先通过嵌入式麦克风阵列从声学信号观测中预测关键点位置，随后利用能量最小化方法，基于估计的关键点重建POE物理可接受的高分辨率网格。我们采用仿真数据评估网格重建模块，并通过真实实验验证完整POE-M管道的性能。消融实验表明，POE-M在网格重建过程中对关键点的显式引导为该管道提供了鲁棒性与稳定性。与当前最先进的端到端软体机器人本体感知模型相比，POE-M将最大倒角距离误差降低了23.10%，并在评估中实现了4.91毫米的平均倒角距离误差。