The precise control of soft and continuum robots requires knowledge of their shape. The shape of these robots has, in contrast to classical rigid robots, infinite degrees of freedom. To partially reconstruct the shape, proprioceptive techniques use built-in sensors resulting in inaccurate results and increased fabrication complexity. Exteroceptive methods so far rely on placing reflective markers on all tracked components and triangulating their position using multiple motion-tracking cameras. Tracking systems are expensive and infeasible for deformable robots interacting with the environment due to marker occlusion and damage. Here, we present a regression approach for 3D shape estimation using a convolutional neural network. The proposed approach takes advantage of data-driven supervised learning and is capable of real-time marker-less shape estimation during inference. Two images of a robotic system are taken simultaneously at 25 Hz from two different perspectives, and are fed to the network, which returns for each pair the parameterized shape. The proposed approach outperforms marker-less state-of-the-art methods by a maximum of 4.4% in estimation accuracy while at the same time being more robust and requiring no prior knowledge of the shape. The approach can be easily implemented due to only requiring two color cameras without depth and not needing an explicit calibration of the extrinsic parameters. Evaluations on two types of soft robotic arms and a soft robotic fish demonstrate our method's accuracy and versatility on highly deformable systems in real-time. The robust performance of the approach against different scene modifications (camera alignment and brightness) suggests its generalizability to a wider range of experimental setups, which will benefit downstream tasks such as robotic grasping and manipulation.

翻译：软体机器人与连续体机器人的精确控制依赖于对其形状的感知。与传统刚性机器人不同，这类机器人的形状具有无限自由度。本体感知技术通过内置传感器进行部分形状重构，但存在精度不足且增加制造复杂度的问题。外部感知方法目前依赖在可追踪组件上布置反射标记，并利用多个运动捕捉摄像头进行三角定位。这种追踪系统成本高昂，且因标记遮挡与损坏而难以用于与环境交互的可变形机器人。本文提出一种基于卷积神经网络的回归方法实现三维形状估计。该数据驱动的监督学习方法能够在推理阶段实时完成无标记形状估计。通过以25Hz频率同步采集两个不同视角的机器人系统图像，网络对每对图像输出参数化形状。相比现有无标记方法，本方法在估计精度上最高提升4.4%，同时具有更强的鲁棒性且无需形状先验知识。本方法仅需两个彩色摄像头（无需深度信息）且无需显式标定外部参数，易于实现。在两种软体机械臂与软体机器鱼上的评估验证了该方法对高度可变形系统的实时精度与泛化能力。该方法对摄像机对齐和亮度等场景变化具有鲁棒性，表明其可推广至更广泛的实验配置，将有益于机器人抓取与操作等下游任务。