Soft robots have applications in safe human–robot interactions, manipulation of fragile objects, and locomotion in challenging and unstructured environments. In this article, we present a computational method for augmenting soft robots with proprioceptive sensing capabilities. Our method automatically computes a minimal stretch-receptive sensor network to user-provided soft robotic designs, which is optimized to perform well under a set of user-specified deformation-force pairs. The sensorized robots are able to reconstruct their full deformation state, under interaction forces. We cast our sensor design as a subselection problem, selecting a minimal set of sensors from a large set of fabricable ones, which minimizes the error when sensing specified deformation-force pairs. Unique to our approach is the use of an analytical gradient of our reconstruction performance measure with respect to selection variables. We demonstrate our technique on a bending bar and gripper example, illustrating more complex designs with a simulated tentacle.

中文翻译：

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MakeSense：本体感受软机器人的自动传感器设计。

软机器人可应用于安全的人机交互、易碎物体的操作以及在具有挑战性和非结构化环境中的运动。在本文中，我们提出了一种增强具有本体感知能力的软机器人的计算方法。我们的方法自动计算最小拉伸接收传感器网络到用户提供的软机器人设计，该设计经过优化以在一组用户指定的变形力对下表现良好。传感机器人能够在相互作用力下重建其完全变形状态。我们将传感器设计作为子选择问题，从大量可制造的传感器中选择最小的一组传感器，从而最大限度地减少感应指定变形力对时的误差。我们的方法的独特之处在于使用我们的重建性能测量关于选择变量的分析梯度。我们在弯曲杆和抓手示例中展示了我们的技术，用模拟触手说明了更复杂的设计。