Actuators using soft materials feature a large number of degrees of freedom. This tremendous flexibility allows a soft actuator to passively adapt its shape to the objects under interaction. In this paper, we propose a novel proprioception method for soft actuators during real-time interaction with previously unknown objects. First, we design a color-based sensing structure that instantly translates the inflation of a bellow into changes in color, which are subsequently detected by a miniaturized color sensor. The color sensor is small and, thus, multiple of them can be integrated into soft pneumatic actuators to reflect local deformations. Second, we make use of a feed-forward neural network to reconstruct a multivariate global shape deformation from local color signals. Our results demonstrate that deformations of the actuator during interaction, including sigmoid-like shapes, can be accurately reconstructed. The accurate shape sensing represents a significant step toward closed-loop control of soft robots in unstructured environments.

This paper introduces a novel approach for sensing the bending deformation on soft robots by leveraging multicolor 3D printing. The measurement of deformation enables to complete the feedback loop of deformation control on soft actuators. The working principle of our approach is based on using compact color sensors to detect deformation that is visualized by the change of color ratios. Two novel designs are presented to generate color signals on 3D printed objects, which we call an external signal generator and an internal signal generator. Signal processing and calibration methods are developed to transform the raw RGB-data into a meaningful deformation metric. Our experimental tests taken on soft pneumatic actuators verify that color signals can be stably generated and captured to indicate the bending deformation. The results also demonstrate the usability of this sensing approach in deformation control.