Tactile sensing in soft robots remains particularly challenging because of the coupling between contact and deformation information which the sensor is subject to during actuation and interaction with the environment. This often results in severe interference and makes disentangling tactile sensing and geometric deformation difficult. To address this problem, this paper proposes a soft capacitive e-skin with a sparse electrode distribution and deep learning for information decoupling. Our approach successfully separates tactile sensing from geometric deformation, enabling touch recognition on a soft pneumatic actuator subject to both internal (actuation) and external (manual handling) forces. Using a multi-layer perceptron, the proposed e-skin achieves 99.88\% accuracy in touch recognition across a range of deformations. When complemented with prior knowledge, a transformer-based architecture effectively tracks the deformation of the soft actuator. The average distance error in positional reconstruction of the manipulator is as low as 2.905$\pm$2.207 mm, even under operative conditions with different inflation states and physical contacts which lead to additional signal variations and consequently interfere with deformation tracking. These findings represent a tangible way forward in the development of e-skins that can endow soft robots with proprioception and exteroception.

翻译：软体机器人中的触觉感知仍面临特殊挑战，因为传感器在执行动作和与环境交互过程中，接触信息与变形信息之间存在耦合，这常导致严重干扰，使得触觉感知与几何形变的解耦变得困难。为解决该问题，本文提出一种具有稀疏电极分布的软质电容式电子皮肤，并结合深度学习实现信息解耦。该方法成功将触觉感知与几何形变分离，使受内部（驱动）和外部（手动操作）力作用的软体气动执行器能够实现触觉识别。通过多层感知器，所提出的电子皮肤在多种形变条件下的触觉识别准确率达到99.88%。结合先验知识时，基于Transformer的架构可有效追踪软体执行器的形变。即使在存在不同充气状态和物理接触（导致额外信号变化并干扰形变追踪）的工作条件下，机械臂位置重构的平均距离误差低至2.905±2.207 mm。这些研究成果为开发能够赋予软体机器人本体感知与外感受的电子皮肤提供了切实可行的技术路径。