Strain sensors are gaining popularity in soft robotics for acquiring tactile data due to their flexibility and ease of integration. Tactile sensing plays a critical role in soft grippers, enabling them to safely interact with unstructured environments and precisely detect object properties. However, a significant challenge with these systems is their high non-linearity, time-varying behavior, and long-term signal drift. In this paper, we introduce a continual learning (CL) approach to model a soft finger equipped with piezoelectric-based strain sensors for proprioception. To tackle the aforementioned challenges, we propose an adaptive CL algorithm that integrates a Long Short-Term Memory (LSTM) network with a memory buffer for rehearsal and includes a regularization term to keep the model's decision boundary close to the base signal while adapting to time-varying drift. We conduct nine different experiments, resetting the entire setup each time to demonstrate signal drift. We also benchmark our algorithm against two other methods and conduct an ablation study to assess the impact of different components on the overall performance.

Soft robots show compliance and have infinite degrees of freedom. Thanks to these properties, such robots are leveraged for surgery, rehabilitation, biomimetics, unstructured environment exploring, and industrial gripper. In this case, they attract scholars from a variety of areas. However, nonlinearity and hysteresis effects also bring a burden to robot modeling. Moreover, following their flexibility and adaptation, soft robot control is more challenging than rigid robot control. In order to model and control soft robots, a large number of data models are utilized in pairs or separately. This review classifies these applied data models into five kinds, which are the Jacobian model, analytical model, statistical model, neural network, and reinforcement learning, and compares the modeling and controller features, e.g., model dynamics, data requirement, and target task, within and among these categories. A discussion about the development of the existing modeling and control approaches is presented, and we forecast that the combination of offline-trained and online-learning controllers will be the widespread implementation in the future.