Data-driven Explainable Controller for Soft Robots based on Recurrent Neural Networks

The nonlinearity and hysteresis of soft robot motions have posed challenges in accurate soft robot control. Neural networks, especially recurrent neural networks (RNNs), have been widely leveraged for this issue due to their nonlinear activation functions and recurrent structures. Although they have shown satisfying accuracy in most tasks, these black-box approaches are not explainable, and hence, they are unsuitable for areas with high safety requirements, like robot-assisted surgery. Based on the RNN controllers, we propose a data-driven explainable controller (DDEC) whose parameters can be updated online. We discuss the Jacobian controller and kinematics controller in theory and demonstrate that they are only special cases of DDEC. Moreover, we utilize RNN, the Jacobian controller, the kinematics controller, and DDECs for trajectory following tasks. Experimental results have shown that our approach outperforms the other controllers considering trajectory following errors while being explainable. We also conduct a study to explore and explain the functions of each DDEC component. This is the first interpretable soft robot controller that overcomes the shortcomings of both NN controllers and interpretable controllers. Future work may involve proposing different DDECs based on different RNN controllers and exploiting them for high-safety-required applications.

A Novel and Accurate BiLSTM Configuration Controller for Modular Soft Robots with Module Number Adaptability

Modular soft robots have shown higher potential in sophisticated tasks than single-module robots. However, the modular structure incurs the complexity of accurate control and necessitates a control strategy specifically for modular robots. In this paper, we introduce a data collection strategy and a novel and accurate bidirectional LSTM configuration controller for modular soft robots with module number adaptability. Such a controller can control module configurations in robots with different module numbers. Simulation cable-driven robots and real pneumatic robots have been included in experiments to validate the proposed approaches, and we have proven that our controller can be leveraged even with the increase or decrease of module number. This is the first paper that gets inspiration from the physical structure of modular robots and utilizes bidirectional LSTM for module number adaptability. Future work may include a planning method that bridges the task and configuration spaces and the integration of an online controller.

A Hybrid Adaptive Controller for Soft Robot Interchangeability

Soft robots have been leveraged in considerable areas like surgery, rehabilitation, and bionics due to their softness, flexibility, and safety. However, it is challenging to produce two same soft robots even with the same mold and manufacturing process owing to the complexity of soft materials. Meanwhile, widespread usage of a system requires the ability to fabricate replaceable components, which is interchangeability. Due to the necessity of this property, a hybrid adaptive controller is introduced to achieve interchangeability from the perspective of control approaches. This method utilizes an offline trained recurrent neural network controller to cope with the nonlinear and delayed response from soft robots. Furthermore, an online optimizing kinematics controller is applied to decrease the error caused by the above neural network controller. Soft pneumatic robots with different deformation properties but the same mold have been included for validation experiments. In the experiments, the systems with different actuation configurations and the different robots follow the desired trajectory with errors of 0.040 and 0.030 compared with the working space length, respectively. Such an adaptive controller also shows good performance on different control frequencies and desired velocities. This controller endows soft robots with the potential for wide application, and future work may include different offline and online controllers. A weight parameter adjusting strategy may also be proposed in the future.