Model-Based Control of Soft Actuators Using Learned Non-linear Discrete-Time Models

Abstract

Soft robots have the potential to significantly change the way that robots interact with the environment and with humans. However, accurately modeling soft robot and soft actuator dynamics in order to perform model-based control can be extremely difficult. Deep neural networks are a powerful tool for modeling systems with complex dynamics such as the pneumatic, continuum joint, six degree-of-freedom robot shown in this paper.Unfortunately it is also difficult to apply standard model-based control techniques using a neural net. In this work, we show that the gradients used within a neural net to relate system states and inputs to outputs can be used to formulate a linearized discrete state space representation of the system. Using the state space representation, model predictive control (MPC) was developed with a six degree of freedom pneumatic robot with compliant plastic joints and rigid links. Using this neural net model, we were able to achieve an average steady state error across all joints of approximately 1 and 2 • with and without integral control respectively. We also implemented a first-principles based model for MPC and the learned model performed better in terms of steady state error, rise time, and overshoot. Overall, our results show the potential of combining empirical modeling approaches with model-based control for soft robots and soft actuators.

Review of machine learning methods in soft robotics

“…Obtaining a kinematic or dynamic model of a soft robot has been a challenge in model-based control strategies. To overcome such limitation, learning algorithms have been applied to acquire the kinematic or dynamic model of soft robots based on SPAs [ 85 – 88 ]. An FNN and radial basis function (RBF) neural networks were applied to the inverse or forward kinematic modeling of a soft continuum robot based on SPAs including 3-Dimensional motions [ 85 , 86 ].…”

“…An FNN and radial basis function (RBF) neural networks were applied to the inverse or forward kinematic modeling of a soft continuum robot based on SPAs including 3-Dimensional motions [ 85 , 86 ]. M. Gillespie et al and P. Hyatt et al proposed a predictive model based on the neural networks, and a learning method for the linearized discrete state space representation of soft robots [ 87 , 88 ]. G. Fang et al developed a learning method based on the local Gaussian Process Regression (GPR) to estimate the motion of SPAs using the kinematic model from the control inputs to the manipulator configurations based on the sequential camera images [ 89 ].…”

“…Commercially available sensors, like depth cameras, film-based flex sensors, and potentiometers, are generally used to estimate the configurations of SPAs with machine learning techniques [ 85 , 88 , 89 , 91 ]. On the other hand, as the traditional sensors can be relatively too rigid to be compatible with SPAs that are highly deformable, soft sensors have often been integrated with SPAs to estimate the configurations of soft robots.…”

Model Reference Predictive Adaptive Control for Large-Scale Soft Robots

“…Control strategies for soft robots vary from open-loop control such as in Shepherd et al ( 2011 ) and Tolley et al ( 2014 ) to Reinforcement Learning (Zhang et al, 2017 ) to model predictive control (Best et al, 2016 ). In Hyatt et al ( 2019 ) and Hyatt and Killpack ( 2020 ) the authors demonstrate the performance of MPC on the same joints used for this work. These implementations of MPC used a learned model of the dynamics based on a less-accurate representation of the continuum joint dynamics.…”

Sensuator: A Hybrid Sensor–Actuator Approach to Soft Robotic Proprioception Using Recurrent Neural Networks

“…To date, the aforementioned approaches have relied on purposefully built sensor structures within the soft robot and may require the use of machine learning to fully utilize the sensor responses. In a similar vein, with the non-linear behavior surrounding the materials and unstructured nature of soft robotics, various facets of soft robotics research have turned to the use of data-driven or deep learning based methods for tasks such as design and fabrication [17], sensing [18,19], state estimation [20,21] and control [22][23][24].…”