  Controller design for soft robots is challenging due to nonlinear deformation
and high degrees of freedom of flexible material. The data-driven approach is a
promising solution to the controller design problem for soft robots. However,
the existing data-driven controller design methods for soft robots suffer from
two drawbacks: (i) they require excessively long training time, and (ii) they
may result in potentially inefficient controllers. This paper addresses these
issues by developing two memory-based controllers for soft robots that can be
trained in a data-driven fashion: the finite memory controller (FMC) approach
and the long short-term memory (LSTM) based approach. An FMC stores the
tracking errors at different time instances and computes the actuation signal
according to a weighted sum of the stored tracking errors. We develop three
reinforcement learning algorithms for computing the optimal weights of an FMC
using the Q-learning, soft actor-critic, and deterministic policy gradient
(DDPG) methods. An LSTM-based controller is composed of an LSTM network where
the inputs of the network are the robot's desired configuration and current
configuration. The LSTM network computes the required actuation signal for the
soft robot to follow the desired configuration. We study the performance of the
proposed approaches in controlling a soft finger where, as benchmarks, we use
the existing reinforcement learning (RL) based controllers and
proportional-integral-derivative (PID) controllers. Our numerical results show
that the training time of the proposed memory-based controllers is
significantly shorter than that of the classical RL-based controllers.
Moreover, the proposed controllers achieve a smaller tracking error compared
with the classical RL algorithms and the PID controller.
