With the rise of soft robotics technology and applications, there have been increasing interests in the development of controllers appropriate for their particular design. Being fundamentally different from traditional rigid robots, there is still not a unified framework for the design, analysis, and control of these high-dimensional robots. This review article attempts to provide an insight into various controllers developed for continuum/soft robots as a guideline for future applications in the soft robotics field. A comprehensive assessment of various control strategies and an insight into the future areas of research in this field are presented.

Control Strategies for Soft Robotic Manipulators: A Survey.

Dynamic control of soft robotic manipulators is an open problem yet to be well explored and analyzed. Most of the current applications of soft robotic manipulators utilize static or quasi-dynamic controllers based on kinematic models or linearity in the joint space. However, such approaches are not truly exploiting the rich dynamics of a soft-bodied system. In this paper, we present a model-based policy learning algorithm for closed-loop predictive control of a soft robotic manipulator. The forward dynamic model is represented using a recurrent neural network. The closed-loop policy is derived using trajectory optimization and supervised learning. The approach is verified first on a simulated piecewise constant strain model of a cable driven under-actuated soft manipulator. Furthermore, we experimentally demonstrate on a soft pneumatically actuated manipulator how closed-loop control policies can be derived that can accommodate variable frequency control and unmodeled external loads.

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Model-Based Reinforcement Learning for Closed-Loop Dynamic Control of Soft Robotic Manipulators

Challenges of continuum robots in clinical context: a review

Herein, the progress of machine learning methods in the field of soft robotics, specifically in the applications of sensing and control, is outlined. Data‐driven methods such as machine learning are especially suited to systems with governing functions that are unknown, impractical or impossible to represent analytically, or computationally intractable to integrate into real‐world solutions. Function approximation with careful formulation of the machine learning architecture enables the encoding of dynamic behavior and nonlinearities, with the added potential to address hysteresis and nonstationary behavior. Supervised learning and reinforcement learning in simulation and on a wide variety of physical robotic systems have shown promising results for the use of empirical data‐driven methods as a solution to contemporary soft robotics problems.

https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.201900171

Machine Learning for Soft Robotic Sensing and Control

This article reviews soft pneumatic actuators (SPAs) that are manufactured entirely via additive manufacturing methods. These actuators are known as four-dimensional (4D)-printed SPAs and can gener...

/pdf/3d-4d-printed-bending-type-soft-pneumatic-actuators-36axuqgq3s.pdf

3D/4D-printed bending-type soft pneumatic actuators: fabrication, modelling, and control

This article introduces a machine-learning-based approach for closed loop kinematic control of continuum manipulators in the task space For this purpose, we propose a unique formulation for learning the inverse kinematics of a continuum manipulator while integrating end-effector feedback We demonstrate that this model-free approach for kinematic control is very well suited for nonlinear stochastic continuum robots The article addresses problems that are vital for practical realization of machine-learning techniques The primary objective is to solve the redundancy problem while making the algorithm scalable, fast, and tolerant to stochasticity, requiring minimal sensor elements and involving few open parameters for tuning In addition, we demonstrate that the proposed controller can exhibit adaptive behavior in the presence of external forces and in an unstructured environment with the help of the morphological properties of the manipulator Experimental validation of the proposed controller i

George ThuruthelThomas

Papers

Learning Closed Loop Kinematic Controllers for Continuum Manipulators in Unstructured Environments