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.

Manipulators based on soft robotic technologies exhibit compliance and dexterity which ensures safe human-robot interaction. This article is a novel attempt at exploiting these desirable properties to develop a manipulator for an assistive application, in particular, a shower arm to assist the elderly in the bathing task. The overall vision for the soft manipulator is to concatenate three modules in a serial manner such that (i) the proximal segment is made up of cable-based actuation to compensate for gravitational effects and (ii) the central and distal segments are made up of hybrid actuation to autonomously reach delicate body parts to perform the main tasks related to bathing. The role of the latter modules is crucial to the application of the system in the bathing task; however, it is a nontrivial challenge to develop a robust and controllable hybrid actuated system with advanced manipulation capabilities and hence, the focus of this article. We first introduce our design and experimentally characterize its functionalities, which include elongation, shortening, omnidirectional bending. Next, we propose a control concept capable of solving the inverse kinetics problem using multiagent reinforcement learning to exploit these functionalities despite high dimensionality and redundancy. We demonstrate the effectiveness of the design and control of this module by demonstrating an open-loop task space control where it successfully moves through an asymmetric 3-D trajectory sampled at 12 points with an average reaching accuracy of 0.79 cm + 0.18 cm. Our quantitative experimental results present a promising step toward the development of the soft manipulator eventually contributing to the advancement of soft robotics.

The central concept of this letter is to develop an assistive manipulator that can automate the bathing task for elderly citizens. We propose to exploit principles of soft robotic technologies to design and control a compliant system to ensure safe human-robot interaction, a primary requirement for the task. The overall system is intended to be modular with a proximal segment that provides structural integrity to overcome gravitational challenges and a distal segment to perform the main bathing activities. The focus of this letter is on the design and control of the latter module. The design comprises of alternating tendons and pneumatics in a radial arrangement, which enables elongation, contraction, and omnidirectional bending. Additionally, a synergetic coactivation of cables and tendons in a given configuration allows for stiffness modulation, which is necessary to facilitate washing and scrubbing. The novelty of the work is twofold: 1) Three base cases of antagonistic actuation are identified that enable stiffness variation. Each category is then experimentally characterized by the application of an external force that imposes a linear displacement at the tip in both axial and lateral directions. 2) The development of a novel algorithm based on cooperative multiagent reinforcement learning that simultaneously optimizes stiffness and position. The results highlight the effectiveness of the design and control to contribute toward the development of the assistive device

Flexible manipulators based on soft robotic technologies demonstrate compliance and dexterous maneuverability with virtually infinite degrees-of-freedom. Such systems have great potential in assistive and surgical fields where safe human-robot interaction is a prime concern. However, in order to enable practical application in these environments, intelligent control frameworks are required that can automate low-level sensorimotor skills to reach targets with high precision. We designed a novel motor learning algorithm based on cooperative Multi-Agent Reinforcement Learning that enables high-dimensional manipulators to exploit an abstracted state-space through a reward-guided mechanism to find solutions that have a guaranteed precision. We test our algorithm on a simulated planar 6-DOF with a discrete action-set and show that the all the points reached by the manipulator average an accuracy of 0.0056m (±0.002). The algorithm was found to be repeatable. We further validated our concept on the Baxter robotic arm to generate solutions up to 0.008m, exceptions being the joint angle accuracy and calibration of the robot

We present a novel soft limb quadruped robot "FASTT," with a simple and cheap design of its legs for dynamic locomotion aimed to expand the applications of soft robotics in mobile robots. The pneumatically actuated soft legs are self-stabilizing, adaptive to ground, and have variable stiffness, all of which are essential properties of locomotion that are also found in biological systems. We tested the soft legs for the pace, trot, and gallop gait and found them to move with a forward velocity for each gait with robustness. The legs were able to produce a flight and stance phase as a result of the body-environment interaction and also support the weight of the body while two legs were in flight phase and two in stance phase. The soft robot also exhibited two different postures i.e. sprawl and semi-erect which can also be found in some biological species as the crocodile. Moreover, the robot is safe to interact with. The results highlight the effectiveness of the soft limbs to produce dynamic locomotion which provides potential for application in uncertain environments