The high deformability and compliance of soft robots allow safer interaction with the environment. On the other hand, these advantages bring along controllability and predictability challenges which result in loss of force and stiffness output. Such challenges should be addressed in order to improve the overall functional performance and to meet the requirements of real-scenario applications. In this paper, we present a bidirectional in-plane manipulator which consists of two unidirectional fiber-reinforced actuators (FRAs) and a hybrid soft-rigid stiffness control structure (SCS), all of them controlled by air pressure. Both controllability and predictability of the manipulator are enhanced by the hybrid soft-rigid structure. While the FRAs provide positioning and position dependent stiffness, the SCS increases the stiffness of the manipulator without position dependency. The SCS is able to increase the manipulator stiffness by 35%, 30%, and 18%, when one FRA is pressurized at 150 kPa, 75 kPa, and 0 kPa, respectively. Experiments are carried out to present the feasibility of the proposed manipulator.

Robots built from soft materials can alter their shape and size in a particular profile. This shape-changing ability could be extremely helpful for rescue robots and those operating in unknown terrains and environments. In changing shape, soft materials also store and release elastic energy, a feature that can be exploited for effective robot movement. However, design and control of these moving soft robots are nontrivial. This paper presents design methodology for a 3-D printed motor-tendon actuated soft robot that is capable of locomotion. The modular design of the robot facilitates rapid fabrication, deployment, and repair. In addition to shape change, the robot uses friction-manipulation mechanisms to effect locomotion. The motor-tendon actuators are comprised of nylon tendons embedded inside the soft body structure along a given path with one end fixed on the body and the other attached to a motor. These actuators directly control the deformation of the soft body, which influences the robot locomotion behavior. Static stress analysis is used as a tool for designing the shape of the paths of these tendons embedded inside the body. This paper also presents a novel model-free learning-based control approach for soft robots, which interact with the environment at discrete contact points. This approach involves discretization of factors dominating robot-environment interactions as states, learning the results as robot transitions between these robot states, and evaluation of desired periodic state control sequences optimizing a cost function corresponding to a locomotion task (rotation or translation). The clever discretization allows the framework to exist in a robot's task space, hence facilitating calculation of control sequences without modeling the actuator, body material, or details of the friction mechanisms. The flexibility of the framework is experimentally explored by applying it to robots with different friction mechanisms and different shapes of tendon paths.