2.2.2 Cable-driven (tendon-driven) actuators

In cable-driven or tendon-driven soft robots, the actuators are situated outside of the robot structures; therefore, they do not interfere with movements of the soft bodies. Instead, cables connected to the actuators transmit the tensions through the cable paths or routings, which are embedded in a soft structure [100]. When it comes to controlling the soft robots, a major problem for the cable-driven mechanisms comes from non-linearity and hysteresis. These issues are mainly caused by high friction between a cable and cable path due to tension of the cable and the bending of the cable path [101].

A supervised learning-based method was proposed with respect to soft manipulators as a solution of the inverse statics problem to realize effective grasping. M. Giorelli et al. implemented an FNN for non-constant curvature manipulators to solve inverse kinematics [102, 103]. The performance of the FNN-based model was experimentally tested by comparing with model-based numerical approach and Jacobian-based method, which requires numerical resolution of integrals along the structure as proposed in [104, 105], for a conical soft manipulator driven by two cables. Based on the results, the FNN showed better performances and faster convergence than the model-based numerical method; however, FNN required model optimization and a bigger dataset [102].

Model-free control strategies based on RNNs were developed to learn the dynamics of robots. For a soft robot with friction-manipulation mechanisms driven by a motor-tendon combination that is capable of terrestrial locomotion, the model-free control framework was experimentally applied to the robot designs while changing the shape of tendon paths, friction mechanisms, and environmental conditions [100]. Nakajima et al. demonstrated a soft silicone arm system that can be employed to deal with the transient dynamics of the soft materials based on RNN and suggested its applicability to a real-world problem [106]. Ansari et al. conducted a study on a soft robot arm module actuated by tendon-based and pneumatic-based actuators for a bathing task for elderly people. Model-free control using reinforcement learning was developed to simultaneously increase the stiffness and positioning capacities [107]. Thuruthel et al. tested a tendon-driven soft manipulator under a simulated environment, in addition to a pneumatically-driven soft manipulator, using model-based reinforcement learning for closed-loop dynamic control. For the forward dynamic model, an RNN was used. Based on the learned dynamic models, a trajectory optimization was implemented to develop an open loop controller; however, the authors reported that the open loop controller is not robust against external disturbances [108]. To overcome this limitation, a model-based policy learning method for the closed-loop dynamic control of a soft robotic manipulator using an RNN was proposed. The representation of the policy architecture allows for the stability of the controller with respect to changes in the control frequency, sensory noise, and dynamics. With respect to the simulation of tendon-driven soft manipulators and experimental evaluations of under-actuated pneumatically-driven soft manipulators, sufficient accuracy levels were maintained, and the control frequency was decreased by a maximum factor of 5 [109].

Previously, machine learning in cable-driven or tendon-driven actuators of soft robots was focused on increasing the performances of position control. Rather than position control, soft manipulators and soft wearable robots with cable-driven actuators require end-effectors’ force or cable tension to generate proper contacting forces in accordance with various object characteristics. However, due to the non-linear characteristics of friction and fatigue with the cable and the cable path, degradations of the cable over time under various loads and situations is still limited in soft robotic systems. As future works, a real-time applicable learning method should be developed by collecting time sequence data of cable tension and the configuration of the soft robot to estimate the precise force control of the soft robots [101].