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Soft robotic grippers are shown to be high effective for grasping unstructured objects with simple sensing and control strategies. However, they are still limited by their speed, sensing capabilities and actuation mechanism. Hence, their usage have been restricted in highly dynamic grasping tasks. This paper presents a soft robotic gripper with tunable bistable properties for sensor-less dynamic grasping. The bistable mechanism allows us to store arbitrarily large strain energy in the soft system which is then released upon contact. The mechanism also provides flexibility on the type of actuation mechanism as the grasping and sensing phase is completely passive. Theoretical background behind the mechanism is presented with finite element analysis to provide insights into design parameters. Finally, we experimentally demonstrate sensor-less dynamic grasping of an unknown object within 0.02 seconds, including the time to sense and actuate.

Soft robots are diverse time varying dynamical systems. Hence models describing their behavior have to constantly adapt to accommodate these changes. This is a challenging problem due to the complexities involved with modelling a soft robot and the diverse array of available modelling approaches. Adaptive controllers that can accommodate for model uncertainties are a viable option in this case. This paper presents a cerebellum-inspired adaptive kinematic controller. The adaptive controller is built on top of an approximate inverse kinematic model. The proposed architecture guarantees error convergence without a priori knowledge about the approximate inverse kinematic controller and learns using only task space error information. Simulation results show how such an architecture can provide monotonously decreasing tracking error reductions online. The robustness of the algorithm to changes in the inverse kinematic component and morphology of the soft robot itself is shown.

Design of continuum manipulators have always been restricted to simplified structures due to the modeling complexity involved. However with the success of machine learning based models, there are potentially more structures and actuation strategies that could be investigated. This paper presents a novel continuum manipulator with variable diameter and a general control strategy for closed loop task space control. The controller constitutes of an inverse kinematics based feedback component, a forward kinematics based feedforward component and a low level velocity controller. Our findings indicate how such an approach can deal with singularities and provide smooth and accurate motion.