Soft continuum manipulators are celebrated for their versatility and physical robustness to external forces and perturbations. However, this feature comes at a cost. The many degrees of freedom and compliance pose challenges for accurate pose reconstruction, both in terms of distributed sensing and pose reconstruction algorithms. Moreover, soft arms are inherently susceptible to deformation from external forces or loads, meaning that closed-loop control is essential for robust task performance. In this article, we propose the integration of multiple Inertial Measurement Units (IMUs) of a soft robot arm, Helix, for reconstruction of pose under internal and external forces. Furthermore, we integrate this dynamic pose reconstruction for kinematic-based closed-loop control strategies. By serially integrating sensing in the body of the Helix soft manipulator, we provide the system with high-frequency pose reconstruction and demonstrate improvements in end effector position with comparison to open-loop performance.

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Many interesting designs of soft robots with variablestiffness capabilities have been presented in the literature. However, little attention has been given on the control of their embedded physical intelligence. In this work, we present an algorithm that exploits the variablejoint stiffness capabilities and the redundancy of a soft manipulator to achieve Cartesian stiffness control at the end effector,thanks to model-based and optimal control techniques. The algorithm is validated both analytically and in the real world. In particular, we present a tendon-driven soft manipulator, equippedwith variable-stiffness segments and proprioceptive sensing. The robot is used as a platform to test the algorithm in real tasks, such as fitting a peg in the hole. Thanks to accurate modeling, the soft manipulator is able to obtain the desired stiffness at the end effector over the workspace.