Soft robots' ability to safely navigate complex environments motivates the development of algorithms for accurate environmental interaction assessment, enabling greater autonomy. Specifically, strain-based shape and force estimation of continuum robots with embedded soft sensors poses an open challenge mainly owing to continuous softness, anisotropic deformation, and non-linear properties. Mathematical description of deformable soft bodies and accurate estimation of external forces are crucial for achieving controllable and intelligent behaviors of these robots. In this paper, a kinetostatic strain-based modeling for rod-driven soft robots (RDSR) with embedded stretch sensors is proposed, which incorporates local strains, actuation variables, and external interactions. The strain model enables full shape estimation of the robot and prediction of strain variations in soft bodies. Building on this, we develop a force estimator based on predicted and measured sensor and actuator lengths to evaluate 3D external forces, accounting for both orthogonal and tangential components relative to the backbone. Moreover, we introduce a methodology using a novel ellipsoid representation to handle tangential forces that may become insensitive in certain singular configurations. This estimator allows us to either disregard such forces when they do not influence deformation or estimate them when they become observable. Our simulations and experiments demonstrate how this approach can be used to analyze the robot's configuration and successfully estimate external forces. Finally, it is demonstrated that when the continuum arm follows trajectories with higher strain sensitivity, tangential force estimation is significantly improved.

High precision control of soft robots is challenging due to their stochastic behavior and material-dependence nature. While RL has been applied in soft robotics, achieving precision in task execution is still a long way off. Traditionally, RL requires substantial data for convergence, often obtained from a training environment. Yet, despite exhibiting high accuracy in the training environment, RL-policies often fall short in reality due to the training-to-reality gap, and the performance is exacerbated by the stochastic nature of soft robots. This study paves the way for the implementation of RL for soft robot control to achieve high precision in task execution. Two sample-efficient adaptive control strategies are proposed, that leverage the RL-policy. The schemes can overcome stochasticity, bridge the training-to-reality gap, and attain desired accuracy even in challenging tasks such as obstacle avoidance. Additionally, deliberate and reversible damage is induced to the pneumatic actuation chamber, altering the soft robot's behavior to test the adaptability of our solutions. Despite the damage, desired accuracy was achieved in most scenarios without needing to retrain the RL-policy.

In this paper, we propose a novel dynamic gait controller for the repetitive behavior of soft robot manipulators performing routine tasks. Compliance with soft robots is advantageous when the robot interacts with living organisms and other fragile objects. However, predicting and controlling repetitive behavior is challenging because of hysteresis and non-linear dynamics governing the interactions. Existing priorfree methods track the dynamic state using recurrent neural networks or rely on known generalized coordinates describing the robot's state. We propose to model the interaction induced by the repetitive behavior as gait dynamics and represent the dynamic state with Central Pattern Generator (CPG) tracking the motion phase and thus reduce the complexity of the robot's forward model. The proposed method bootstraps an ensemble of the forward models exploring multiple dynamic contexts that are expanded as it searches for repetitive motion producing the target repetitive behavior. The proposed approach is experimentally validated on a pneumatically actuated soft robot arm I-Support, where the method infers gaits for different targets.