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Large-scale soft robots have the capability and potential to perform highly dynamic tasks such as hammering a nail into a board, throwing items long distances, or manipulating objects in cluttered environments. This is due to their joints being underdamped and their ability to store potential energy. The soft robots presented in this article are pneumatically actuated and thus have the ability to perform these tasks without the need for large motors or gear trains. However, getting soft robots to perform highly dynamic tasks requires controllers that can track highly dynamic trajectories to complete those tasks. For soft robots, this is a difficult problem to solve due to the uncertainty in their shape and their complicated dynamics and kinematics. This article presents a formulation of a model reference adaptive controller (MRAC) that causes a three-link soft robot arm to behave like a highly dynamic 2nd-order critically damped system. Using the dynamics of a 2nd-order system, we also present a method to generate joint trajectories for throwing a ball to a desired point in Cartesian space. We demonstrate the viability of our joint-level controller in simulation and on hardware with a reported maximum root mean square error of 0.0872 radians between a reference and executed trajectory. We also demonstrate that our combined MRAC controller and trajectory generator can, on average, throw a ball to within 25–28% of a desired landing location for a throwing distance of between 1.5 and 2 m on real hardware. 

A network of stretchable optical waveguides is created and tested under locally applied lateral force and stretching. The network is comprised of urethane waveguides formed into interconnected junctions. These junctions split light pulses sent from a time-of-flight sensor, causing them to travel down different paths throughout the network and are input agnostic. The different split pulses' arrival times and amplitudes can be used to detect these deformations, differentiate between types of deformations and locate which junctions they occurred between.le 

Not AvailablePose measurement and contact realization for soft robots are important but are also challenging to perform. In this article, a new approach is proposed which uses light transmission through the bore of the robot to sense its pose. By combining optical signals with inertial measurement units and pressure signals, along with governing models and machine learning algorithms, this sensing approach allows us to measure the pose of the soft robot in free space. It also allows the applied machine learning algorithm to estimate the robot configuration during contact events, predicting the location, direction, and magnitude of the contact force by learning from prior contact data in a training phase. The sensing devices used are affordable and widely available so that the robot can be mass-produced. Experimental results show that the proposed sensing system successfully estimates these quantities with good accuracy. 

Soft manipulators, renowned for their compliance and adaptability, hold great promise in their ability to engage safely and effectively with intricate environments and delicate objects. Nonetheless, controlling these soft systems presents distinctive hurdles owing to their nonlinear behavior and complicated dynamics. Learning‐based controllers for continuum soft manipulators offer a viable alternative to model‐based approaches that may struggle to account for uncertainties and variability in soft materials, limiting their effectiveness in real‐world scenarios. Learning‐based controllers can be trained through experience, exploiting various forward models that differ in physical assumptions, accuracy, and computational cost. In this article, the key features of popular forward models, including geometrical, pseudo‐rigid, continuum mechanical, or learned, are first summarized. Then, a unique characterization of learning‐based policies, emphasizing the impact of forward models on the control problem and how the state of the art evolves, is offered. This leads to the presented perspectives outlining current challenges and future research trends for machine‐learning applications within soft robotics. 

You can print anything... or can you? 3D printing is an exciting new technology that promises to very quickly create anything people can design. Scientists who want to make soft robots, like Baymax from Big Hero 6^TM, are excited about 3D printers. Our team uses 3D printing to make molds to produce soft robots. Molding is like using a muffin tin to make cupcakes. But can you make anything with 3D printing or are there times when 3D-printed molds do not work? Just like a cupcake liner, 3D-printed molds leave ridges, like a Ruffles potato chip, in soft robots. These ridges are a weak point where cracks can form, causing the robot to pop like a balloon. To prevent this, we sometimes need to make our robots using very smooth molds made from metal. This article talks about when and how 3D printing is useful in making soft robots. 

Aggressive and accurate control of complex dynamical systems, such as soft robots, is especially challenging due to the difficulty of obtaining an accurate and tractable model for real-time control. Learned dynamic models are incredibly useful because they do not require derivation of an analytical model, they can represent complex, nonlinear behavior directly from data, and they can be evaluated quickly on graphics-processing units (GPUs). In this paper, we present an open-source Python library to further current research in model-based control of soft robot systems. Our library for Modeling of Learned Dynamics (MoLDy), is designed to generate learned forward models of complex systems through a data-driven approach to hyperparameter optimization and learned model training. Included in the MoLDy library, we present an open-source version of NEMPC (Nonlinear Evolutionary Model Predictive Control), a previously published control algorithm validated on soft robots. We demonstrate the ability of MoLDy and NEMPC to accurately perform modelbased control on a physical pneumatic continuum joint. We also present a benchmarking study on the effect of the loss metric used in model training on control performance. The results of this paper serve to guide other researchers in creating learned dynamic models of novel systems and using them in closed-loop control tasks.