Editorial: Advances in Modeling and Control of Soft Robots

Advances in Modelling and Control of Soft RobotsThe emerging field of soft robotics is nowadays looking at innovative ways to create and apply robotic technology in our lives.It is a relatively new domain in the field of robotics, but one that has a lot of potential to change how we relate with robots and also how they are used.In natural environments and human-centric operations, where safety and adaptability to uncertainty are fundamental requirements, soft robots may beneficially show these characteristics: they can conform to variable but sensitive environments, adaptively move, manipulate and grasp unknown objects varying in size and shape and their soft condition allows them to squeeze through confined spaces.In addition to the many challenges and research achievements on the material side, actuation and sensing techniques, and fabrication technologies, the questions on how to model soft robots and how to control their movements are challenging scientifically and important from the application perspective.Classical control approaches in robotics are nonlinear-model-based.However, the highly complex and nonlinear models necessary for a soft robotic system make this approach a difficult task and therefore seem to come to a limit in the presence of soft robots.Therefore, other methods have been applied seemingly being more useful in this context, such as learning-based control algorithms, model-free approaches like bang-bang control, control algorithms motivated by neuroscience, or morphological computation.These methods add new perspectives to the wellknown model-based approach.Such research challenges and the current achievements in the field have been discussed by the soft robotics community in a second forum on this topic, at the second workshop on "Advances in Soft Robots Control", held on November 4th, 2019, in Macau, China, during the 2019 IEEE/RSJ International Conference of Intelligent Robots and Systems (IROS 2019).The workshop wanted to answer questions like "Do we have to rethink the basic approach in robot control, which is modelbased, when it comes to controlling soft robotic systems?"The papers collected in this issue come from that discussion and compose the pictures of the achievements presented, with extensions following the discussion and analysis done in that interactive context.They cover achievements from the theoretical modeling of soft robots to their control, up to specific application-driven developments.A few works answer the workshop question by rethinking the modeling approaches and techniques.They address dynamic modeling and related control of soft robots, starting from the current approaches based on statics, or second-order dynamics, and model predictive control (MPC), using basic lumped-parameters.Thuruthel et al. show how the dynamic model of a soft robot can be reduced to first-order dynamical equation, thanks to high damping and low inertia, with minimal loss in accuracy.The work by Hyatt et al. demonstrates that online model adaptation is key in soft robot dynamic modeling and shows their results with a model predictive control.It is based on

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