Editorial: Machine Learning Techniques for Soft Robots

Abstract

Soft robotic technologies have introduced new paradigms in the design and development of robots. This shift in outlook presents new challenges and opportunities for modeling, control, and design of these robots. Traditional techniques based on analytical models have proven to be insufficient to tackle these new challenges. This is because of their highly nonlinear, time-varying and high-dimensional characteristics coupled with an immense diversity in their design. Machine learning-based approaches provide a promising alternative to traditional analytical approaches. Learningbased approaches have proven to be a valuable tool for control, data-processing, and design optimization of nonlinear systems in traditional robotics and other scientific disciplines. However, their usage has been largely limited and unexplored in soft robotics, in spite of their potential value.