MS182 - Computational Methods for Soft Robotics

Organized by: R. Sachse (Technical University of Munich, Germany), J. Hughes (École Polytechnique Fédérale de Lausanne, Switzerland) and E. Milana (University of Freiburg, Germany)
Keywords: Control Algorithms, Embodied Intelligence, Motion Planning, Physical Intelligence, Soft Actuators, Soft Sensors, Soft Robotics
Soft robotics is a promising and innovative field that offers remarkable advantages due to its compliant and flexible nature, enabling safe interactions with humans and delicate objects. Moreover, integrating embodied intelligence further enhances soft robots, granting them the ability to learn, adapt, and interact intelligently with the environment, making them more versatile, responsive, and user-friendly. This combination of soft materials and embodied intelligence is promising for transforming industries, healthcare, and everyday life with innovative and human-centric robotic solutions. In developing soft robotic medical devices, computational mechanics plays a vital role as it enables accurate modeling and simulation of the complex and deformable structures in soft robots. This precision is crucial for designing, optimizing and controlling of soft robotic systems, especially in medical engineering applications where soft robots can revolutionize minimally invasive surgeries, enhance rehabilitation, and provide personalized medical assistance, all while conforming to delicate biological tissues with enhanced safety and efficacy. This mini-symposium aims to provide a platform to present latest research, exchange ideas, and address challenges in applying computational methods to soft robotics and embodied intelligence. Topics of interest include, but are not limited to: - Computational modeling of soft robots by advanced numerical methods and state-of-the-art tools for modeling the complex behavior of soft robotic structures and materials. - Optimization methods to improve the performance and design of soft robotic systems. - Computational mechanics of soft sensors and actuators. - Machine learning for computational soft robotics. - Multiscale and multiphysics simulations for soft robotic systems. - Interaction of soft robots with biological systems and the human body. - Model-based control in soft robotic applications.