soft robotics engineers

Abstract: Soft electroactive composites can exhibit strongly architecture-dependent mechanical and electrical responses, but explicitly resolving dense fiber networks in three dimensions is computationally expensive. We develop a mixed-dimensional finite-element formulation in which electroactive fibers are represented by geometrically exact beams embedded in a deformable dielectric matrix. Unlike existing electroactive beam formulations in which the electric potential is defined directly on the beam, the embedded fibers here are driven by the three-dimensional electric field of the surrounding matrix. The matrix field is sampled along the beam centerlines and enters the beam dielectric enthalpy directly, providing two-way electromechanical coupling without introducing independent electric-potential degrees of freedom on the beams. Beam and matrix mechanics are coupled through projected mortar constraints, and the electromechanical problem is solved monolithically. We use the formulation as the microscale model in a periodic homogenization framework to compute effective stress and electric displacement. Verification studies quantify discretization and field-sampling sensitivity, followed by structured and irregular network examples that demonstrate architecture-dependent mechanical and electrical responses without body-fitted three-dimensional fiber meshes.

Abstract: Jellyfish-inspired robots offer a compliant and efficient approach to underwater locomotion, but achieving large deformation together with repeatable actuation and closed-loop control remains challenging. In this work, we present a tendon-driven robotic jellyfish with constrained soft actuation. Each actuator combines a flexible substrate with discrete constraints, enabling bending up to \(150^\circ\) with an approximately linear tendon displacement-bending relationship. Eight actuators driven by four servos allow the robot to perform stable swimming, attitude adjustment, and self-righting. Based on the linear actuation, a reinforcement-learning controller is further developed, enabling closed-loop depth regulation in both simulation and physical experiments. These results show that mechanical constraints can improve the controllability of soft actuation while preserving compliant jellyfish-like motion, providing a route toward manoeuvrable and autonomous jellyfish robots.

Abstract: Octopus crawling motivates soft robots that exploit redundancy, yet discovering and organizing diverse coordination modes for adaptation remains challenging. To address this, we introduce a Diffusion-based Uncertainty-aware Optimization (DUO) algorithm that learns demonstration-free crawling controllers for a simulated, muscle-actuated CyberOctopus. This work represents the first application of diffusion-based control to soft multi-arm robots in contact-rich simulations. By embedding a variety of locomotion behaviors within a shared control distribution, this approach enables the simulated octopus to navigate dynamic physical constraints, demonstrating that learned coordination diversity inherently facilitates robust adaptation. The main contributions include: (i) a symmetry-structured policy representation that folds radially equivalent controllers into a canonical directional sector, (ii) an online black-box optimization strategy, the DUO algorithm, that discovers and retains diverse coordination modes, and (iii) a control editing technique that adapts existing controllers to novel actuator constraints without retraining. These results show how learned coordination diversity makes motor abundance a practical resource for adaptation in soft multi-arm robots.