Soft Robot Control

Soft robot control focuses on developing effective methods to manipulate these inherently compliant and complex systems, addressing challenges posed by their nonlinear dynamics and high dimensionality. Current research emphasizes model-based approaches, such as reduced-order models (e.g., rod models) and model predictive control, alongside data-driven techniques including recurrent neural networks and reinforcement learning, often incorporating bio-inspired designs like central pattern generators. These advancements are crucial for realizing the full potential of soft robots in diverse applications, ranging from minimally invasive surgery and adaptable manipulation to locomotion in unstructured environments.