Flexible Object

Research on flexible object manipulation focuses on enabling robots to dexterously handle deformable materials like fabrics and soft robotics components, a challenge due to their complex, nonlinear dynamics. Current efforts leverage deep learning models, often incorporating physics-based constraints or tactile sensing alongside vision, to improve prediction accuracy and control during manipulation tasks. These advancements are crucial for applications ranging from advanced manufacturing and planetary exploration to assistive robotics, where robust and efficient handling of flexible objects is essential. The integration of diverse sensing modalities and improved model architectures are key themes driving progress in this field.