Learning Neural Force Manifolds for Sim2Real Robotic Symmetrical Paper Folding

Robotic manipulation of slender objects is challenging, especially when the induced deformations are large and nonlinear. Traditionally, learning-based control approaches, such as imitation learning, have been used to address deformable material manipulation. These approaches lack generality and often suffer critical failure from a simple switch of material, geometric, and/or environmental (e.g., friction) properties. This article tackles a fundamental but difficult deformable manipulation task: forming a predefined fold in paper with only a single manipulator. A sim2real framework combining physically-accurate simulation and machine learning is used to train a deep neural network capable of predicting the external forces induced on the manipulated paper given a grasp position. We frame the problem using scaling analysis, resulting in a control framework robust against material and geometric changes. Path planning is then carried out over the generated “neural force manifold” to produce robot manipulation trajectories optimized to prevent sliding, with offline trajectory generation finishing 15
$\times$
faster than previous physics-based folding methods. The inference speed of the trained model enables the incorporation of real-time visual feedback to achieve closed-loop model-predictive control. Real-world experiments demonstrate that our framework can greatly improve robotic manipulation performance compared to state-of-the-art folding strategies, even when manipulating paper objects of various materials and shapes.
Note to Practitioners
—This article is motivated by the need for efficient robotic folding strategies for stiff materials such as paper. Previous robot folding strategies have focused primarily on soft materials (e.g., cloth) possessing minimal bending resistance or relied on multiple complex manipulators and sensors, significantly increasing computational and monetary costs. In contrast, we formulate a robust, sim2real, physics-based method capable of folding papers of varying stiffness with a single manipulator. The proposed folding scheme is limited to papers of homogeneous material and folding along symmetric centerlines. Future work will involve formulating efficient methods for folding along arbitrary geometries and preexisting creases.