# Learning Neural Force Manifolds for Sim2Real Robotic Symmetrical Paper Folding

arXiv: https://arxiv.org/abs/2301.01968 · HTML: https://mlsanity.com/paper/2301.01968/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2023.

## Built on by (most cited first, 2 of 2)

- [Robotic Paper Wrapping by Learning Force Control](https://mlsanity.com/paper/2503.15685/) (2025)
- [Learning-based Cooperative Robotic Paper Wrapping: A Unified Control Policy with Residual Force Control](https://mlsanity.com/paper/2511.03181/) (2025)

## Abstract (arXiv)

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× 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.