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 data-driven 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 sensorimotor 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.

翻译：细长物体的机器人操作具有挑战性，尤其当诱导形变具有大且非线性的特征时。传统上，基于学习的控制方法（如模仿学习）已被用于解决可变形材料操作问题。但这些方法缺乏通用性，且经常因材料、几何和/或环境（如摩擦）属性的简单变化而出现关键性失效。本文针对一项基础但困难的可变形操作任务：仅使用单台操作器在纸上形成预定折痕。通过结合物理精确模拟与机器学习的数驱动框架，我们训练了一个深度神经网络，能够基于抓取位置预测施加于所操作纸张上的外力。我们利用尺度分析构建问题框架，从而获得对材料与几何变化具有鲁棒性的控制框架。随后在生成的"神经力流形"上进行路径规划，生成经优化以防止滑移的机器人操作轨迹；离线轨迹生成速度较以往基于物理的折纸方法提升15倍。训练模型具备的推理速度可整合实时视觉反馈，实现闭环感知运动控制。真实世界实验表明，即使操作不同材料与形状的纸质物体，我们的框架相较于最先进的折纸策略也能显著提升机器人操作性能。