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.

翻译：细长物体的机器人操作具有挑战性，尤其是在诱导变形较大且非线性时。传统上，基于学习的控制方法（如模仿学习）已被用于处理可变形材料操作。这些方法缺乏通用性，且常因材料、几何和/或环境（例如摩擦）属性的简单变化而严重失效。本文解决了一项基础但困难的可变形操作任务：仅使用单机械臂在纸上形成预定义的折痕。我们采用结合物理精确仿真与机器学习的Sim2Real框架，训练深度神经网络，使其能根据抓取位置预测作用于被操作纸上的外力。通过尺度分析构建问题框架，得到对材料和几何变化鲁棒的控制框架。随后在生成的“神经力流形”上进行路径规划，生成优化以防止滑动的机器人操作轨迹，离线轨迹生成速度比以往基于物理的折叠方法快15倍。训练模型的推理速度使其能够整合实时视觉反馈，实现闭环模型预测控制。实际实验表明，与最先进的折叠策略相比，我们的框架能显著提升机器人操作性能，即使操作不同材料和形状的纸对象。