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极具挑战性，尤其当诱导形变具有大范围非线性特征时。传统上，模仿学习等基于学习的控制方法被用于处理可变形材料操作，但这些方法缺乏泛化能力，且常因材料、几何和/或环境（如摩擦）属性的简单改变而遭遇关键性失效。本文针对一项基础但困难的柔性体操作任务——仅用单机械臂在纸张上形成预定折痕。通过结合物理精确仿真与机器学习的Sim2Real框架，训练出能根据抓取位置预测纸张所受外力的深度神经网络。我们采用缩放分析对问题进行建模，构建出对材料和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，生成可防止滑动的机器人操作轨迹，其离线轨迹生成速度较先前基于物理的折叠方法提升15倍。训练后的模型推理速度可实现实时视觉反馈，达成闭环模型预测控制。真实世界实验表明，即便处理不同材质与形状的纸张对象，我们的框架相比最先进的折叠策略能显著提升机器人操作性能。