接触隐式模型预测控制用于灵巧手内操作：一种长时域且鲁棒的方法
Contact-Implicit Model Predictive Control for Dexterous In-hand Manipulation: A Long-Horizon and Robust Approach
AI总结：
本文提出一种接触隐式模型预测控制方法，实现无需预定义接触序列的鲁棒长时域灵巧手内操作，在23自由度旋转任务上以20Hz运行，并泛化至不同物体。
摘要：
灵巧手内操作是生产和生活中的一项基本技能。然而，接触的高度刚性和可变性限制了实时接触检测与推断，从而降低了基于模型方法的性能。受近期接触丰富型运动与操作领域进展的启发，本文提出了一种新颖的基于模型的方法来控制灵巧手内操作并克服当前局限。所提方法具有一个吸引人的特性，即允许机器人无需预定义接触序列或单独规划程序即可鲁棒地执行长时域手内操作。具体而言，我们设计了一个高层接触隐式模型预测控制器，以生成由低层跟踪控制器执行的实时接触计划。与其他基于模型的方法相比，这种长时域特性使得能够重新规划并鲁棒地执行接触丰富的运动，从而更高效地实现大位移手内操作；与现有基于学习的方法相比，所提方法实现了灵巧性，并且无需任何预训练即可泛化到不同物体。详细的仿真和消融研究证明了我们方法的效率和有效性。该方法在23自由度、长时域、手内物体旋转任务上以20Hz运行。
英文摘要：
Dexterous in-hand manipulation is an essential skill of production and life. However, the highly stiff and mutable nature of contacts limits real-time contact detection and inference, degrading the performance of model-based methods. Inspired by recent advances in contact-rich locomotion and manipulation, this paper proposes a novel model-based approach to control dexterous in-hand manipulation and overcome the current limitations. The proposed approach has an attractive feature, which allows the robot to robustly perform long-horizon in-hand manipulation without predefined contact sequences or separate planning procedures. Specifically, we design a high-level contact-implicit model predictive controller to generate real-time contact plans executed by the low-level tracking controller. Compared to other model-based methods, such a long-horizon feature enables replanning and robust execution of contact-rich motions to achieve large displacements in-hand manipulation more efficiently; Compared to existing learning-based methods, the proposed approach achieves dexterity and also generalizes to different objects without any pre-training. Detailed simulations and ablation studies demonstrate the efficiency and effectiveness of our method. It runs at 20Hz on the 23-degree-of-freedom, long-horizon, in-hand object rotation task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
