无互补性多接触建模与优化用于灵巧操作
Complementarity-Free Multi-Contact Modeling and Optimization for Dexterous Manipulation
AI总结：
本文提出一种无互补性的轻量级多接触模型，用于灵巧操作，通过闭式时间步进和可微性实现高效规划与控制，在多种任务中达到96.5%成功率和高精度。
摘要：
阻碍基于模型的方法实现实时且多功能的灵巧机器人操作的一个重大障碍是多接触动力学的固有复杂性。传统上，多接触动力学被表述为互补性模型，引入了非光滑性和组合复杂性，使接触丰富的规划和优化变得复杂。在本文中，我们通过引入一个轻量级但功能强大的多接触模型来规避这些挑战。我们的新模型源自基于优化的接触模型的对偶性，完全摒弃了互补性构造，提供了计算优势，如闭式时间步进、可微性、自动满足库仑摩擦定律以及最少的超参数调整。我们在一系列具有挑战性的灵巧操作任务中展示了该模型在规划和控制方面的有效性和效率，包括指尖三维空中操作、TriFinger手中操作以及Allegro手在掌上重新定向，所有任务均使用不同物体完成。我们的方法始终取得最先进的结果：（I）在所有物体和任务中平均成功率达到96.5%，（II）高操作精度，平均重新定向误差为11°，位置误差为7.8毫米，（III）接触隐式模型预测控制在所有物体和任务中以50-100赫兹运行。这些结果是在最少的超参数调整下实现的。
英文摘要：
A significant barrier preventing model-based methods from achieving real-time and versatile dexterous robotic manipulation is the inherent complexity of multi-contact dynamics. Traditionally formulated as complementarity models, multi-contact dynamics introduces non-smoothness and combinatorial complexity, complicating contact-rich planning and optimization. In this paper, we circumvent these challenges by introducing a lightweight yet capable multi-contact model. Our new model, derived from the duality of optimization-based contact models, dispenses with the complementarity constructs entirely, providing computational advantages such as closed-form time stepping, differentiability, automatic satisfaction with Coulomb friction law, and minimal hyperparameter tuning. We demonstrate the effectiveness and efficiency of the model for planning and control in a range of challenging dexterous manipulation tasks, including fingertip 3D in-air manipulation, TriFinger in-hand manipulation, and Allegro hand on-palm reorientation, all performed with diverse objects. Our method consistently achieves state-of-the-art results: (I) a 96.5% average success rate across all objects and tasks, (II) high manipulation accuracy with an average reorientation error of 11° and position error of 7.8mm, and (III) contact-implicit model predictive control running at 50-100 Hz for all objects and tasks. These results are achieved with minimal hyperparameter tuning.
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。
