A significant barrier preventing model-based methods from matching the high performance of reinforcement learning in dexterous manipulation is the inherent complexity of multi-contact dynamics. Traditionally formulated using complementarity models, multi-contact dynamics introduces combinatorial complexity and non-smoothness, complicating contact-rich planning and control. In this paper, we circumvent these challenges by introducing a novel, simplified 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 explicit time stepping, differentiability, automatic satisfaction of 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 with diverse objects. Our method consistently achieves state-of-the-art results: (I) a 96.5% average success rate across tasks, (II) high manipulation accuracy with an average reorientation error of 11{\deg} and position error of 7.8 mm, and (III) model predictive control running at 50-100 Hz for all tested dexterous manipulation tasks. These results are achieved with minimal hyperparameter tuning.

翻译：阻碍基于模型的方法在灵巧操作领域达到与强化学习同等高性能水平的一个主要障碍是多接触动力学的固有复杂性。传统上采用互补性模型表述的多接触动力学引入了组合复杂性和非光滑性，使得接触丰富的规划与控制问题变得复杂。本文通过引入一种新颖的简化多接触模型来规避这些挑战。我们提出的新模型源自基于优化的接触模型的对偶性，完全摒弃了互补性结构，从而提供了显著的计算优势，包括显式时间步进、可微性、自动满足库仑摩擦定律以及极少的超参数调整需求。我们在一系列具有挑战性的灵巧操作任务中验证了该模型在规划与控制方面的有效性和高效性，包括指尖三维空中操作、TriFinger手内操作以及Allegro手掌重定向操作，所有任务均涉及多种不同物体。我们的方法始终取得最先进的结果：(I) 跨任务平均成功率高达96.5%，(II) 以平均11°重定向误差和7.8毫米位置误差实现高精度操作，(III) 在所有测试的灵巧操作任务中模型预测控制能以50-100 Hz频率运行。这些成果是在仅需极少超参数调整的情况下实现的。