In contact-rich tasks, like dexterous manipulation, the hybrid nature of making and breaking contact creates challenges for model representation and control. For example, choosing and sequencing contact locations for in-hand manipulation, where there are thousands of potential hybrid modes, is not generally tractable. In this article, we are inspired by the observation that far fewer modes are actually necessary to accomplish many tasks. Building on our prior work learning hybrid models, represented as linear complementarity systems, we find a reduced-order hybrid model requiring only a limited number of task-relevant modes. This simplified representation, in combination with model predictive control, enables real-time control yet is sufficient for achieving high performance. We demonstrate the proposed method first on synthetic hybrid systems, reducing the mode count by multiple orders of magnitude while achieving task performance loss of less than 5%. We also apply the proposed method to a three-fingered robotic hand manipulating a previously unknown object. With no prior knowledge, we achieve state-of-the-art closed-loop performance within a few minutes of online learning, by collecting only a few thousand environment samples.

中文翻译：

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用于灵巧操作的任务驱动混合模型简化

在接触丰富的任务中，例如灵巧的操作，建立和断开接触的混合性质给模型表示和控制带来了挑战。例如，选择和排序用于手动操作的接触位置通常不容易处理，其中存在数千种潜在的混合模式。在本文中，我们受到以下观察的启发：完成许多任务实际上需要更少的模式。基于我们之前的学习混合模型（表示为线性互补系统），我们发现了一种降阶混合模型，仅需要有限数量的任务相关模式。这种简化的表示与模型预测控制相结合，可以实现实时控制，但足以实现高性能。我们首先在合成混合系统上演示了所提出的方法，将模式数量减少了多个数量级，同时实现了小于 5% 的任务性能损失。我们还将所提出的方法应用于操纵先前未知物体的三指机器人手。在没有先验知识的情况下，我们仅通过收集几千个环境样本，就可以在几分钟的在线学习内实现最先进的闭环性能。