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 paper, 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.

翻译：在接触密集的任务（如灵巧操作）中，接触的建立与断开形成的混合特性给模型表示与控制带来了挑战。例如，针对手内操作中接触位置的选择与排序，由于存在数千种潜在的混合模态，通常难以实现全局可解。本文受实际任务所需模态远少于全部可能模态的观察启发，基于我们先前学习线性互补系统表示的混合模型的工作，发现仅需有限数量的任务相关模态即可构建降阶混合模型。这种简化表示与模型预测控制相结合，既能实现实时控制，又足以达到高性能。我们首先在合成混合系统上验证所提方法，模态数量降低多个数量级的同时任务性能损失低于5%。此外，我们将该方法应用于操作未知物体的三指机械手。在无先验知识条件下，通过在线学习仅采集数千次环境样本，即可在数分钟内实现具有闭环控制性能的先进水平。