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 in less than five minutes of online learning.
翻译:在接触繁忙的任务中,比如多式操纵,制作和打破接触的混合性质给模型的表达和控制带来了挑战。例如,选择和排序手边操作的接触地点(那里有数千种潜在的混合模式)一般是无法操作的。在本文件中,我们受到以下观察的启发,即实际完成许多任务所需的模式要少得多。以我们先前的工作学习混合模式为基础,以线性互补系统为代表,我们发现一个简化的混合模式,只需要数量有限的任务相关模式。这种简化的表达方式与模型预测控制相结合,使得实时控制能够达到高性能。我们首先在合成混合系统中展示了拟议的方法,将模式的计算数量减少多个数量,同时实现任务绩效损失不到5%。我们还将拟议方法应用于一个三指机械手操纵一个先前未知的天体。我们没有事先了解,在不到5分钟的网上学习中实现了最先进的闭路功能。