We propose a control framework which can utilize tactile information by exploiting the complementarity structure of contact dynamics. Since many robotic tasks, like manipulation and locomotion, are fundamentally based in making and breaking contact with the environment, state-of-the-art control policies struggle to deal with the hybrid nature of multi-contact motion. Such controllers often rely heavily upon heuristics or, due to the combinatorial structure in the dynamics, are unsuitable for real-time control. Principled deployment of tactile sensors offers a promising mechanism for stable and robust control, but modern approaches often use this data in an ad hoc manner, for instance to guide guarded moves. This framework can close the loop on tactile sensors and it is non-combinatorial, enabling optimization algorithms to automatically synthesize provably stable control policies. We demonstrate this approach on multiple numerical examples, including quasi-static friction problems and a high dimensional problem with ten contacts. We also validate our results on an experimental setup and show the effectiveness of the proposed method on an underactuated multi-contact system.
翻译:由于许多机器人任务,如操纵和移动等,都从根本上以产生和破坏与环境的接触为基础,因此,最先进的控制政策努力处理多接触运动的混合性质。这些控制器往往严重依赖杂交性,或由于动态中的组合结构,不适于实时控制。有原则地部署触控传感器为稳定和稳健的控制提供了一种有希望的机制,但现代方法经常以临时方式使用这些数据,例如,指导有防护的动作。这个框架可以关闭触控传感器的环路,它不具有节制性,使优化算法能够自动合成稳健的稳定控制政策。我们用多个数字例子,包括准静电摩擦问题和十次接触的高维问题来证明这一方法。我们还验证了我们实验性设置的结果,并展示了低触控多接触系统的拟议方法的有效性。