Robots must make and break contact to interact with the world and perform useful tasks. However, planning and control through contact remains a formidable challenge. In this work, we achieve real-time contact-implicit model predictive control with a surprisingly simple method: inverse dynamics trajectory optimization. While trajectory optimization with inverse dynamics is not new, we introduce a series of incremental innovations that collectively enable fast model predictive control on a variety of challenging manipulation and locomotion tasks. We implement these innovations in an open-source solver, and present a variety of simulation examples to support the effectiveness of the proposed approach. Additionally, we demonstrate contact-implicit model predictive control on hardware at over 100 Hz for a 20 degree-of-freedom bi-manual manipulation task.

翻译：机器人必须通过建立和断开接触来与环境交互并执行有用任务。然而，通过接触进行规划和控制仍然是一个艰巨的挑战。在这项工作中，我们通过一种出奇简单的方法——逆动力学轨迹优化——实现了实时接触隐式模型预测控制。尽管使用逆动力学的轨迹优化并非新概念，但我们引入了一系列渐进式创新，这些创新共同使快速模型预测控制得以在多种具有挑战性的操作和 locomotion 任务上实现。我们将这些创新集成在一个开源求解器中，并呈现多种仿真示例以支持所提方法的有效性。此外，我们在硬件上演示了接触隐式模型预测控制，在超过 100 Hz 的频率下完成了一个 20 自由度双臂操作任务。