Robots must make and break contact with the environment to perform useful tasks, but 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 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. Video and code are available at https://idto.github.io.

翻译：机器人必须通过与环境接触和断开接触来完成有用任务，但通过接触进行规划与控制仍是一个艰巨挑战。本研究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。尽管逆动力学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与运动任务中生效。我们将这些创新实现于一个开源求解器中，并展示了仿真示例以佐证所提出方法的有效性。此外，我们还在硬件上以超过100Hz的频率实现了20自由度双臂操作任务的接触隐式模型预测控制。视频与代码可从https://idto.github.io获取。