Planning and execution of agile locomotion maneuvers have been a longstanding challenge in legged robotics. It requires to derive motion plans and local feedback policies in real-time to handle the nonholonomy of the kinetic momenta. To achieve so, we propose a hybrid predictive controller that considers the robot's actuation limits and full-body dynamics. It combines the feedback policies with tactile information to locally predict future actions. It converges within a few milliseconds thanks to a feasibility-driven approach. Our predictive controller enables ANYmal robots to generate agile maneuvers in realistic scenarios. A crucial element is to track the local feedback policies as, in contrast to whole-body control, they achieve the desired angular momentum. To the best of our knowledge, our predictive controller is the first to handle actuation limits, generate agile locomotion maneuvers, and execute optimal feedback policies for low level torque control without the use of a separate whole-body controller.

翻译：机动机动机动操作的规划和执行一直是脚步机器人长期面临的一项挑战。 它需要实时生成运动计划和地方反馈政策, 才能处理运动瞬间不协调的情况。 为了实现这一点, 我们提议了一个混合预测控制器, 考虑机器人的动作限制和全体动态。 它将反馈政策与触动信息结合起来, 以便本地预测未来的行动。 由于采用可行性驱动的方法, 它在几毫秒内会聚集在一起。 我们的预测控制器使安马利机器人能够在现实情况下生成敏捷的动作。 一个关键要素是跟踪本地反馈政策, 因为与整个机体控制相比, 它们能够实现理想的角动力。 根据我们的知识, 我们的预测控制器是第一个处理动作限制, 产生敏捷的移动动作动作操作, 并为低级别控制执行最佳反馈政策, 而不使用单独的整体控制器 。