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.

本文提出了一种混合预测控制器，使用触觉信息和全身动力学来局部预测机器人的未来动作，实现了机器人快速生成灵活的运动。通过跟踪反馈策略，该预测控制器能够在没有使用单独的全身控制器的情况下执行低级扭矩控制。

四足机器人的敏捷机动：一种预测控制方法

Agile Maneuvers in Legged Robots: a Predictive Control Approach

We propose a model-based approach to design feedback policies for dexterous
robotic manipulation. The manipulation problem is formulated as reaching the
target region from an initial state for some non-smooth nonlinear system.
First, we use trajectory optimization to find a feasible trajectory. Next, we
characterize the local multi-contact dynamics around the trajectory as a
piecewise affine system, and build a funnel around the linearization of the
nominal trajectory using polytopes. We prove that the feedback controller at
the vicinity of the linearization is guaranteed to drive the nonlinear system
to the target region. During online execution, we solve linear programs to
track the system trajectory. We validate the algorithm on hardware, showing
that even under large external disturbances, the controller is able to
accomplish the task.

我们提出了一个基于模型的方法来设计巧妙的机器人操作反馈策略，利用轨迹优化找到可行的轨迹，并使用多面体构建线性化轨迹的漏斗以及在线执行时解决线性问题以跟踪系统轨迹。