论文标题
ValuenetQP：学习的一步最佳控制
ValueNetQP: Learned one-step optimal control for legged locomotion
论文作者
论文摘要
最佳控制是一种成功为复杂机器人生成动作的方法，尤其是用于腿部运动的方法。但是，这些技术通常太慢，无法实时运行，以进行模型预测性控制，或者需要大幅简化动力学模型。在这项工作中，我们提出了一种学会预测问题价值功能的梯度和黑森的方法，从而可以通过一个步骤二次程序快速解决预测控制问题。此外，我们的方法能够满足诸如摩擦锥和单侧约束之类的约束，这对于高动态运动任务很重要。我们演示了我们方法在模拟中的能力以及执行小跑和边界运动的真实四倍的机器人。
Optimal control is a successful approach to generate motions for complex robots, in particular for legged locomotion. However, these techniques are often too slow to run in real time for model predictive control or one needs to drastically simplify the dynamics model. In this work, we present a method to learn to predict the gradient and hessian of the problem value function, enabling fast resolution of the predictive control problem with a one-step quadratic program. In addition, our method is able to satisfy constraints like friction cones and unilateral constraints, which are important for high dynamics locomotion tasks. We demonstrate the capability of our method in simulation and on a real quadruped robot performing trotting and bounding motions.
