CAREER: Machine Learning Control of Underactuated Mechanical Systems

非线性欠驱动系统代表了机器人技术中一个重要而普遍的问题，这些问题在分析和数控设计范式中被证明是最棘手的。欠驱动控制的机器学习方法采用近似方法使数值最优控制技术易于处理，将广泛应用于从步行机器人到飞行器和流体系统的控制。这里，我们对应用于线性时不变（LTI）系统的算法进行了仔细的分析。这将有助于研究不同学习算法的收敛速度，以及设计最大化收敛速度的机器人机构和输入输出“特征”。理论结果与一个强非线性控制问题的实验相结合-一个两连杆双足机器人在崎岖地形上行走。为这个机器人获得一个接近最优的反馈策略将产生令人惊讶和引人注目的结果（因为一个简单的机器人将穿越比任何人形机器人所展示的更复杂的地形），但也清楚和揭示（因为机器人的简单暴露了行走的基本问题，而不是更多）。理论和实验都允许我们在机器学习控制和现代控制中更成熟的控制方法之间进行仔细的比较。

The nonlinear underactuated systems represent an important and generalclass of problems in robotics which have proven mostly intractable foranalytical and numerical control design paradigms. Machine learningapproaches to underactuated control, which employ approximations tomake numerical optimal control techniques tractable, will have broadapplications from walking robots to the control of aerial vehicles andfluid systems. Here we pursue a careful analysis of the algorithmsapplied to linear time-invariant (LTI) systems. This will contributefundamental results on the convergence rate of different learningalgorithms, and the design of robot mechanisms and input-output"features" which maximize the rate of convergence. Theoreticalresults are coupled with experiments on a strongly nonlinear controlproblem - a two-link bipedal robot walking over rough terrain.Acquiring a near optimal feedback policy for this robot would producea result that is surprising and compelling (because a simple robotwill be traversing more complicated terrain than has been demonstratedby any humanoid), but also clear and revealing (because the simplicityof the robot exposes the fundamental problems in walking and nothingmore). Both theory and experiment allow us to perform a carefulcomparison between machine learning control and more mature controlapproaches from modern control.