CAREER: Machine Learning Control of Underactuated Mechanical Systems

职业：欠驱动机械系统的机器学习控制

• 批准号: 0746194
• 负责人: Russell Tedrake
• 金额: $ 50万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-06-15 至 2014-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0746194&HistoricalAwards=false
• 关键词: CAREER; Machine; Learning; Control; Underactuated

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.

非线性欠驱动系统是机器人领域中一类重要而又普遍的问题，对于解析和数值控制设计范式来说，这类问题已被证明是非常棘手的. 机器学习方法欠驱动控制，采用近似使数字最优控制技术易于处理，将有广泛的应用，从步行机器人控制的飞行器和流体系统。在这里，我们追求一个仔细分析的算法适用于线性时不变（LTI）系统。 这将有助于不同learningalgorithms的收敛速度的基本结果，和机器人机制和输入输出的“功能”，最大限度地提高收敛速度的设计。 理论结果与实验结果相结合的一个强非线性控制问题-一个两连杆双足机器人行走在粗糙的地形。获得一个接近最优的反馈政策，这种机器人将产生一个结果，是令人惊讶和引人注目的（因为一个简单的机器人正在穿越比任何人形机器人都要复杂的地形），但也清晰和揭示（因为机器人的简单性暴露了行走的基本问题，仅此而已）。 理论和实验都使我们能够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。