结构机械系统学习的通用框架
A General Framework for Structured Learning of Mechanical Systems
AI总结：
本文提出了一种通用框架，用于结构化学习机械系统，通过结合先验知识和训练表达式近似器来提高模型的准确性和效率。
摘要：
学习准确的动力学模型对于优化和顺应性控制机器人系统至关重要。当前使用解析参数化进行白盒建模或使用神经网络进行黑盒建模的方法可能会产生高偏差或高方差。我们提出了一个灵活的灰盒模型，可以无缝地结合可用的先验知识，并在没有时训练具有表达能力的函数近似器。我们提出使用神经网络参数化机械系统，以建模其拉格朗日量和作用在其上的广义力。我们在模拟的驱动双摆上测试了我们的方法。我们展示了我们的方法在数据效率以及基于模型的强化学习中的性能优于朴素的黑盒模型。我们还系统地研究了我们的方法在结合可用的系统先验知识以提高数据效率方面的能力。
英文摘要：
Learning accurate dynamics models is necessary for optimal, compliant control of robotic systems. Current approaches to white-box modeling using analytic parameterizations, or black-box modeling using neural networks, can suffer from high bias or high variance. We address the need for a flexible, gray-box model of mechanical systems that can seamlessly incorporate prior knowledge where it is available, and train expressive function approximators where it is not. We propose to parameterize a mechanical system using neural networks to model its Lagrangian and the generalized forces that act on it. We test our method on a simulated, actuated double pendulum. We show that our method outperforms a naive, black-box model in terms of data-efficiency, as well as performance in model-based reinforcement learning. We also conduct a systematic study of our method's ability to incorporate available prior knowledge about the system to improve data efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
