多体模型学习的可微牛顿-欧拉算法
A Differentiable Newton Euler Algorithm for Multi-body Model Learning
摘要：
在这项工作中，我们研究了一个频谱的混合模型域的多体机器人动力学。我们激发了一个计算图形架构，体现了牛顿-欧拉方程，强调李代数形式的效用，将动态几何转化为一个有效的计算结构，用于学习。我们描述了所使用的虚拟参数，使不受约束的物理似然动力学和所使用的致动器模型。在实验中，我们定义了一个家庭的26个灰箱模型和评估他们的系统辨识的模拟和物理古田摆和Cartpole。比较表明，以前的白盒系统辨识方法所需的运动学参数，可以准确地推断出数据。此外，我们强调，模型与保证有界能量的不受控制的系统产生非发散的轨迹，而更一般的模型没有这样的保证，所以它们的性能强烈依赖于数据分布。因此，这项工作的主要贡献是引入了一个白盒模型，共同学习动力学和运动学参数，并可以与黑盒组件相结合。然后，我们对具有挑战性的系统和不同的数据集进行了广泛的实证评估，这些数据集阐明了我们的灰盒架构与可比较的白盒和黑盒模型的比较性能。
英文摘要：
In this work, we examine a spectrum of hybrid model for the domain of multi-body robot dynamics. We motivate a computation graph architecture that embodies the Newton Euler equations, emphasizing the utility of the Lie Algebra form in translating the dynamical geometry into an efficient computational structure for learning. We describe the used virtual parameters that enable unconstrained physical plausible dynamics and the used actuator models. In the experiments, we define a family of 26 grey-box models and evaluate them for system identification of the simulated and physical Furuta Pendulum and Cartpole. The comparison shows that the kinematic parameters, required by previous white-box system identification methods, can be accurately inferred from data. Furthermore, we highlight that models with guaranteed bounded energy of the uncontrolled system generate non-divergent trajectories, while more general models have no such guarantee, so their performance strongly depends on the data distribution. Therefore, the main contributions of this work is the introduction of a white-box model that jointly learns dynamic and kinematics parameters and can be combined with black-box components. We then provide extensive empirical evaluation on challenging systems and different datasets that elucidates the comparative performance of our grey-box architecture with comparable white- and black-box models.
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。
