Deep learning has been widely used within learning algorithms for robotics. One disadvantage of deep networks is that these networks are black-box representations. Therefore, the learned approximations ignore the existing knowledge of physics or robotics. Especially for learning dynamics models, these black-box models are not desirable as the underlying principles are well understood and the standard deep networks can learn dynamics that violate these principles. To learn dynamics models with deep networks that guarantee physically plausible dynamics, we introduce physics-inspired deep networks that combine first principles from physics with deep learning. We incorporate Lagrangian mechanics within the model learning such that all approximated models adhere to the laws of physics and conserve energy. Deep Lagrangian Networks (DeLaN) parametrize the system energy using two networks. The parameters are obtained by minimizing the squared residual of the Euler-Lagrange differential equation. Therefore, the resulting model does not require specific knowledge of the individual system, is interpretable, and can be used as a forward, inverse, and energy model. Previously these properties were only obtained when using system identification techniques that require knowledge of the kinematic structure. We apply DeLaN to learning dynamics models and apply these models to control simulated and physical rigid body systems. The results show that the proposed approach obtains dynamics models that can be applied to physical systems for real-time control. Compared to standard deep networks, the physics-inspired models learn better models and capture the underlying structure of the dynamics.
翻译:在机器人的学习算法中广泛使用深层次学习方法。深层次网络的一个缺点是这些网络是黑盒式的演示。因此,所学的近似法忽略了物理或机器人的现有知识。特别是对于学习动态模型来说,这些黑盒模型是不可取的,因为这些基本原则得到了很好的理解,标准的深层次网络可以学习违反这些原则的动态。为了学习具有深层次网络的动态模型,以保证物理上看似合理的动态,我们引入了物理学启发的深层次网络,这些网络将物理与深层次学习的最初原则结合起来。我们将这些深层的机械学纳入模型学习中,这样所有的近似模型都遵守物理和节能法则。深层拉格兰格网络(Degrangian Nets)利用两个网络将系统能源配对齐。这些参数是通过最大限度地减少Euler-Lagrange差异方程式的正方形残余而获得的。因此,为了学习单个系统的具体知识,这些模型是可以解释的,并且可以用作前向、反向和能源模型。这些特性只有在使用系统识别技术时才能获得,这些系统需要了解对运动结构结构结构结构进行更深层次结构的模型。我们应用了僵化的物理模型来学习模型,然后才能在学习物理模型和比较模型中学习物理模型上取得这些模型。 将物理模型的物理模型和物理控制结果显示。