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:深度学习在机器人学习算法中得到了广泛应用。深度网络的一个缺点是它们是黑盒表示。因此,学习到的近似忽略了物理或机器人学的现有知识。特别是对于学习动力学模型,这些黑盒模型并不理想,因为已经了解了潜在的基本原理,并且标准的深层网络可以学习到违反这些原理的动态。为了使用深层网络学习保证物理上合理的动态模型,我们介绍了物理启发式深层网络,它结合了物理学和深度学习的基本原理。我们将Lagrangian力学纳入到模型的学习过程中,以便所有近似的模型均遵守物理定律并遵守能量守恒原理。深度Lagrangian网络(DeLaN)使用两个网络对系统能量进行参数化。通过最小化欧拉-拉格朗日微分方程的平方残差来获取参数。因此,结果模型不需要特定知识就可以应用于前向、反向和能量模型,且具有可解释性。以前,只有在使用需要关于运动结构的知识的系统识别技术时,才能获得这些性质。我们将DeLaN应用于学习动力学模型,并将这些模型应用于控制模拟和物理刚体系统。结果表明,所提出的方法获得了可以应用于实时控制物理系统的动力学模型。与标准的深层网络相比,物理启发式模型学习得到更好的模型并捕获动态的潜在结构。