Precise identification of dynamic models in robotics is essential to support control design, friction compensation, output torque estimation, etc. A longstanding challenge remains in the identification of friction models for robotic joints, given the numerous physical phenomena affecting the underlying friction dynamics which result into nonlinear characteristics and hysteresis behaviour in particular. These phenomena proof difficult to be modelled and captured accurately using physical analogies alone. This has motivated researchers to shift from physics-based to data-driven models. Currently, these methods are still limited in their ability to generalize effectively to typical industrial robot deployement, characterized by high- and low-velocity operations and frequent direction reversals. Empirical observations motivate the use of dynamic friction models but these remain particulary challenging to establish. To address the current limitations, we propose to account for unidentified dynamics in the robot joints using latent dynamic states. The friction model may then utilize both the dynamic robot state and additional information encoded in the latent state to evaluate the friction torque. We cast this stochastic and partially unsupervised identification problem as a standard probabilistic representation learning problem. In this work both the friction model and latent state dynamics are parametrized as neural networks and integrated in the conventional lumped parameter dynamic robot model. The complete dynamics model is directly learned from the noisy encoder measurements in the robot joints. We use the Expectation-Maximisation (EM) algorithm to find a Maximum Likelihood Estimate (MLE) of the model parameters. The effectiveness of the proposed method is validated in terms of open-loop prediction accuracy in comparison with baseline methods, using the Kuka KR6 R700 as a test platform.

翻译：机器人学中动态模型的精确辨识对于支持控制设计、摩擦补偿、输出扭矩估计等至关重要。鉴于影响底层摩擦动力学的众多物理现象（这些现象尤其会导致非线性特性和迟滞行为），机器人关节摩擦模型的辨识长期以来一直是一个挑战。仅通过物理类比难以精确建模和捕捉这些现象。这促使研究人员从基于物理的模型转向数据驱动模型。目前，这些方法在有效泛化到典型工业机器人部署场景（其特点是包含高速与低速运行以及频繁的方向反转）方面仍存在局限。经验观察支持使用动态摩擦模型，但这些模型的建立尤其具有挑战性。为应对当前局限，我们提出利用隐动态状态来考虑机器人关节中未辨识的动力学。摩擦模型随后可同时利用动态机器人状态和隐状态中编码的附加信息来评估摩擦扭矩。我们将这一随机且部分无监督的辨识问题表述为标准概率表示学习问题。在本工作中，摩擦模型和隐状态动力学均参数化为神经网络，并集成到传统的集总参数动态机器人模型中。完整的动力学模型直接从机器人关节的噪声编码器测量数据中学习。我们使用期望最大化（EM）算法寻找模型参数的最大似然估计（MLE）。以Kuka KR6 R700为测试平台，通过与基线方法的开环预测精度比较，验证了所提方法的有效性。