论文标题
局部各向同性机器人运动的时间序列逆动力学模型的混合学习
Hybrid Learning of Time-Series Inverse Dynamics Models for Locally Isotropic Robot Motion
论文作者
论文摘要
力控制和运动计划的应用通常依赖于反向动力学模型来表示运动过程中机器人的高维动态行为。低速，小规模，局部各向同性运动（Limo）的广泛发生通常会使适当模型的识别复杂化，这是由于动态效应的夸大和感官扰动而引起的，这是由于滞后的复杂摩擦和现象引起的，例如，与关节弹性有关。我们提出了一个混合模型学习基础体系结构，结合了一个基于多层pecceptron，LSTM和变压器拓扑的参数回归和时间序列神经网络体系结构标识的刚体动态模型。此外，我们介绍了新颖的旋转旋转历史记录编码，从而加强了时间信息，以有效地模拟动态滞后。在算法生成的局部各向同性运动期间，在KUKA IIWA 14上评估了模型。与旋转编码一起，所提出的体系结构的表现优于最先进的基线，其大小为10 $^3 $，得出的RMSE为0.14 nm。利用混合结构和编码能力的时间序列，我们的方法可以进行准确的扭矩估算，表明在运动序列中，其适用性超过了超过常规逆动力学模型能力的运动序列，同时由于面对稀缺数据的能力，并且由于使用了稀缺性数据，并且由于使用了所采用的物理学模型。
Applications of force control and motion planning often rely on an inverse dynamics model to represent the high-dimensional dynamic behavior of robots during motion. The widespread occurrence of low-velocity, small-scale, locally isotropic motion (LIMO) typically complicates the identification of appropriate models due to the exaggeration of dynamic effects and sensory perturbation caused by complex friction and phenomena of hysteresis, e.g., pertaining to joint elasticity. We propose a hybrid model learning base architecture combining a rigid body dynamics model identified by parametric regression and time-series neural network architectures based on multilayer-perceptron, LSTM, and Transformer topologies. Further, we introduce novel joint-wise rotational history encoding, reinforcing temporal information to effectively model dynamic hysteresis. The models are evaluated on a KUKA iiwa 14 during algorithmically generated locally isotropic movements. Together with the rotational encoding, the proposed architectures outperform state-of-the-art baselines by a magnitude of 10$^3$ yielding an RMSE of 0.14 Nm. Leveraging the hybrid structure and time-series encoding capabilities, our approach allows for accurate torque estimation, indicating its applicability in critically force-sensitive applications during motion sequences exceeding the capacity of conventional inverse dynamics models while retaining trainability in face of scarce data and explainability due to the employed physics model prior.
