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.
翻译:应用武力控制和运动规划往往依赖反向动态模型,以代表运动期间机器人的高维动态行为。广泛出现的低速、小规模、局部异向运动(LIMO)通常使确定适当模型的工作复杂化,因为复杂的摩擦和歇斯底里现象(例如,与联合弹性有关)造成的动态效应和感官扰动现象夸大了,例如,与联合弹性有关的复合摩擦和歇斯底里现象造成的动态效应和感官扰动。我们提议了一个混合示范学习基础架构,结合一个由参数回归和时序神经网络结构所查明的僵硬身体动态模型,该模型以多层-视线、LSTM和变异式结构为基础的硬体动态模型。此外,我们引入了新的联合-明智的轮换历史编码,强化了时间信息,以有效模拟常规的动态歇斯底线(LIMO II 14) 。模型在本地自算运动运动运动运动运动运动运动运动运动运动运动期间,与轮换编码一起,拟议结构比目前最先进的保留状态基线高出10美元,在10-3美元的水平上产生一个IMENE的可追溯性、精确的可变的可变式机能模型,同时在前可变动性序列中显示其精确的可变式数据结构,在精确的可变式序列中,在精确性变式序列中显示其精确性结构结构上显示其精确性能应用能力,在精确性结构结构上显示其精确性能在精确性结构上显示其精确性结构的可变能力。