论文标题
实际到SIM：使用基于学习的无知Kalman滤波器来预测具有稀疏数据的机器人系统的残差错误
Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter
论文作者
论文摘要
实现接近真实机器人的高度精确的动态或模拟器模型可以促进基于模型的控制（例如，模型预测性控制或线性 - Quadradic调节器），基于模型的轨迹计划（例如，轨迹优化），并减少强化学习方法所需的学习时间。因此，这项工作的目的是学习动态和/或模拟器模型与真实机器人之间的残差错误。这是使用神经网络实现的，在该神经网络中，神经网络的参数通过无味的卡尔曼过滤器（UKF）公式进行更新。使用此方法，我们仅使用少量数据对这些残差错误进行建模 - 这是我们通过直接从现实世界操作中学习改善模拟器/动态模型的必要性。我们演示了我们关于机器人硬件的方法（例如，操纵器臂和车轮机器人），并表明，通过学习的残差错误，我们可以进一步缩小动态模型，仿真和实际硬件之间的现实差距。
Achieving highly accurate dynamic or simulator models that are close to the real robot can facilitate model-based controls (e.g., model predictive control or linear-quadradic regulators), model-based trajectory planning (e.g., trajectory optimization), and decrease the amount of learning time necessary for reinforcement learning methods. Thus, the objective of this work is to learn the residual errors between a dynamic and/or simulator model and the real robot. This is achieved using a neural network, where the parameters of a neural network are updated through an Unscented Kalman Filter (UKF) formulation. Using this method, we model these residual errors with only small amounts of data -- a necessity as we improve the simulator/dynamic model by learning directly from real-world operation. We demonstrate our method on robotic hardware (e.g., manipulator arm, and a wheeled robot), and show that with the learned residual errors, we can further close the reality gap between dynamic models, simulations, and actual hardware.
