Model-based controllers on real robots require accurate knowledge of the system dynamics to perform optimally. For complex dynamics, first-principles modeling is not sufficiently precise, and data-driven approaches can be leveraged to learn a statistical model from real experiments. However, the efficient and effective data collection for such a data-driven system on real robots is still an open challenge. This paper introduces an optimization problem formulation to find an informative trajectory that allows for efficient data collection and model learning. We present a sampling-based method that computes an approximation of the trajectory that minimizes the prediction uncertainty of the dynamics model. This trajectory is then executed, collecting the data to update the learned model. In experiments we demonstrate the capabilities of our proposed framework when applied to a complex omnidirectional flying vehicle with tiltable rotors. Using our informative trajectories results in models which outperform models obtained from non-informative trajectory by 13.3\% with the same amount of training data. Furthermore, we show that the model learned from informative trajectories generalizes better than the one learned from non-informative trajectories, achieving better tracking performance on different tasks.

中文翻译：

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使用信息轨迹进行主动模型学习，以改进实际机器人上的闭环控制

真实机器人上基于模型的控制器需要准确了解系统动力学，才能实现最佳性能。对于复杂的动力学，第一性原理建模不够精确，并且可以利用数据驱动的方法从实际实验中学习统计模型。然而，在真实的机器人上针对这种数据驱动系统的有效数据收集仍然是一个开放的挑战。本文介绍了一个优化问题的表述，以找到一条信息轨迹，从而可以进行有效的数据收集和模型学习。我们提出了一种基于采样的方法，该方法可以计算轨迹的近似值，从而使动力学模型的预测不确定性最小。然后执行该轨迹，收集数据以更新学习的模型。在实验中，我们证明了本文提出的框架应用于具有可倾斜旋翼的复杂全向飞行器时的功能。使用我们的信息性轨迹所生成的模型，在相同数量的训练数据下，其性能比从非信息性轨迹获得的模型要好13.3％。此外，我们表明，从信息轨迹学习的模型比从非信息轨迹学习的模型具有更好的概括性，在不同任务上实现了更好的跟踪性能。