Online Gaussian processes (GPs), typically used for learning models from time-series data, are more flexible and robust than offline GPs. Both local and sparse approximations of GPs can efficiently learn complex models online. Yet, these approaches assume that all signals are relatively accurate and that all data are available for learning without misleading data. Besides, the online learning capacity of GPs is limited for high-dimension problems and long-term tasks in practice. This paper proposes a sparse online GP (SOGP) with a forgetting mechanism to forget distant model information at a specific rate. The proposed approach combines two general data deletion schemes for the basis vector set of SOGP: The position information-based scheme and the oldest points-based scheme. We apply our approach to learn the inverse dynamics of a collaborative robot with 7 degrees of freedom under a two-segment trajectory tracking problem with task switching. Both simulations and experiments have shown that the proposed approach achieves better tracking accuracy and predictive smoothness compared with the two general data deletion schemes.
翻译:通常用于从时间序列数据中学习模型的在线高斯进程(GPs)比离线GPs更为灵活和有力。当地和稀少的GPs近似点可以有效地在线学习复杂的模型。然而,这些方法假定所有信号相对准确,所有数据都可用于学习,而没有误导数据。此外,GPs在线学习能力因高差异问题和实践中的长期任务而受到限制。本文件建议采用稀少的在线GP(SOGP),并采用一种忘记机制,以特定速度忘记遥远的模型信息。拟议方法将SOGP的基矢量数据集的两个一般性数据删除计划:基于位置的信息计划和基于点的最古老的计划。我们采用我们的方法,在两组化轨迹追踪问题和任务转换过程中学习7度自由的协作机器人的反动态。两个模拟和实验都表明,拟议的方法与两个一般数据删除计划相比,可以更好地跟踪准确性和预测性顺畅。