Towards robust online inverse dynamics learning
Towards robust online inverse dynamics learning
复制标题
迈向稳健的在线逆动态学习
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
S. Schaal
中科院分区：
文献类型：
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作者：
Franziska Meier;Daniel Kappler;Nathan D. Ratliff;S. Schaal
当分析模型只是粗略的近似时，逆动力学建模误差的学习是柔顺或力控制的关键。因此，设计具有真实的时间能力的函数逼近算法已经成为实现在线模型学习目标的必要焦点。然而，由于这些方法学习从实际状态和加速度到扭矩的映射，因此需要良好的跟踪来观察期望路径上的数据点。最近，已经显示了如何在简单的建模误差偏移项上在线梯度下降以最小化加速度水平的跟踪可以解决这个问题。然而，为了适应较大的错误，需要在线学习器的高学习率，从而降低了顺应性。因此，在这里，我们建议联合收割机两种方法：在线自适应偏移项确保良好的跟踪，使得非线性函数逼近器能够学习所需轨迹上的误差模型。这反过来又减少了自适应反馈的负载，使其能够使用较低的学习速率。结合这一点，创建一个控制器与可变反馈和低增益，以及前馈模型，可以考虑更大的建模误差。我们证明了这个框架的有效性，在模拟和一个真实的系统。
Learning of inverse dynamics modeling errors is key for compliant or force control when analytical models are only rough approximations. Thus, designing real time capable function approximation algorithms has been a necessary focus towards the goal of online model learning. However, because these approaches learn a mapping from actual state and acceleration to torque, good tracking is required to observe data points on the desired path. Recently it has been shown how online gradient descent on a simple modeling error offset term to minimize tracking at acceleration level can address this issue. However, to adapt to larger errors a high learning rate of the online learner is required, resulting in reduced compliancy. Thus, here we propose to combine both approaches: The online adapted offset term ensures good tracking such that a nonlinear function approximator is able to learn an error model on the desired trajectory. This, in turn, reduces the load on the adaptive feedback, enabling it to use a lower learning rate. Combined this creates a controller with variable feedback and low gains, and a feedforward model that can account for larger modeling errors. We demonstrate the effectiveness of this framework, in simulation and on a real system.