基于动态系统的机器人模仿学习方法研究

针对当前机器人模仿学习过程中，运动模仿存在无法收敛到目标点以及泛化能力差的问题，引入一种基于动态系统（dynamical system，DS）的模仿学习方法。该方法通过高斯混合模型（gaussian mixture model，GMM）将示教运动数据建模为一非线性动态系统；将DS全局稳定的充分条件作为约束，以保证DS所生成的所有轨迹收敛到目标点；将动态系统模型的参数学习问题转化为求解一个约束优化问题，从而得到模型参数。以7bot机械臂为实验对象，进行仿真实验和机器人实验，实验结果表明:该方法学习的DS模型从不同起点生成的所有轨迹都收敛到目标点，轨迹平滑，泛化能力好。

Research on robot imitation learning method based on dynamical system

In the current robot imitation learning process, the motion imitation cannot converge to the target point, and the generalization ability is poor. To solve this problem, an imitation learning method based on dynamical system (DS) is introduced. First, the demonstration motion data is modeled as a nonlinear DS by Gaussian mixture model (GMM). Second, the sufficient condition of DS global stability is used as a constraint to ensure that all the DS-generated trajectories converge to the target. Finally, the parameter learning problem of the DS model is transformed into seeking for a solution to a constrained optimization problem to obtain the model parameters. Simulation experiments and robot experiments were carried out using the 7bot manipulator. The experimental results show that all the trajectories generated by the DS model from different starting points converged to the target point, and the trajectory was smooth and the generalization performance was improved.