A learning-based modular motion planning pipeline is presented that is compliant, safe, and reactive to perturbations at task execution. A nominal motion plan, defined as a nonlinear autonomous dynamical system (DS), is learned offline from kinesthetic demonstrations using a Neural Ordinary Differential Equation (NODE) model. To ensure both stability and safety during inference, a novel approach is proposed which selects a target point at each time step for the robot to follow, using a time-varying target trajectory generated by the learned NODE. A correction term to the NODE model is computed online by solving a Quadratic Program that guarantees stability and safety using Control Lyapunov Functions and Control Barrier Functions, respectively. Our approach outperforms baseline DS learning techniques on the LASA handwriting dataset and is validated on real-robot experiments where it is shown to produce stable motions, such as wiping and stirring, while being robust to physical perturbations and safe around humans and obstacles.

翻译：提出了一种基于学习的模块化运动规划管道，该管道在执行任务时具有柔顺性、安全性，并能对扰动做出反应。名义运动规划被定义为非线性自主动力学系统，通过神经常微分方程（NODE）模型从动觉示教中离线学习。为确保推理过程中的稳定性和安全性，我们提出了一种新颖方法，该方法在每个时间步为机器人选择一个目标点，使其跟随由学习到的NODE生成的时变目标轨迹。通过求解一个二次规划问题在线计算NODE模型的修正项，该二次规划分别利用控制李雅普诺夫函数和控制障碍函数保证稳定性和安全性。我们的方法在LASA手写数据集上优于基线动力学系统学习技术，并在真实机器人实验中得到了验证，展示了其在擦拭和搅拌等任务中产生稳定运动的能力，同时能抵抗物理扰动，并在人类和障碍物周围保持安全。