Abstract
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.
Abstract (translated)
基于学习的模块运动规划管道呈现,能够在任务执行中符合要求、确保安全,并响应干扰。一个名义的运动计划,定义为非线性自主动态系统(DS),通过使用神经网络普通微分方程模型从触觉演示中学习 offline 。为了在推理期间保证稳定性和安全性,提出一种新方法,使用 learned 的 NODE 模型在每个时间步骤选择目标点,使用由 learned 的 NODE 模型生成的时变目标轨迹。一个 NODE 模型的修正 term 在线通过解决一个 Quadratic 程序计算,使用控制 Lyapunov 函数和控制屏障函数分别保证稳定性和安全性。我们在 LASA 手写数据集上比基线 DS 学习技术表现更好,并在真实机器人实验中验证,它显示产生稳定的运动,如擦拭和搅拌,同时 robust 到物理干扰,并在人类和障碍物周围安全。
URL
https://arxiv.org/abs/2308.00186
