Mastering complex sequential tasks continues to pose a significant challenge in robotics. While there has been progress in learning long-horizon manipulation tasks, most existing approaches lack rigorous mathematical guarantees for ensuring reliable and successful execution. In this paper, we extend previous work on learning long-horizon tasks and stable policies, focusing on improving task success rates while reducing the amount of training data needed. Our approach introduces a novel method that (1) segments long-horizon demonstrations into discrete steps defined by waypoints and subgoals, and (2) learns globally stable dynamical system policies to guide the robot to each subgoal, even in the face of sensory noise and random disturbances. We validate our approach through both simulation and real-world experiments, demonstrating effective transfer from simulation to physical robotic platforms. Code is available at https://github.com/Alestaubin/stable-imitation-policy-with-waypoints

本研究解决了机器人在复杂顺序任务中的学习难题，特别是在长时间操作任务中缺乏可靠性保证的问题。通过将长时间演示分段并学习全局稳定的动态系统策略，该方法提升了任务成功率，减少了所需训练数据。实验结果表明，该方法在仿真与实际应用中均表现出良好的转移能力，显示了重大的潜在应用价值。

长时间操作任务中稳定动态系统的单次学习