While imitation learning methods have seen a resurgent interest for robotic manipulation, the well-known problem of compounding errors continues to afflict behavioral cloning (BC). Waypoints can help address this problem by reducing the horizon of the learning problem for BC, and thus, the errors compounded over time. However, waypoint labeling is underspecified, and requires additional human supervision. Can we generate waypoints automatically without any additional human supervision? Our key insight is that if a trajectory segment can be approximated by linear motion, the endpoints can be used as waypoints. We propose Automatic Waypoint Extraction (AWE) for imitation learning, a preprocessing module to decompose a demonstration into a minimal set of waypoints which when interpolated linearly can approximate the trajectory up to a specified error threshold. AWE can be combined with any BC algorithm, and we find that AWE can increase the success rate of state-of-the-art algorithms by up to 25% in simulation and by 4-28% on real-world bimanual manipulation tasks, reducing the decision making horizon by up to a factor of 10. Videos and code are available at https://lucys0.github.io/awe/

翻译：虽然模仿学习方法在机器人操作领域重新受到关注，但行为克隆（BC）中众所周知的复合误差问题仍然存在。航路点可以通过缩短BC学习问题的时域来解决这一问题，从而减少随时间累积的误差。然而，航路点的标注缺乏明确规范，需要额外的人工监督。我们能否在不增加任何人工监督的情况下自动生成航路点？我们的关键见解是：如果轨迹片段可近似为线性运动，则其端点可作为航路点。为此，我们提出用于模仿学习的自动航路点提取（AWE）模块——一种预处理组件，可将示范轨迹分解为最小航路点集合，通过线性插值可逼近原始轨迹至指定误差阈值。AWE可与任意BC算法结合使用。实验表明，AWE可使最先进算法在仿真中的成功率提升高达25%，在真实双臂操作任务中提升4-28%，并将决策时域缩短至原本的十分之一。视频与代码详见https://lucys0.github.io/awe/