Waypoint-Based Imitation Learning for Robotic Manipulation
Lucy Xiaoyang Shi Archit Sharma Tony Z. Zhao Chelsea Finn
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/