Learning Manipulation Skills Via Hierarchical Spatial Attention
This work addresses the problem of efficient skill learning in robotics for researchers and practitioners, but it is incremental as it builds on existing attention-based methods with specific constraints.
The paper tackled the challenge of learning generalizable robotic manipulation skills by introducing a hierarchical spatial attention method to simplify attention policy learning and analyzing conditions where partial observability does not hinder optimal policy finding, achieving successful real-robot experiments on pick-place tasks.
Learning generalizable skills in robotic manipulation has long been challenging due to real-world sized observation and action spaces. One method for addressing this problem is attention focus -- the robot learns where to attend its sensors and irrelevant details are ignored. However, these methods have largely not caught on due to the difficulty of learning a good attention policy and the added partial observability induced by a narrowed window of focus. This article addresses the first issue by constraining gazes to a spatial hierarchy. For the second issue, we identify a case where the partial observability induced by attention does not prevent Q-learning from finding an optimal policy. We conclude with real-robot experiments on challenging pick-place tasks demonstrating the applicability of the approach.