Hierarchical reinforcement learning of multiple grasping strategies with human instructions

Grasping is an essential component for robotic manipulation and has been investigated for decades. Prior work on grasping often assumes that a sufficient amount of training data is available for learning and planning robotic grasps. However, constructing such an exhaustive training dataset is very challenging in practice, and it is desirable that a robotic system can autonomously learn and improves its grasping strategy. Although recent work has presented autonomous data collection through trial and error, such methods are often limited to a single grasp type, e.g. vertical pinch grasp. To address these issues, we present a hierarchical policy search approach for learning multiple grasping strategies. To leverage human knowledge, multiple grasping strategies are initialized with human demonstrations. In addition, a database of grasping motions and point clouds of objects is also autonomously built upon a set of grasps given by a user. The problem of selecting the grasp location and grasp policy is formulated as a bandit problem in our framework. We applied our reinforcement learning to grasping both rigid and deformable objects. The experimental results show that our framework autonomously learns and improves its performance through trial and error and can grasp previously unseen objects with a high accuracy.