Physics-Guided Hierarchical Reward Mechanism for Learning-Based Robotic Grasping
This work addresses the practical adoption barriers of learning-based grasping for robotics, though it appears incremental as it builds on existing deep reinforcement learning approaches.
The authors tackled the low learning efficiency and poor generalizability of learning-based robotic grasping by developing a physics-guided deep reinforcement learning method with a hierarchical reward mechanism, which outperformed standard deep reinforcement learning methods in various grasping tasks.
Learning-based grasping can afford real-time grasp motion planning of multi-fingered robotics hands thanks to its high computational efficiency. However, learning-based methods are required to explore large search spaces during the learning process. The search space causes low learning efficiency, which has been the main barrier to its practical adoption. In addition, the trained policy lacks a generalizable outcome unless objects are identical to the trained objects. In this work, we develop a novel Physics-Guided Deep Reinforcement Learning with a Hierarchical Reward Mechanism to improve learning efficiency and generalizability for learning-based autonomous grasping. Unlike conventional observation-based grasp learning, physics-informed metrics are utilized to convey correlations between features associated with hand structures and objects to improve learning efficiency and outcomes. Further, the hierarchical reward mechanism enables the robot to learn prioritized components of the grasping tasks. Our method is validated in robotic grasping tasks with a 3-finger MICO robot arm. The results show that our method outperformed the standard Deep Reinforcement Learning methods in various robotic grasping tasks.