Learning-based grasping can afford real-time motion planning of
multi-fingered robotics hands thanks to its high computational efficiency.
However, it needs to explore large search spaces during its learning process.
The search space causes low learning efficiency, which has been the main
barrier to its practical adoption. In addition, the generalizability of the
trained policy is limited unless they are identical or similar to the trained
objects. In this work, we develop a novel Physics-Guided Deep Reinforcement
Learning with a Hierarchical Reward Mechanism to improve the 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, a
hierarchical reward mechanism is developed to enable the robot to learn the
grasping task in a prioritized way. It is validated in grasping tasks with a
MICO robot arm in both simulation and physical experiments. The results show
that our method outperformed the standard Deep Reinforcement learning method in
task performance by 48% and learning efficiency by 40%.

本文提出了基于物理引导的深度强化学习方法用于改善学习效率和推广性，在多指机器人手势能力的数据集上进行测试，结果表明，该方法在任务性能方面优于标准深度强化学习方法 48% 和学习效率 40%。