论文标题
物理学指导的基于学习机器人抓钩的层次奖励机制
Physics-Guided Hierarchical Reward Mechanism for Learning-Based Robotic Grasping
论文作者
论文摘要
基于学习的掌握可以负担得益于其高计算效率的多指机器人手的实时掌握运动计划。但是，需要基于学习的方法来在学习过程中探索大型搜索空间。搜索空间会导致低学习效率，这是其实际采用的主要障碍。此外，训练有素的政策缺乏可普遍的结果，除非对象与受过训练的对象相同。在这项工作中，我们通过分层奖励机制开发了一种新颖的物理学引导的深层增强学习，以提高学习效率和基于学习的自主掌握的概括性。与传统的基于观察的掌握学习不同，物理知识的指标被用来传达与手相关的特征与对象相关的特征之间的相关性，以提高学习效率和结果。此外，分层奖励机制使机器人能够学习掌握任务的优先组件。我们的方法在使用3指MICO机器人组的机器人抓握任务中进行了验证。结果表明，我们的方法在各种机器人抓握任务中都优于标准的深钢筋学习方法。
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.
