用于基于学习的机器人抓取的物理引导分层奖励机制
Physics-Guided Hierarchical Reward Mechanism for Learning-Based Robotic Grasping
AI总结：
本文提出带分层奖励机制的物理引导深度强化学习方法，利用物理信息指标和任务优先级提升多指机器人自主抓取的学习效率与泛化能力，并在三指 MICO 机械臂上验证其优于标准 DRL。
摘要：
基于学习的抓取凭借其高计算效率，能够实现多指机器人手的实时抓取运动规划。然而，基于学习的方法需要在学习过程中探索庞大的搜索空间。该搜索空间导致学习效率低下，这一直是其实际应用的主要障碍。此外，除非物体与训练物体完全相同，否则训练得到的策略缺乏可泛化的结果。在本工作中，我们开发了一种新颖的带有分层奖励机制的物理引导深度强化学习方法，以提高基于学习的自主抓取的学习效率和泛化能力。与传统的基于观测的抓取学习不同，该方法利用物理信息指标来传达与手部结构和物体相关的特征之间的关联，从而提升学习效率和结果。此外，分层奖励机制使机器人能够学习抓取任务中的优先组成部分。我们的方法在使用三指 MICO 机械臂的机器人抓取任务中得到验证。结果表明，在多种机器人抓取任务中，我们的方法优于标准深度强化学习方法。
英文摘要：
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.
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