OPG-Policy：基于非模态分割的遮挡推抓策略学习
OPG-Policy: Occluded Push-Grasp Policy Learning with Amodal Segmentation
AI总结：
提出OPG-Policy框架，利用非模态分割预测目标被遮挡部分，通过深度Q学习结合领域知识协调推抓动作，在密集杂乱场景中有效提升了检索遮挡目标的成功率与动作效率。
摘要：
密集杂乱环境中的目标导向抓取是机器人领域的一项基本挑战，需要自适应策略来处理被遮挡的目标物体和多样的配置。以往方法通常基于遮挡目标的部分可观测片段学习策略以生成动作。然而，由于不同场景中不同遮挡目标物体不可见部分的不确定性，这些策略往往难以生成最优动作，导致动作效率低下。为此，我们提出OPG-Policy，一种利用非模态分割预测目标被遮挡部分的新框架，并针对目标物体被部分观测的杂乱场景开发自适应推抓策略。具体而言，我们的方法为多样目标物体训练专用非模态分割模块以生成非模态掩码。这些掩码和场景观测通过深度Q学习映射到抓取和推动作原语的未来奖励，以学习动作评论器。随后，评论器预测的推和抓动作候选及相关领域知识被输入协调器，以生成由机器人执行的最优动作。在模拟和真实环境中进行的大量实验证明了我们方法在生成检索遮挡目标动作序列方面的有效性，在成功率和动作效率上优于其他基线方法。
英文摘要：
Goal-oriented grasping in dense clutter, a fundamental challenge in robotics, demands an adaptive policy to handle occluded target objects and diverse configurations. Previous methods typically learn policies based on partially observable segments of the occluded target to generate motions. However, these policies often struggle to generate optimal motions due to uncertainties regarding the invisible portions of different occluded target objects across various scenes, resulting in low motion efficiency. To this end, we propose OPG-Policy, a novel framework that leverages amodal segmentation to predict occluded portions of the target and develop an adaptive push-grasp policy for cluttered scenarios where the target object is partially observed. Specifically, our approach trains a dedicated amodal segmentation module for diverse target objects to generate amodal masks. These masks and scene observations are mapped to the future rewards of grasp and push motion primitives via deep Q-learning to learn the motion critic. Afterward, the push and grasp motion candidates predicted by the critic, along with the relevant domain knowledge, are fed into the coordinator to generate the optimal motion implemented by the robot. Extensive experiments conducted in both simulated and real-world environments demonstrate the effectiveness of our approach in generating motion sequences for retrieving occluded targets, outperforming other baseline methods in success rate and motion efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
