Iterative Grasp Pose Refinement: A Deep Reinforcement Learning Approach for 2D Vision

arXiv:2608.17628v1 Announce Type: new Abstract: Developing robots capable of understanding and manipulating objects requires compact, interpretable, and generalizable representations. This work proposes a reinforcement learning-based framework for robotic grasp refinement, integrating keypoint-based object representations with a Deep Q-Network (DQN). Using 2D overhead images captured in a simulated environment, a geometric-based algorithm generates initial grasp candidates, which are iteratively...