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

• URL: http://arxiv.org/abs/2608.17628v1
• Date: Tue, 18 Aug 2026 10:44:58 GMT
• Title: Iterative Grasp Pose Refinement: A Deep Reinforcement Learning Approach for 2D Vision
• Abstract summary: This work proposes a reinforcement learning-based framework for robotic grasp refinement.<n>A geometric-based algorithm generates initial grasp candidates, which are iteratively refined by the proposed framework.<n>Experiments conducted on 300 objects from the Dex-Net dataset demonstrate the framework's effectiveness.
• Score: 2.5899040911480182
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 refined by the proposed framework, transforming failed grasps into successful ones. Experiments conducted on 300 objects from the Dex-Net dataset using a UR5 manipulator demonstrate the framework's effectiveness, achieving a 100% success rate on objects previously deemed ungraspable by geometrical methods. The framework's sim-to-real transferability is further validated through physical experiments on a Delta parallel robot, where a refined grasp successfully manipulates an object that was previously ungraspable. The findings underscore the effectiveness of reinforcement learning in addressing challenges in robotic grasping, offering a scalable and adaptable solution for contact-rich manipulation tasks.

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