A deep learning-based grasp estimation model designed to enable robotic manipulation with articulated objects that incorporates the attention-based semantic and geometric feature fusion (ASGF) module improved the grasp success rate in the evaluated setting.
Abstract
Unlike rigid object grasping, manipulating articulated objects requires grasp poses that ensure both stability and actionability, that is, the ability to interact with an object’s joints. Therefore, we propose a deep learning-based grasp estimation model designed to enable robotic manipulation with articulated objects. The model first utilizes RGB-D images to predict graspable regions in the form of semantic heatmaps, identifying where to grasp. These regions are then combined with geometric features derived from point clouds through an attention-based semantic and geometric feature fusion module, ultimately to regress a manipulable 6-DoF grasp pose. To train and evaluate the proposed model, we constructed a custom dataset composed of RGB-D images and 6-DoF grasp labels collected from real-world articulated objects such as doors. Real-world robotic experiments demonstrated the effectiveness of the proposed model in producing stable and actionable grasps for articulated-object manipulation. Furthermore, ablation studies showed that incorporating the attention-based semantic and geometric feature fusion (ASGF) module improved the grasp success rate in the evaluated setting.
A reinforcement learning-based framework for robotic grasp refinement, integrating keypoint-based object representations with a Deep Q-Network (DQN), is proposed, offering a scalable and adaptable solution for contact-rich manipulation tasks.
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