Sim2Real Transfer for Vision-Based Grasp Verification

• URL: http://arxiv.org/abs/2505.03046v1
• Date: Mon, 05 May 2025 22:04:12 GMT
• Title: Sim2Real Transfer for Vision-Based Grasp Verification
• Authors: Pau Amargant, Peter Hönig, Markus Vincze,
• Abstract summary: We present a vision-based approach for grasp verification to determine whether the robotic gripper has successfully grasped an object.<n>Our method employs a two-stage architecture; first YOLO-based object detection model to detect and locate the robot's gripper.<n>To address the limitations of real-world data capture, we introduce HSR-Grasp Synth, a synthetic dataset designed to simulate diverse grasping scenarios.
• Score: 7.9471205712560264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The verification of successful grasps is a crucial aspect of robot manipulation, particularly when handling deformable objects. Traditional methods relying on force and tactile sensors often struggle with deformable and non-rigid objects. In this work, we present a vision-based approach for grasp verification to determine whether the robotic gripper has successfully grasped an object. Our method employs a two-stage architecture; first YOLO-based object detection model to detect and locate the robot's gripper and then a ResNet-based classifier determines the presence of an object. To address the limitations of real-world data capture, we introduce HSR-GraspSynth, a synthetic dataset designed to simulate diverse grasping scenarios. Furthermore, we explore the use of Visual Question Answering capabilities as a zero-shot baseline to which we compare our model. Experimental results demonstrate that our approach achieves high accuracy in real-world environments, with potential for integration into grasping pipelines. Code and datasets are publicly available at https://github.com/pauamargant/HSR-GraspSynth .

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