发表机构
Comenius University Bratislava（布拉迪斯拉发夸美纽斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对人形机器人模拟到现实转换中因定位误差妨碍抓取的问题，通过添加多种检测和定位方法及校正，形成基于校准的抓取管道，并实现视觉反馈模型，提高了抓取成功率。
AI 中文摘要
机器人抓取需要视觉感知、物体定位、逆运动学和手部控制之间的精确协调。模拟中规划的动作在物理机器人上执行时，模拟到现实的差距会导致小定位误差，妨碍成功抓取。此前研究引入低成本触觉校准方法提高了人形机器人NICO的二维到达精度。本文通过添加基于YOLO的物体和手部检测、利用机器人内置低分辨率鱼眼相机的立体视觉定位以及抓取执行的特定任务校正，将该方法从到达扩展到桌面物体抓取。这些组件形成了一种新颖的基于校准的抓取管道，无需RGB-D相机、动作捕捉或外部跟踪系统。还实现了视觉反馈模型，在抓取前将机器人手与检测到的物体对齐。结果表明，完全非线性校准模型在校准区域内性能最佳，视觉反馈模型在整个桌面工作空间的总体抓取成功率最高。
英文摘要
Robotic grasping requires accurate coordination between visual perception, object localization, inverse kinematics, and hand control. However, when movements planned in simulation are executed on a physical robot, the sim-to-real gap can cause small positioning errors that prevent successful grasping. In our previous work, we introduced a low-cost haptic calibration method that improved 2D reaching accuracy of the humanoid robot NICO. In this paper, we extend this approach from reaching to tabletop object grasping by adding YOLO-based object and hand detection, stereo vision-based localization using the robot's built-in low-resolution fisheye cameras, and task-specific corrections for grasp execution. Together, these components form a novel calibration-based grasping pipeline that does not require RGB-D cameras, motion capture, or external tracking systems. We also implemented a visual feedback model that aligns the robot hand with the detected object before grasping. Our results show that the fully nonlinear calibration model achieved the best performance inside the calibrated area, while the visual feedback model achieved the highest overall grasping success across the full tabletop workspace.
Comments12 pages, 8 figures, accepted to International Conference on Artificial Neural Networks 2026, Neurorobotics workshop
