用触觉反馈校准机器人，提升仿真到现实的精度。
Robotic Calibration Based on Haptic Feedback Improves Sim-to-Real Transfer
- 通过触摸屏获取真实末端位置，补全仿真缺失信息。
- 非线性神经网络模型将定位误差显著降低。
- 适合需要高精度仿真实现的机器人控制场景。
在采用逆运动学控制机械臂执行操作任务时，仿真中机器人的末端执行器（EE）位置与实际物理位置常存在差异。大多数具有仿真到现实迁移的场景中，我们掌握仿真和现实中各关节的位置信息，但仅仿真中可获得末端执行器位置。为此，本文提出一种基于触觉反馈的校准方法：利用位于机器人前方的触摸屏，记录机器人接触屏幕特定点时的真实末端位置信息。校准阶段完成后，构建基于线性变换与神经网络的映射函数，能够根据任意部分输入（仿真/真实关节/末端位置）输出所有缺失变量。实验结果表明，完全非线性的神经网络模型表现最佳，显著降低了定位误差。
原文摘要 · Abstract (English)
When inverse kinematics (IK) is adopted to control robotic arms in manipulation tasks, there is often a discrepancy between the end effector (EE) position of the robot model in the simulator and the physical EE in reality. In most robotic scenarios with sim-to-real transfer, we have information about joint positions in both simulation and reality, but the EE position is only available in simulation. We developed a novel method to overcome this difficulty based on haptic feedback calibration, using a touchscreen in front of the robot that provides information on the EE position in the real environment. During the calibration procedure, the robot touches specific points on the screen, and the information is stored. In the next stage, we build a transformation function from the data based on linear transformation and neural networks that is capable of outputting all missing variables from any partial input (simulated/real joint/EE position). Our results demonstrate that a fully nonlinear neural network model performs best, significantly reducing positioning errors.
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