Robotic Calibration Based on Haptic Feedback Improves Sim-to-Real Transfer
Journal:
arXiv
Published Date:
Jul 11, 2025
Abstract
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.
