Using Deep Neural Networks to Improve Contact Wrench Estimation of Serial Robotic Manipulators in Static Tasks

Reliable force-driven robot-interaction requires precise contact wrench measurements. In most robot systems these measurements are severely incorrect and in most manipulation tasks expensive additional force sensors are installed. We follow a learning approach to train the dependencies between joint torques and end-effector contact wrenches. We used a redundant serial light-weight manipulator (KUKA iiwa 7 R800) with integrated force estimation based on the joint torques measured in each of the robot's seven axes. Firstly, a simulated dataset is created to let a feed-forward net learn the relationship between end-effector contact wrenches and joint torques for a static case. Secondly, an extensive real training dataset was acquired with 330,000 randomized robot positions and end-effector contact wrenches and used for retraining the simulated trained feed-forward net. We can show that the wrench prediction error could be reduced by around 57% for the forces compared to the manufacturer's proprietary force estimation model. In addition, we show that the number of high outliers can be reduced substantially. Furthermore we prove that the approach could be also transferred to another robot (KUKA iiwa 14 R820) with reasonable prediction accuracy and without the need of acquiring new robot specific data.