Human Intention Estimation based on Neural Networks for Enhanced Collaboration with Robots

Abstract

In human-robot collaboration, the robot is required to provide assistance to the user by facilitating task execution. However, due to stability requirements, a well-damped admittance behavior of the robot is necessary during interaction, thus inducing fatigue in the operator. While available schemes involve variable impedance controllers to mitigate this effect, here we propose an alternative approach entailing a proactive robot behavior that assists in the cooperative execution of trajectories towards desired goals, by estimating the user intention. To this end, we make use of Recurrent Neural Networks (RNNs) to predict and classify cooperative motions, on the basis of a set of predefined goals in the workspace and model-based generated data of human movements. Manual guidance validation experiments are conducted on a 6 d.o.f. ABB IRB140 industrial robot equipped with a force sensor.