Touch Gesture Recognition-Based Physical Human–Robot Interaction for Collaborative Tasks

Abstract

Human–robot collaboration (HRC) has recently attracted increasing attention as a vital component of next-generation automated manufacturing and assembly tasks, yet physical human–robot interaction (pHRI)—which is an inevitable component of collaboration—is often limited to rudimentary touches. This article therefore proposes a deep-learning-based pHRI method that utilizes predefined types of human touch gestures as intuitive communicative signs for collaborative tasks. To this end, a touch gesture network model is first designed upon the framework of the gated recurrent unit (GRU) network, which accepts a set of ground-truth dynamic responses (energy change, generalized momentum, and external joint torque) of robot manipulators under the action of known types of touch gestures and learns to predict the five representative touch gesture types and the corresponding link toward a random touch gesture input. After training the GRU-based touch gesture model using a collected dataset of dynamic responses of a robot manipulator, a total of 35 outputs (five gesture types with seven links each) is recognized with 96.94% accuracy. The experimental results of recognition accuracy correlated with the touch gesture types, and their strength results are shown to validate the performance and disclose the characteristics of the proposed touch gesture model. An example of an IKEA chair assembly task is also presented to demonstrate a collaborative task using the proposed touch gestures. By developing the proposed pHRI method and demonstrating its applicability, we expect that this method can help position physical interaction as one of the key modalities for communication in real-world HRC applications.