Transformer-Based Behavioral Evaluation Model for Graph-Attentive Service Robots

Aiming at the problem of difficult quantization of robot comfort value in dynamic environments, we proposed a robot behavior evaluation network based on transformer and graphical attention mechanism. Firstly, we defined the characteristics of environment nodes, robot nodes and pedestrian nodes. Secondly, pedestrians and robots are taken as nodes of the relationship graph. Information of each node is aggregated using graphical neural network and self-attention mechanism, so as to obtain the characteristics of the whole scene, which gives a comfort score for pedestrians. Finally, the parameters of the evaluation model were trained using the manually labeled public dataset SocNav2, and the results of the test dataset show that our designed evaluation network is closer to the real human feelings. Meanwhile, we built different scenarios in gazebo environment to verify the value of this robot behavior evaluation network in applications. The experimental results showed that our designed evaluation network can objectively represent the impact of robot behavior on the comfort of surrounding pedestrians, which is instructive for designing robot behavior.