Adapting Transformer-Based Multi-Style Networks for Human Pose Prediction with a Custom Data Pipeline in Industrial Human-Robot Collaboration

This paper presents an innovative approach to predicting human joint trajectories in industrial human-robot collaboration using the Multi-Style Networks (MSN) model. By accurately anticipating human movements in shared workspaces, we can enhance safety and reduce the risk of accidents. Our predictions are a cornerstone in promoting a safe and productive work environment. The research extends to evaluating the MSN model, which is adapted explicitly for human joints, and developing a customized data pipeline using the CHICO dataset, expressly curated for human-robot interaction studies. While the direct assessment of safety enhancements is not within the scope of this paper, the precise forecasting of human joint movements by the MSN model is expected to contribute to the safety and efficiency of human-robot collaborations. By analyzing the model’s performance using metrics like Average Displacement Error (ADE) and Final Displacement Error (FDE), this study offers valuable insights into reducing collision risks and improving the synergy between humans and robots by taking into account the Operator’s action styles for predicting human movements.