Robot Behavior Generation for Social Human-Robot Interaction

Kurzfassung

The increasing presence of robots in human workspaces underscores the need for intelligent systems that can understand human behaviors and act accordingly for a natural human-robot interaction (HRI). In this work, we propose a method to generate a robot's behavior for social HRI by integrating both human and robot intentions into the robot's decision-making process. Our system learns appropriate robot behaviors in social scenarios by observing human-human interactions (HHI). Using a transformer-based model, we first capture the dynamics of each individual and then iteratively adapt both human and robot behavior to achieve a successful interaction. By connecting our model with a human-to-robot motion retargeting framework, our system learns how a robot should behave solely from observing human data. To address the disparity between HHI and HRI, we employ several loss functions that encourage our robot to reproduce the social dynamics observed in humans. As a result, our approach outperforms the state-of-the-art in dyadic human motion forecasting prediction in the largest dataset available and obtains high-quality robot behaviors in human-robot interaction scenarios. Finally, we conduct a thorough evaluation of our performance for HHI, and HRI, and implement and test the system in the real-world TIAGo++ robot.