Social-Aware Motion Planning with Human Behavioral Prediction Models

Building on Goto et al.'s (2024) discovery that robot face-turning during handovers induces human hesitation, this research proposes a novel motion planning framework that explicitly models human behavioral responses to robot social cues. Unlike traditional planners that treat humans as static obstacles (e.g., CB-MPC in Tajbakhsh et al., 2023), this approach uses real-time prediction of human action plans—derived from gaze, posture, and motion cues—to adapt robot trajectories proactively. By combining reinforcement learning (as in Zhang et al., 2023) with social psychology models, robots could learn to minimize human uncertainty (e.g., by avoiding ambiguous head-turns during critical handover phases). This diverges from existing MRMP methods (Liang et al., 2025) by prioritizing behavioral safety over collision avoidance alone, addressing a critical gap in human-centric robotics.

References:

1. Using Implicit Behavior Cloning and Dynamic Movement Primitive to Facilitate Reinforcement Learning for Robot Motion Planning. Zengjie Zhang, Jayden Hong, Amir M. Soufi Enayati, H. Najjaran (2023). IEEE Transactions on robotics.
2. Conflict-Based Model Predictive Control for Scalable Multi-Robot Motion Planning. Ardalan Tajbakhsh, L. Biegler, Aaron M. Johnson (2023). IEEE International Conference on Robotics and Automation.
3. Analysis of Hesitation Behavior by Human Receiver toward Facing Motion of Robot during Handover. Ryoya Goto, Modar Hassan, Kenji Suzuki (2024). 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN).
4. Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models. Jinhao Liang, Jacob Christopher, Sven Koenig, Ferdinando Fioretto (2025). arXiv.org.