Realizing Human-Robot Cooperative Rope-Spinning with Central Pattern Generator-Based Control Using Visual Information
Résumé
Achieving coordinated motion through flexible objects remains a significant challenge in Human-Robot Interaction (HRI). This study investigates a novel application of Central Pattern Generator (CPG) control, previously used in handshake robots, to a rope-spinning task involving human-robot cooperation. A real-time motion feedback system was developed using Azure Kinect, enabling a robot to synchronize its movements with human input by dynamically adjusting CPG outputs. We evaluated the system’s performance by varying rope lengths (250– 400 cm) and analyzing spatial trajectories and Euclidean distances between the human and robot end-effectors. Results showed that while high coordination was achieved under shorter rope conditions, longer ropes introduced increased slack and tension variability, which reduced the robot’s tracking stability. Frequency analysis also revealed weaker synchronization on the robot side, particularly in the vertical (Z) direction. These findings indicate that vision-based feedback alone is insufficient for robust adaptation to the dynamic characteristics of flexible objects. The vision-based method demonstrated lower amplitude fidelity and synchronization precision than our previous force-feedback approach. Future work will focus on integrating multimodal feedback, combining visual and force sensing, to improve coordination and robustness in flexible-object-mediated HRI.