Controllers for Compliant and Safe Human-Humanoid Collaboration

Our research focuses on developing advanced control frameworks that enable humanoid robots to collaborate physically with humans, particularly in tasks such as object transportation and manipulation. This research addresses the challenges of ensuring safe and effective collaboration between humans and robots, especially in environments where the robot’s compliance with the human’s movements is critical.

A significant contribution of this project is the development of a Model Predictive Control (MPC) framework, integrated with an admittance control model, designed to adjust the robot’s behavior dynamically based on interaction forces. The framework uses a novel Interaction Linear Inverted Pendulum (I-LIP) model to generate footstep patterns, ensuring the robot’s stability while exhibiting compliant behavior during the task.

This work pushes the boundaries of human-robot collaboration by enabling humanoid robots to perform tasks that typically require two human partners, with a focus on maintaining safety and agility during complex interactions.

Work has been supported by the following grants: NSF 2018905.