An Ergo-Interactive Framework for Human-Robot Collaboration Via Learning From Demonstration

Abstract

This work presents an ergonomic and interactive human-robot collaboration (HRC) framework, through which new collaborative skills are extracted from a one-shot human demonstration and learned through Riemannian dynamic movement primitives (DMP). The proposed framework responds to human-robot interaction forces to adapt to the task requirements, while generating virtual “ergonomic forces” that guide the human toward more ergonomic postures, based on online monitoring of a kinematics-based index. The resulting motion is then integrated into the learned task trajectories. The framework is implemented on a mobile manipulator with a weighted whole-body Cartesian velocity controller, which meets the needs of large-scale HRC. To evaluate the proposed framework, a multi-subject experiment involving a human-robot co-carrying task is conducted. The performance of the ergo-interactive control in terms of task performance and ergonomics adaptation is verified under different experimental conditions. This is followed by a comparative statistical analysis. The experimental results show that the learned trajectory can be reproduced and generalized to several targets and adjusted online according to human preferences and ergonomics.