Dynamic Movement Primitive based Motion Retargeting for Dual-Arm Sign Language Motions
This addresses the challenge of equipping service robots with sign language skills, focusing on dual-arm coordination, which is incremental as it builds on existing motion retargeting techniques.
The paper tackles the problem of transferring complex dual-arm sign language motions from human to robot, proposing a method using graph optimization and Dynamic Movement Primitives, and successfully demonstrates it on a 26-DOF robot system with Chinese Sign Language motions.
We aim to develop an efficient programming method for equipping service robots with the skill of performing sign language motions. This paper addresses the problem of transferring complex dual-arm sign language motions characterized by the coordination among arms and hands from human to robot, which is seldom considered in previous studies of motion retargeting techniques. In this paper, we propose a novel motion retargeting method that leverages graph optimization and Dynamic Movement Primitives (DMPs) for this problem. We employ DMPs in a leader-follower manner to parameterize the original trajectories while preserving motion rhythm and relative movements between human body parts, and adopt a three-step optimization procedure to find deformed trajectories for robot motion planning while ensuring feasibility for robot execution. Experimental results of several Chinese Sign Language (CSL) motions have been successfully performed on ABB's YuMi dual-arm collaborative robot (14-DOF) with two 6-DOF Inspire-Robotics' multi-fingered hands, a system with 26 DOFs in total.