Human-Inspired Pre-Design Optimization for Humanoid Robots in Dynamic Interaction Tasks

This study presents a novel pre-design optimization framework for humanoid robots, enabling precise alignment of robotic kinematics with human motion patterns for dynamic tasks. The aim is to reduce prototyping costs and support task-specific designs. Leveraging mechatronics expertise, the framework features an interactive interface for real-time parameter tuning, guided by metrics such as workspace coverage and inverse kinematics (IK) success rate. The predesign tool is applied on a 3-DOF arm, part of an upper-body humanoid robot under development, achieves <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 0 0 \%}$</tex> workspace coverage and an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{8 5. 5 1 \%}$</tex> IK success rate across four experiments for jabs and hooks. Motion similarity metrics were used to validate human-like performance and smoothness.