Human-Inspired Redundancy Resolution for Upper-Limb Robotics Using Phase-Dominant Performance Criteria
The aim of this study is to develop improved motion planning algorithms for humanoid robots and to enhance prosthetic control and rehabilitation training through predictive interfaces grounded in natural human movement. To address this aim, we introduce a synthesized biomechanically plausible human-inspired inverse-kinematics framework for a 10-DOF Robotic Human Upper-Body Model (RHUBM) that resolves redundancy via phase-dominant performance criteria. We hypothesize that humans prioritize lighter joint movements over heavier ones and integrate various performance criteria such as manipulability, velocity ratio, and mechanical advantage while avoiding joint limits during different Activities of Daily Living (ADLs) tasks and propose a phase-dependent weighting strategy derived from statistical analysis of human motion. To validate our hypothesis, a subject-specific motion-capture dataset of Range-of-Motion and ADL tasks was created, phase-dominant performance criteria were identified by segmenting each task trajectory into major phases, and a Weighted Least-Norm (WLN) inverse kinematics controller was implemented whose joint-space weight matrix combines link weighting, joint-limit avoidance weights, and gradient-based weights derived from the phase-dominant performance criteria. The WLN algorithm outcomes were compared against motion capture (MoCap) data and the Least Norm (LN) solution. Results on a representative ADL (Drinking) demonstrate phase-dependent criterion switching, consistent with our hypothesis: Linear manipulability dominates during grasp/return phases, while angular velocity ratio dominates near the mouth. Across joints, WLN produces MoCap-consistent trajectories with consistently lower RMS errors than the LN solution. The framework formalizes human-inspired redundancy resolution and supports principled motion synthesis for humanoids, prosthetics, and rehabilitation interfaces.