A Hybrid Inverse Kinematics Framework for Biomimetic Redundancy Resolution in 7-DoF Humanoid Arms.

Area of Science:

• Robotics
• Biomimetic Robotics
• Control Systems

Background:

• Humanoid robot arms with 7 degrees-of-freedom (DoF) face challenges in resolving kinematic redundancy for natural motion generation.
• Existing methods often struggle to achieve human-like movements and maintain end-effector tracking accuracy.

Purpose of the Study:

• To present a hybrid inverse kinematics (IK) framework that learns pose-dependent parameters for natural redundancy resolution in 7-DoF humanoid arms.
• To integrate a learned redundancy parameter into a differential IK solver for improved motion generation.

Main Methods:

• Employed the stereographic Shoulder-Elbow-Wrist (SEW) angle for geometric parameterization, transforming algorithmic singularities.
• Collected a motion dataset via teleoperation with an anthropomorphic wearable exoskeleton to capture operator-specific postural preferences.
• Trained a lightweight neural network to map end-effector poses to operator-specific SEW angles, used as a dynamic secondary objective in the IK solver.

Main Results:

• The proposed framework achieved a 22.5% improvement in Joint Configuration Quality Index (CQI) compared to static methods.
• Demonstrated an 11.3% reduction in energy costs.
• Achieved sub-millisecond inference latency (0.44 ms), suitable for real-time control.

Conclusions:

• The hybrid IK framework effectively resolves kinematic redundancy for natural, human-like motions in humanoid arms.
• The approach preserves end-effector tracking accuracy while enhancing motion quality and energy efficiency.
• The low inference latency enables practical real-time implementation in robotic control systems.