Imitation Learning and Dimensionality Reduction for the Resolution of Inverse Kinematics in Anthropomorphic Robotic Arms

• Victor Alfonzo Cornejo Arismendi Universidad Católica San Pablo
• Dennis Barrios Aranibar Universidad Católica San Pablo

Abstract

This paper presents a novel human imitation-based methodology for solving the inverse kinematics problem in anthropomorphic robotic arms. Unlike traditional approaches, this proposal leverages human intuition and reduces mathematical complexity, facilitating the generation of natural and efficient kinematic solutions. The main innovation lies in a new mathematical approach for dimensionality reduction of human motion data, addressing the challenge of kinematic redundancy. The methodology consists of three phases: dimensionality reduction, nonlinear interpolation, and dimensionality expansion. In the first phase, human motion is captured with an exoskeleton and reduced to a representative variable. Then, a function is created to generalize kinematic knowledge, and finally, the robot’s joint angles are obtained. Experimental results, using a 7 degree-of-freedom robotic arm, demonstrate the system’s effectiveness in replicating complex human movements in a simulated environment. This research significantly contributes to humanoid robotics by simplifying the calculation process and facilitating the integration of robots into collaborative workspaces.

Keywords: Dimensionality reduction, Interpolation, Technological innovation, Redundancy, Humanoid robots, Collaboration, Kinematics, Manipulators, Robot sensing systems, Safety, Inverse Kinematics, Robotic Arm, Imitation Learning, Anthropomorphic Arm, Neural Networks, Non-Linear Interpolation, Dimensionality Reduction, Human Imitation