Learning inverse kinematics: reduced sampling through decomposition into virtual robots.

Area of Science:

• Robotics
• Machine Learning
• Computational Science

Background:

• Learning robot manipulator inverse kinematics is computationally intensive.
• Existing decomposition methods often impose architectural constraints on robots.

Purpose of the Study:

• To develop a general technique for accelerating robot manipulator inverse kinematics learning.
• To overcome limitations of previous decomposition approaches by removing architectural requirements.

Main Methods:

• Decomposing the robot manipulator's inverse kinematics problem into multiple virtual robot arms.
• Utilizing Parametrized Self-Organizing Maps (PSOMs) for the learning process.
• Comparing direct learning results with those obtained through the decomposition method.

Main Results:

• Achieved significant speedups in learning inverse kinematics, up to two orders of magnitude.
• Demonstrated the generality of the decomposition technique across different robot architectures.
• Validated the effectiveness of PSOMs for this learning and comparison task.

Conclusions:

• The proposed decomposition technique offers a highly efficient and general solution for learning robot inverse kinematics.
• This approach substantially reduces computation time without compromising accuracy.
• The method is broadly applicable to various robot manipulator systems.