How Many Training Samples Are Needed for the Inverse Kinematics Solutions by Artificial Neural Networks
arXiv:2605.23583v1 Announce Type: cross Abstract: Inverse Kinematics (IK) plays a critical role in robotic motion planning and control. The IK solutions of a robot manipulator could be done by conventional ways such as geometric, algebraic, or Jacobian methods, which have drawbacks. The Artificial Neural Networks (ANNs) have become a promising alternative for approximating IK solutions due to their generalization ability and computational efficiency. This approach basically trains only a few samples of the end effector that are recorded for the solution of the IK problem. However, a fundamenta
The continuous advancements in AI and increased demand for sophisticated robotic control make efficient Inverse Kinematics solutions critical. This research addresses a fundamental challenge in applying ANNs to robotics at a time when industrial automation is rapidly expanding.
Improving the efficiency and reliability of Inverse Kinematics solutions through ANNs can significantly accelerate the development and deployment of advanced robotics. This can lead to more versatile and capable robots across various industries, from manufacturing to healthcare.
The research aims to optimize the training process for ANN-based Inverse Kinematics, potentially reducing computational resources and time required for robot programming. This improvement could make advanced robotic systems more accessible and easier to implement.
- · Robotics manufacturers
- · Automation industry
- · AI hardware developers
- · Researchers in reinforcement learning
- · Traditional IK solution providers
More efficient and adaptable robotic systems will emerge across manufacturing and logistics.
Reduced costs and increased capabilities in robotics will accelerate automation, impacting labor markets and supply chains.
Enhanced robotic autonomy could lead to breakthroughs in areas like hazardous environment exploration and advanced surgical procedures.
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