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Robotics study finds 125 samples sufficient for ANN inverse kinematics

By PulseAugur Editorial · [2 sources] ·

A new study published on arXiv investigates the optimal number of training samples required for artificial neural networks (ANNs) to accurately solve inverse kinematics (IK) problems in robotics. The research found that beyond 125 training samples, additional data did not significantly improve the model's efficiency or approximation accuracy. This work offers practical guidance for optimizing data requirements in ANN-based IK solutions, balancing computational costs with desired accuracy for robotic applications. AI

IMPACT Provides practical guidance on data efficiency for ANN-based IK solutions, potentially reducing computational costs in robotics.

RANK_REASON This is a research paper published on arXiv detailing findings about the optimal number of training samples for ANNs in robotics.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Robotics study finds 125 samples sufficient for ANN inverse kinematics

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This is a research paper published on arXiv detailing findings about the optimal number of training samples for ANNs in robotics.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Dong-Won Lim ·

    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 drawbac…

  2. arXiv cs.LG TIER_1 English(EN) · Dong-Won Lim ·

    How Many Training Samples Are Needed for the Inverse Kinematics Solutions by Artificial Neural Networks

    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 bec…

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