Robustness Evaluation of Machine Learning Models for Robot Arm Action
  Recognition in Noisy Environments
Elaheh Motamedi Kian Behzad Rojin Zandi Hojjat Salehinejad Milad Siami
In the realm of robot action recognition, identifying distinct but spatially
proximate arm movements using vision systems in noisy environments poses a
significant challenge. This paper studies robot arm action recognition in noisy
environments using machine learning techniques. Specifically, a vision system
is used to track the robot's movements followed by a deep learning model to
extract the arm's key points. Through a comparative analysis of machine
learning methods, the effectiveness and robustness of this model are assessed
in noisy environments. A case study was conducted using the Tic-Tac-Toe game in
a 3-by-3 grid environment, where the focus is to accurately identify the
actions of the arms in selecting specific locations within this constrained
environment. Experimental results show that our approach can achieve precise
key point detection and action classification despite the addition of noise and
uncertainties to the dataset.