Agentic Ai with Dynamics and Kinematics Control Incorporated with RPA Attributes in a Humanoid Robotics with Predictive Machine Learning over Edge Computing

Artificial Intelligence or AI gives robots a computer vision to navigate, sense, plan act and calculate their reaction accordingly. Humanoid Robots mimic human behaviour , learn to perform their tasks from humans through machine learning is a part of computer programming and AI. Machine learning enables robots to adapt their manipulation strategies to unexpected changes or disturbances through continuous monitoring of the manipulation process. Humanoid Robots can then adjust their movements accordingly to maintain control,prevent errors, Quantization in non monotone neural schema. Machine learning algorithms enable Humanoid robots to learn from data and adapt to dynamic environments. These algorithms allow robots to identify patterns, make predictions, and improve their performance over time, making them more versatile and effective in a wide range of applications, Even if the robot is trained in a local environment for module learning, it is needed to arrive an objective point with no collision with obstacles in the static or dynamic environment , Adaptive fusion strategy in needed for selecting sensor information. To achieve this Neural networks are to be improvised for incorporating neural weights and for this the study of neural schema is required for choosing humanoid robot for the analysis.