MCQ IN COMPUTER SCIENCE & ENGINEERING

Previous
Next

COMPUTER SCIENCE AND ENGINEERING

MACHINE LEARNING

Question [CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
Robotic tasks include a multitude of ML methods tailored towards navigation, robotic control and several other tasks. Robotic control includes controlling the actuators available to the robotic system. An example of this is control of a robotic arm in paint-shops in automotive industries. The robotic arm must be able to paint every corner in the automotive parts while minimizing the quantity of paint wasted in the process. Which of the following learning paradigms would you select for training such a robotic arm?
A
Supervised learning
B
Unsupervised learning
C
Combination of supervised and unsupervised learning
D
Reinforcement learning
Explanation: 

Detailed explanation-1: -Answer: The correct answer is self-supervised learning and Reinforcement Learning. Explanation: The training that we can use for learning paradigms for the robotic arms will be Supervised Learning and Reinforcement Learning.

Detailed explanation-2: -Northwester University developed the MICO robotic arm that is the most recent example of machine learning-based robotic assistive technologies, and it is developed by combining assistive machines with more autonomy.

Detailed explanation-3: -Precise machine learning processes are being used to train robots and improve accuracy. Artificial intelligence teaches functions like spatial relations, grasping objects, computer vision, motion control, etc., in robots to make them understand and work on unseen data and situations.

Detailed explanation-4: -Examples are the robot dog Aibo, the Roomba vacuum, AI-powered robot assistants, and a growing variety of robotic toys and kits. Disaster Response: These robots perform dangerous jobs like searching for survivors in the aftermath of an emergency.

There is 1 question to complete.
Previous
Next