Robotics in Artificial Intelligence

Study Notes

Robotics is a subfield of artificial intelligence that involves the design, construction, and operation of robots that can perform tasks that typically require human intelligence.
The intersection of AI and robotics involves the development of robots that can perceive their environment, reason about the current state of the environment, and take actions to achieve a goal.

Autonomy: Robots with autonomy can operate independently, making decisions based on their programming and sensor data.
Sensing and Perception: Robots use sensors to perceive their environment, including cameras, lidars, and tactile sensors.
Action and Control: Robots use actuators to perform actions, such as moving, grasping, and manipulating objects.

Industrial Robots: Designed for repetitive tasks, such as assembly, welding, and painting.
Service Robots: Perform tasks that benefit humans, such as cleaning, cooking, and providing assistance.
Autonomous Robots: Capable of navigating and performing tasks without human intervention, such as self-driving cars and drones.
Humanoid Robots: Designed to mimic human appearance and movement, such as robots used for space exploration and search and rescue.

Manufacturing: Robots are used for assembly, inspection, and material handling.

Machine learning is a subfield of artificial intelligence that involves training machines to learn from data and make predictions or decisions without being explicitly programmed.

Supervised Learning: The machine is trained on labeled data to learn the relationship between input and output.
Unsupervised Learning: The machine is trained on unlabeled data to discover patterns or relationships.
Reinforcement Learning: The machine learns through trial and error by receiving rewards or penalties for its actions.

Training Data: The dataset used to train the machine learning model.
Model: The algorithm or set of rules used to make predictions or decisions.
Hyperparameters: Parameters set before training a model, such as learning rate or number of hidden layers.
Overfitting: When a model is too complex and performs well on training data but poorly on new data.
Underfitting: When a model is too simple and fails to capture the underlying pattern in the data.

Linear Regression: A linear model that predicts a continuous output variable.
Decision Trees: A tree-based model that splits data into subsets based on features.
Random Forest: An ensemble of decision trees that improves prediction accuracy.
Neural Networks: A model inspired by the human brain, composed of interconnected nodes (neurons).