Learning with AI

Machine learning (ML) and deep learning (DL) enable the robots to learn repeatedly from the available data to the end that they can execute their tasks better as time goes on.

Robotics:

• Supervised Learning: Instructing robots to detect patterns, i.e., detecting faults in a production line.
• Reinforcement Learning: The ability of robots to learn to do things as best as possible, i.e., the ability of robots to learn maze navigation or the ability to grasp.
• Deep Neural Networks: Deep jobs, including image recognition or speech processing, are driven by these networks.

Applications:

• Training of robotics arms to offer accuracy when assembling the goods.
• The way to get drones to navigate around obstacles is by using reinforcement learning.
• A case is a robot arm that uses reinforcement learning to optimize its grips to enable it to pick fragile objects.

Computer Vision

The computer vision as an aspect has to do with the fact that the robots are able to view and comprehend the images being shown to them by the cameras and sensors this comes into play where a robot is attempting to move and detect things.

Robotics:

• Object Detection: finding and locating objects in real-time using an algorithm like YOLO or Faster R-CNN.
• Image Segmentation: It involves the division of the images into meaningful sections to proceed with activities like supporting the surgeries.
• Pose Estimation: The position and direction of objects or humans.

Applications:

• Automobiles with embedded technology that read road maps and road pedestrians.
• Robots sort the packages in warehouses.
• A computer vision delivery robot can recognize obstacles on a sidewalk.

Natural Language Processing

 NLP assists the robot to decipher the language of the humans and respond to them and it makes communication between the human and robot possible.

Robotics:

• Speech Recognition: Speaking the commands that are going to be fulfilled in practice.
• Dialogue Systems: To propel conversational-based robots like customer service robots.
• Sentiment Analysis: In which human sentiments are measured to make adaptive responses.

Applications:

• The social robots that direct the elderly patients to speak out loud.
• The restaurants have service robots that accept orders.
•  A robot having a humanlike appearance completes a command of a user to inform him of the weather through NLP.

Path Planning, Navigation

Artificial intelligence programs help robots to navigate through a room, past obstructions, and in laying paths.

Robotics:

• Search Strategy: A or Dijkstra algorithm to find out the optimal ways.
• SLAM ( simultaneous localization and mapping): Locating and at the same time mapping the robot.
• Motion planning: Searching the paths that do not cause collision around robotic arms or drones.

Applications:

• Machinery drones to survey crops.
• Hospital robots are walking down the corridors to deliver supplies.
• An instance is that of the house cleaner robot that uses SLAM to locate its way around a house and avoid any furniture and other objects.

Sensor Fusion

Sensor fusion In sensor fusion, data of different panels (e.g. LIDAR, cameras, IMUs) are used to provide a comprehensive view of the surrounding.

Robotics:

• Data integration: Fusion of sensor information to sense real information.
• Robustness: Enhancing dependability in a raucous or changing situation.
• Real-Time Processing: Real-time decisions.

Applications:

• Self-driving cars are integrated with the information from LIDAR, radar, and camera.
• The convergence of industrial robots with the environmental differences.
• An example of a robotic vacuum combines LIDAR and camera information to navigate around a room.

Control Reinforcement Learning

Reinforcement learning (RL) could be defined as a process of training robots to take sequential actions depending on the rewards that would teach them the desired behavior.

Robotics:

• Policy Learning: Making plans that are taken to accomplish tasks like reaching out or moving around.
• Flexibility: Providing the robots with the ability to handle unforeseen circumstances.
• Simulation: They learn it by practicing as it happens in a simulation, then start it in real life.

Applications:

• Trained robotic dogs that walk on rough tracks.
• Energy-efficient motions of robots in the industry.
• RL is used to demonstrate how to train a quadruped robot to climb up stairs.