AI in robotics lets machines sense the world, make decisions, and improve their actions over time. Instead of only following fixed instructions, an AI-powered robot can use cameras, sensors, and data to respond to changing situations. This matters in places where robots must work safely around people, objects, and obstacles.
Examples include warehouse pickers, self-driving cars, surgical robots, drones, and home assistants.
A useful way to understand AI robotics is through three connected capabilities: perception, planning, and learning. Perception turns sensor input, such as camera images, into labels and measurements like person, car, dog, distance, or speed. Planning uses those results to choose actions, such as finding a safe path from a start point to a goal while avoiding obstacles.
Learning improves the robot by using training data, feedback, or rewards so future actions become more accurate and efficient.
Key Facts
- AI robot loop: sense -> think -> act -> learn.
- Perception converts sensor data into useful information, such as object labels, positions, and distances.
- Planning chooses a sequence of actions to reach a goal while following rules and avoiding obstacles.
- A* path planning often uses f(n) = g(n) + h(n), where g(n) is cost so far and h(n) is estimated cost to the goal.
- Machine learning improves performance by finding patterns in data instead of relying only on hand-written rules.
- Reinforcement learning uses rewards: better actions receive higher reward values, so the robot learns which choices work best.
Vocabulary
- Perception
- Perception is the process of using sensors and AI to identify objects, locations, motion, and other features in the environment.
- Planning
- Planning is the process of selecting a safe and useful sequence of actions to move from a current state to a goal state.
- Machine Learning
- Machine learning is a type of AI in which a system improves by using data to recognize patterns and make predictions.
- Training Data
- Training data is a set of examples used to teach an AI model how to perform a task such as detecting objects or choosing actions.
- Reward Signal
- A reward signal is feedback that tells a learning robot how good or bad an action was for reaching its goal.
Common Mistakes to Avoid
- Thinking AI robots understand the world like humans, which is wrong because they process sensor data and patterns rather than having human awareness.
- Ignoring bad sensor data, which is wrong because poor lighting, blocked cameras, or noisy sensors can cause incorrect object detection and unsafe decisions.
- Assuming the shortest path is always the best path, which is wrong because a robot must also consider obstacles, safety rules, speed limits, and uncertainty.
- Training on too little or biased data, which is wrong because the robot may perform well in practice examples but fail in new real-world situations.
Practice Questions
- 1 A robot camera identifies 80 objects in a warehouse. If 72 labels are correct, what is the object detection accuracy as a percent?
- 2 A delivery robot compares two paths. Path A is 12 m long and takes 24 s. Path B is 15 m long and takes 25 s. What is the average speed for each path, and which is faster?
- 3 A self-driving car detects a person near the road, a parked car, and a construction cone. Explain how perception, planning, and learning each help the car choose a safe action.