What is Reinforcement Learning?
Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. It’s widely used in robotics to enable machines to perform complex tasks through trial and error.
Key Applications in Robotics
- Autonomous Navigation: Robots use RL to adapt to dynamic environments (e.g., self-driving cars).
- Manipulation Skills: Training robots to grasp objects or assemble parts (e.g., robotic arms).
- Human-Robot Interaction: Improving collaboration through adaptive behavior (e.g., assistive robots).
Why RL for Robotics?
- Adaptability: Learns from real-world feedback rather than pre-defined rules.
- Scalability: Handles high-dimensional sensor data and complex actions.
- Efficiency: Optimizes performance over time through reward maximization.
Further Reading
Explore more about AI in robotics: /en/robotics/ai_overview