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Reinforcement Learning in Robotics | IABAC

Reinforcement Learning in robotics enables machines to learn tasks by trial and error, using feedback from their environment. Robots optimize actions to maximize rewards, allowing adaptive behavior, improved decision-making, and autonomy in dynamic, complex, or unpredictable settings.<br>

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Reinforcement Learning in Robotics | IABAC

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  1. Reinforcement Learning in Robotics iabac.org

  2. Introduction to Reinforcement Learning (RL) RL is a type of machine learning where agents learn by interacting with an environment. Key components: Age nt: The robot or system learning to perform a task Environment: The world or simulation the robot interacts with Reward: Feedback signal guiding learning Goal: Maximize cumulative rewards over time iabac.org

  3. Why RL is Important in Robotics Enables robots to learn complex behaviors without explicit programming Adap ts to dynamic and uncertain environments Reduces human intervention in repetitive or dangerous tasks Examples: robotic arms, autonomous vehicles, drones iabac.org

  4. Applications in Robotics Robotic Manipulation: Learning to grasp and move objects Navigation & Path Planning: Au tonomous movement in unknown terrains Human-Robot Interaction: Adapting actions based on human behavior Industrial Automation: Optimizing assembly line tasks iabac.org

  5. Challenges & Future Outlook Challenges High computational cost Sample inefficiency (needs many trials) Safety concerns in real-world training Future Outlook Improved simulation-to-real-world transfer Integration with other AI techniques (e.g., computer vision) Wider adoption in healthcare, manufacturing, and service robotics iabac.org

  6. Thank you Visit: www.iabac.org iabac.org

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