Reinforcement Learning in Robotics | IABAC

Reinforcement Learning in Robotics | IABAC

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  Reinforcement Learning in Robotics iabac.org‌
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  RL is atype 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 Introduction to Reinforcement Learning (RL) iabac.org‌
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  Enables robots tolearn 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 Why RL is Important in Robotics iabac.org‌
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  Robotic Manipulation: ‌ Learningto 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 Applications in Robotics iabac.org‌
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  Challenges‌ Challenges & FutureOutlook 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
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  Thank you Visit: www.iabac.org‌ iabac.org‌