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Safe Reinforcement Learning for Robotics
During this project, we will develop machine learning based techniques to let a (real) drone learn to fly nimbly through gaps and gates, while minimizing the risk of critical failures and collisions.
Reinforcement Learning (RL) has recently emerged has a technique to let robots learn by their own experience.
Current methods for RL are very data-intensive, and require a robot to fail many times before actually accomplishing their goal.
However some systems, such as flying robots, require to respect safety constraints during learning and/or deployment.
While maximizing performance, those methods usually aim to minimize the number of system failures and overall risk.
Reinforcement Learning (RL) has recently emerged has a technique to let robots learn by their own experience. Current methods for RL are very data-intensive, and require a robot to fail many times before actually accomplishing their goal. However some systems, such as flying robots, require to respect safety constraints during learning and/or deployment. While maximizing performance, those methods usually aim to minimize the number of system failures and overall risk.
During this project, we will develop machine learning based techniques to let a (real) drone learn to fly nimbly through gaps and gates, while minimizing the risk of critical failures and collisions.
During this project, we will develop machine learning based techniques to let a (real) drone learn to fly nimbly through gaps and gates, while minimizing the risk of critical failures and collisions.