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Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation
Our aim is to devise a robotic reinforcement learning system for learning navigation and manipulation together, in an autonomous way without human intervention, enabling continual learning under realistic assumptions.
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