DreamerV3 enables pixel-to-control policies for drone racing that reach 9 m/s in both simulation and real hardware-in-the-loop tests.
End-to-end reinforcement learn- ing for time-optimal quadcopter flight
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A sensorimotor policy with a pre-trained autoencoder perception head and LSTM controller, trained in two stages via privileged learning and curriculum reinforcement learning with domain randomization, achieves zero-shot transfer for outdoor obstacle evasion on unseen environments and platforms.
citing papers explorer
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Dream to Fly: Model-Based Reinforcement Learning for Vision-Based Drone Flight
DreamerV3 enables pixel-to-control policies for drone racing that reach 9 m/s in both simulation and real hardware-in-the-loop tests.
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Vision-Guided Outdoor Flight and Obstacle Evasion via Reinforcement Learning
A sensorimotor policy with a pre-trained autoencoder perception head and LSTM controller, trained in two stages via privileged learning and curriculum reinforcement learning with domain randomization, achieves zero-shot transfer for outdoor obstacle evasion on unseen environments and platforms.