A 2084-parameter recurrent policy trained by distilling 1000 RL teacher policies enables zero-shot control across 10 real quadrotors differing in mass, motors, frames, propellers, and flight controllers.
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Evolving hexacopter morphologies together with learnable controllers produces unconventional drones that outperform standard designs on complex tasks while introducing new metrics for evolution-learning interactions.
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RAPTOR: A Foundation Policy for Quadrotor Control
A 2084-parameter recurrent policy trained by distilling 1000 RL teacher policies enables zero-shot control across 10 real quadrotors differing in mass, motors, frames, propellers, and flight controllers.
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Unconventional Hexacopters via Evolution and Learning: Performance Gains and New Insights
Evolving hexacopter morphologies together with learnable controllers produces unconventional drones that outperform standard designs on complex tasks while introducing new metrics for evolution-learning interactions.