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CoFL-S achieves superior navigation performance by leveraging language-conditioned flow fields, outperforming traditional action representations in both simulation and real-world applications.
Stabilizing system energy with a novel regularization loss lets neural MPC outperform analytical methods for controlling omnidirectional aerial robots.
A novel 2-DoF crank-slider mechanism lets a wire-driven robotic fish swim fast *and* turn sharply, breaking the trade-off between speed and maneuverability.
Forget discrete actions: CoFL navigates with smooth, continuous flow fields, outperforming modular and generative baselines in language-conditioned tasks and even working zero-shot in the real world.
