Ghost Attractor Networks are theoretically derived dynamical decoders that impose basin-attractor geometry on latent space via potential-drift dynamics, enabling efficient multi-modal sequential generation and closed-loop control.
Promp: Proximal meta-policy search,
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Aco2 trains a quadrotor policy in simulation that adapts to diverse payload dynamics via latent context encoding and contrastive structuring, enabling zero-shot real-world deployment for autonomous aerial delivery.
citing papers explorer
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Ghost Attractor Networks: Basin-Structured Dynamical Decoders for Closed-Loop Sequential Generation
Ghost Attractor Networks are theoretically derived dynamical decoders that impose basin-attractor geometry on latent space via potential-drift dynamics, enabling efficient multi-modal sequential generation and closed-loop control.
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Autonomous Aerial Manipulation via Contextual Contrastive Meta Reinforcement Learning
Aco2 trains a quadrotor policy in simulation that adapts to diverse payload dynamics via latent context encoding and contrastive structuring, enabling zero-shot real-world deployment for autonomous aerial delivery.