Demo-JEPA enables one-shot cross-embodiment imitation by mapping visual demonstrations to shared latent future trajectories that serve as subgoals for the target agent's own forward dynamics planning.
Imitation learning: A survey of learning methods.ACM Computing Surveys (CSUR), 50(2):1–35
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ZALT learns latent hub states and hub-to-hub dynamics from demonstrations to plan zero-shot solutions for unseen start-goal tasks, achieving 55% success in a 3D maze versus 6% for baselines.
An event-centric framework encodes environments as semantic events and retrieves weighted prior maneuvers from a knowledge bank to enable interpretable, physics-aware decision-making for UAVs.
citing papers explorer
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Demo-JEPA: Joint-Embedding Predictive Architecture for One-shot Cross-Embodiment Imitation
Demo-JEPA enables one-shot cross-embodiment imitation by mapping visual demonstrations to shared latent future trajectories that serve as subgoals for the target agent's own forward dynamics planning.
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Zero-shot Imitation Learning by Latent Topology Mapping
ZALT learns latent hub states and hub-to-hub dynamics from demonstrations to plan zero-shot solutions for unseen start-goal tasks, achieving 55% success in a 3D maze versus 6% for baselines.
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Event-Centric World Modeling with Memory-Augmented Retrieval for Embodied Decision-Making
An event-centric framework encodes environments as semantic events and retrieves weighted prior maneuvers from a knowledge bank to enable interpretable, physics-aware decision-making for UAVs.