WM-VS: Progress-Aligned World Models for Closed-Loop Visual Servoing

arXiv:2609.20892 · cs.RO, cs.CV · Submitted 2026-09-17 · Read on arXiv

cs.RO, cs.CV

Submitted: 2026-09-17

Updated: 2026-09-17

Terminology

• Alignment — 14× in this paper · explained in 1 episode
• World Model — 14× in this paper · explained in 7 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Accuracy — 4× in this paper · explained in 1 episode
• Consistency — 4× in this paper · explained in 3 episodes
• International Conference on Robotics and Automation — 4× in this paper
• Latent Dynamics — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Decode — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Latent World Models — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Sim-to-Real Transfer — 1× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• Deep ViT Features as Dense Visual Descriptors
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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