Achieve What You Imagined: Learning to Align Actions with Visual Plans
cs.RO, cs.AI, cs.LG
Submitted: 2026-09-27
Updated: 2026-09-27
Project page: https://imagine-to-achieve.github.io
Terminology
Sources
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- Turning Video Models into Generalist Robot Policies
- World Action Models are Zero-shot Policies
- V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
- LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
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