add short model card

README.md

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+---
+license: mit
+pipeline_tag: robotics
+tags:
+- worldmodel
+---
+Ctrl-World is designed for policy-in-the-loop rollouts with generalist robot policies. It generates joint multi-view predictions (including wrist views), enforces fine-grained action control via frame-level conditioning, and sustains coherent long-horizon dynamics through pose-conditioned memory retrieval. Together, these components enable (1) accurate evaluation of policy instruction-following ability via imagination, and (2) targeted policy improvement on previously unseen instructions.
+
+**Model architecture**
+
+Ctrl-World is initialized from a pretrained video diffusion model and adapted into a controllable, temporally consistent world model with: (1) Multi-view input and joint prediction for unified information understanding. (2) Memory retrieval mechanism, which adds sparse history frames in context and project pose information into each frame via frame-level cross-attention, re-anchoring predictions to similar past states. (3) Frame-level action conditioning to better align high-frequency action with visual dynamics.
+
+**Acknowledgement**
+
+Ctrl-World is developed from the opensourced video foundation model Stable-Video-Diffusion. The VLA model used in this repo is from openpi. We thank the authors for their efforts!
+
+More information here: https://github.com/Robert-gyj/Ctrl-World