Add model card for APT

README.md

@@ -0,0 +1,45 @@
+---
+license: apache-2.0
+pipeline_tag: robotics
+---
+
+# APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
+
+This repository contains the checkpoints for APT, a two-stage training method for Vision-Language-Action (VLA) models that emphasizes Action Expert Pretraining to improve generalization to out-of-distribution (OOD) instructions.
+
+* **Paper**: [APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies](https://huggingface.co/papers/2606.12366)
+* **Project Page**: [https://xukechun.github.io/papers/APT/](https://xukechun.github.io/papers/APT/)
+* **Code/Github**: [https://github.com/xukechun/APT](https://github.com/xukechun/APT)
+
+## Method Overview
+
+APT factorizes the VLA policy into a Vision-Action (VA) prior and a language-conditioned VLA likelihood. In Stage 1, the action expert is pretrained as a VA prior on vision-action pairs from a frozen VLM to bypass language imbalance. In Stage 2, language tokens are injected through a gated fusion mechanism that integrates VLM features while preserving the learned visuomotor prior.
+
+## Sample Usage
+
+For local inference, you can instantiate the planner directly:
+
+```python
+from apt.infer.planner import TrajPlanner
+
+planner = TrajPlanner(
+    ckpt_path="checkpoints/APT/ft_vla/ckpt_latest.pt",
+    device="cuda:0",
+    ensemble=4,
+    use_ema=False,
+)
+planner.set_prompt("Pick up the grape and place it on the pink box.")
+planner.add_obs_frame(obs_frame)
+actions = planner.get_action()
+```
+
+## Citation
+
+```bibtex
+@article{xu2026apt,
+      title={APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies},
+      author={Xu, Kechun and Zhu, Zhenjie and Chen, Anzhe and Xiong, Rong and Wang, Yue},
+      journal={arXiv preprint arXiv:2606.12366},
+      year={2026}
+    }
+```