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+assets/videos/flow-demo-web-2x/install_phone.mp4 filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -1,3 +1,60 @@
----
-license: cc-by-nc-4.0
----
+# UnifoLM-ER-Flow
+
+[Project Page](https://unigen-x.github.io/unifolm-wla.github.io/)
+
+UnifoLM-ER-Flow extends UnifoLM-ER-1 with interaction-centric world modeling and discrete action learning. It jointly aligns visual observations, language conditions, predicted future dynamic regions, and robot actions within a single vision-language model.
+
+## Dynamic Region Prediction
+
+We use optical flow to extract dynamic regions that capture future scene changes, then train a VQ-VAE to encode them into fixed-length sequences of discrete tokens. Conditioned on the current image and a task description or action, the VLM directly predicts mask tokens for future dynamic regions, focusing on interaction subjects and the scene changes they induce to enable interaction-centric world modeling.
+
+![Dynamic region prediction pipeline](assets/dynamic-region-prediction-pipeline.png)
+
+
+### Demonstrations
+
+<table>
+  <tr>
+    <td width="50%" align="center">
+      <video src="assets/videos/flow-demo-web-2x/clean_table.mp4" controls muted loop playsinline width="100%"></video>
+      <br><strong>01 · Clean table</strong>
+    </td>
+    <td width="50%" align="center">
+      <video src="assets/videos/flow-demo-web-2x/fold_towel.mp4" controls muted loop playsinline width="100%"></video>
+      <br><strong>02 · Fold towel</strong>
+    </td>
+  </tr>
+  <tr>
+    <td width="50%" align="center">
+      <video src="assets/videos/flow-demo-web-2x/install_phone.mp4" controls muted loop playsinline width="100%"></video>
+      <br><strong>03 · Packaging phone</strong>
+    </td>
+    <td width="50%" align="center">
+      <video src="assets/videos/flow-demo-web-2x/place_plates.mp4" controls muted loop playsinline width="100%"></video>
+      <br><strong>04 · Place plates</strong>
+    </td>
+  </tr>
+</table>
+
+## Discrete Action Learning
+
+We partition the unified action space into three components: end-effector (EEF) poses, end-effector joints, and lower-body joints. A separate residual vector quantization (RVQ) model is trained for each component to discretize action sequences. Building on UnifoLM-ER-1, we introduce discrete action tokens and mask tokens for future dynamic regions, jointly aligning visual, language, and action representations within a single VLM to obtain the UnifoLM-ER-Flow model.
+
+### Action Encoding Pipeline
+
+Each continuous motion component is encoded independently with its own RVQ model. The resulting token sequences share the same timesteps and are synchronously fed into the VLM.
+
+![Discrete action learning pipeline](assets/discrete-action-learning-pipeline.png)
+
+### Unified Training Signals
+
+| Signal | Representation | Role |
+|---|---|---|
+| Visual observations | Image tokens | Describe the current environment |
+| Task or action condition | Language or action tokens | Specify the intended interaction |
+| Future dynamic regions | Mask tokens | Represent interaction-induced scene changes |
+| EEF poses | Discrete EEF tokens | Encode end-effector trajectories |
+| End-effector joints | Discrete hand tokens | Encode gripper or dexterous-hand motion |
+| Lower-body joints | Discrete lower-body tokens | Encode whole-body coordination |
+
+Together, these signals connect spatial understanding, future-change prediction, and action representation in a unified multimodal model.

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chat_template.jinja

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+{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system
+You are a helpful assistant.<|im_end|>
+{% endif %}<|im_start|>{{ message['role'] }}
+{% if message['content'] is string %}{{ message['content'] }}<|im_end|>
+{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>
+{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant
+{% endif %}

config.json

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+}

generation_config.json

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