Add robotics pipeline tag and improve model card

README.md

@@ -1,28 +1,30 @@
 ---
-license: apache-2.0
 base_model: Qwen/Qwen3-VL-4B-Instruct
-tags:
-  - reward model
-  - robot learning
-  - foundation models
 library_name: transformers
+license: apache-2.0
+pipeline_tag: robotics
+tags:
+- reward model
+- robot learning
+- foundation models
 ---
 
 # Robometer 4B
 
-**Paper:** [arXiv](https://arxiv.org/abs/2603.02115)
+[**Project Page**](https://robometer.github.io/) | [**Paper**](https://arxiv.org/abs/2603.02115) | [**GitHub**](https://github.com/robometer/robometer)
 
-**Robometer** is a general-purpose vision-language reward model for robotics. It is trained on [RBM-1M](https://huggingface.co/datasets/) with **Qwen3-VL-4B** to predict **per-frame progress**, **per-frame success**, and **trajectory preferences** from rollout videos. The model combines (1) frame-level progress supervision on expert data and (2) trajectory-comparison preference supervision, so it can learn from both successful and failed rollouts and generalize across diverse robot embodiments and tasks.
+**Robometer** is a general-purpose vision-language reward model for robotics. It is trained on [RBM-1M](https://huggingface.co/datasets/) (a dataset of over one million trajectories) using a **Qwen3-VL-4B** backbone to predict **per-frame progress**, **per-frame success**, and **trajectory preferences** from rollout videos. 
 
-Given a **task instruction** and a **rollout video** (or frame sequence), the model predicts:
+The model combines frame-level progress supervision on expert data with trajectory-comparison preference supervision, allowing it to learn from both successful and failed rollouts and generalize across diverse robot embodiments and tasks.
 
-- **Per-frame progress** — continuous progress values over time (e.g. 0–1 or binned).
-- **Per-frame success** — success probability (or binary) at each timestep.
-- **Preference / ranking** — which of two trajectories is better for the task.
+Given a **task instruction** and a **rollout video** (or frame sequence), the model predicts:
+- **Per-frame progress**: Continuous progress values over time (e.g., 0–1 or binned).
+- **Per-frame success**: Success probability (or binary) at each timestep.
+- **Preference / ranking**: Which of two trajectories is better for the task.
 
 ### Usage
 
-For full setup, example scripts, and configs, see the **GitHub repo**: [github.com/robometer/robometer](https://github.com/robometer/robometer).
+For full setup and configurations, see the [GitHub repository](https://github.com/robometer/robometer).
 
 **Option 1 — Run the model locally** (loads this checkpoint from Hugging Face):
 
@@ -56,10 +58,10 @@ uv run python scripts/example_inference.py \
 If you use this model, please cite:
 
 ```bibtex
-@article{liang2026robometer,
+@inproceedings{liang2026robometer,
   title     = {Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons},
-  author={Anthony Liang and Yigit Korkmaz and Jiahui Zhang and Minyoung Hwang and Abrar Anwar and Sidhant Kaushik and Aditya Shah and Alex S. Huang and Luke Zettlemoyer and Dieter Fox and Yu Xiang and Anqi Li and Andreea Bobu and Abhishek Gupta and Stephen Tu and Erdem Biyik and Jesse Zhang},
-  year={2026},
-  journal   = {arXiv preprint arXiv:2603.02115}
+  author    = {Anthony Liang and Yigit Korkmaz and Jiahui Zhang and Minyoung Hwang and Abrar Anwar and Sidhant Kaushik and Aditya Shah and Alex S. Huang and Luke Zettlemoyer and Dieter Fox and Yu Xiang and Anqi Li and Andreea Bobu and Abhishek Gupta and Stephen Tu and Erdem Biyik and Jesse Zhang},
+  year      = {2026},
+  booktitle = {Robotics: Science and Systems 2026},
 }
-```
+```