Update README.md

README.md

@@ -2,26 +2,67 @@
 license: apache-2.0
 base_model: Qwen/Qwen3-VL-4B-Instruct
 tags:
-- reward_model
-- rfm
-- preference_comparisons
+  - reward model
+  - robot learning
+  - foundation models
 library_name: transformers
 ---
 
-# aliangdw/libero_ablation_prog_pref_with_fail_lora_ft_4frames
+# Robometer 4B LIBERO
 
-## Model Details
+**Paper:** [arXiv (Coming Soon)](https://arxiv.org/)
 
-- **Base Model**: Qwen/Qwen3-VL-4B-Instruct
-- **Model Type**: qwen3_vl
+**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.
 
-## Training Run
+Given a **task instruction** and a **rollout video** (or frame sequence), the model predicts:
 
-- **Wandb Run**: [libero_ablation_prog_pref_with_fail_lora_ft_4frames_2000steps](https://wandb.ai/clvr/rfm/runs/f4yifl7b)
-- **Wandb ID**: `f4yifl7b`
-- **Project**: rfm
-- **Notes**: libero prog pref with fail lora ft
+- **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.
+
+This model is trained on LIBERO-10/Spatial/Object/Goal
+
+### Usage
+
+For full setup, example scripts, and configs, see the **GitHub repo**: [github.com/aliang8/robometer](https://github.com/aliang8/robometer).
+
+**Option 1 — Run the model locally** (loads this checkpoint from Hugging Face):
+
+```bash
+uv run python scripts/example_inference_local.py \
+  --model-path aliangdw/Robometer-4B-LIBERO \
+  --video /path/to/video.mp4 \
+  --task "your task description"
+```
+
+**Option 2 — Use the evaluation server** (start server, then run client):
+
+```bash
+# Start server
+uv run python robometer/evals/eval_server.py \
+  --config-path=robometer/configs \
+  --config-name=eval_config_server \
+  model_path=aliangdw/Robometer-4B-LIBERO \
+  server_url=0.0.0.0 \
+  server_port=8000
+
+# Client (no robometer dependency)
+uv run python scripts/example_inference.py \
+  --eval-server-url http://localhost:8000 \
+  --video /path/to/video.mp4 \
+  --task "your task description"
+```
 
 ## Citation
 
 If you use this model, please cite:
+
+```bibtex
+@misc{robometer2025,
+  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 B{\i}y{\i}k† and Jesse Zhang†},
+  year={2025},
+  url={https://github.com/aliang8/reward_fm},
+  note={arXiv coming soon}
+}
+```