🗞️ News
2026-04-05: 🤗 We released Robo-Dopamine-GRM-2.0-4B-Preview model.2026-03-05: 🤗 We released Robo-Dopamine-GRM-2.0-8B-Preview model. More General, More Powerful!!!2026-03-02: 🤗 We released Robo-Dopamine-GRM-8B model2026-02-22: 🔥🔥🔥 Robo-Dopamine gets accepted to CVPR 2026! See you in Denver, Colorado, USA!2026-02-10: ⚡ We released data generation pipeline and finetune codes. Try to finetune with your own data.2026-01-26: 🔍 We released Robo-Dopamine-Bench benchmark and evaluation codes.2026-01-08: 🤗 We released Robo-Dopamine-GRM-3B model and inference codes.2025-12-30: ✨ Codes, Dataset and Weights are coming soon! Stay tuned for updates.2025-12-30: 🔥 We released our Project Page of Robo-Dopamine.
🤖 Overview
Robo-Dopamine is composed of two core components: (a) Dopamine-Reward Modeling Method -- At the heart of our reward modeling is to build the General Reward Model (GRM), a vision-language model that is prompted with a task description and conditioned on multi-view images of initial, goal, "BEFORE," and "AFTER" states to predict a relative progress or regress hop. To ensure a stable and accurate signal, we employ Multi-Perspective Progress Fusion, which combines incremental, forward-anchored, and backward-anchored predictions into a final fused reward. And (b) Dopamine-RL Training Framework -- The Dopamine-RL framework first adapts the pre-trained GRM to a novel task using a single demonstration, i.e., One-Shot GRM Adaptation. Subsequently, it uses a theoretically-sound Policy-Invariant Reward Shaping method to convert the GRM's dense output into a reward signal that accelerates learning without altering the optimal policy. This approach is universally compatible with a wide range of RL algorithms.
🛠️ Setup
# clone repo.
git clone https://github.com/FlagOpen/Robo-Dopamine.git
cd Robo-Dopamine
# build conda env.
conda create -n robo-dopamine python=3.10
conda activate robo-dopamine
pip install -r requirements.txt
💡 Simple Inference
import os
from examples.inference import GRMInference
model = GRMInference("tanhuajie2001/Robo-Dopamine-GRM-2.0-4B-Preview")
TASK_INSTRUCTION = "organize the table"
BASE_DEMO_PATH = "./examples/demo_table"
OUTPUT_ROOT = "./results"
## Note: If no target/goal image is provided,
## please replace `GOAL_IMAGE_PATH` with the blank image "./examples/demo_table/blank_goal.png".
GOAL_IMAGE_PATH = "./examples/demo_table/goal_image.png" # "./examples/demo_table/blank_goal.png"
# select prediction model: Forward-Mode, Incremental-Mode or Backward-Mode
PREDICTION_MODE = "forward" # "incremental" or "backward"
# multi-view usage:
output_dir = model.run_pipeline(
cam_high_path = os.path.join(BASE_DEMO_PATH, "cam_high.mp4"),
cam_left_path = os.path.join(BASE_DEMO_PATH, "cam_left_wrist.mp4"),
cam_right_path = os.path.join(BASE_DEMO_PATH, "cam_right_wrist.mp4"),
out_root = OUTPUT_ROOT,
task = TASK_INSTRUCTION,
frame_interval = 10, # modify frame_interval as desired, but it shouldn't be set too small if using 'incremental'.
batch_size = 1, # please increase batch_size > 1, if you have enough GPU memory.
goal_image = GOAL_IMAGE_PATH,
eval_mode = PREDICTION_MODE,
visualize = True
)
print(f"Episode ({BASE_DEMO_PATH}) processed with multi-view {PREDICTION_MODE}-mode. Output at: {output_dir}")
# single-view usage:
output_dir = model.run_pipeline(
cam_high_path = os.path.join(BASE_DEMO_PATH, "cam_high.mp4"),
cam_left_path = os.path.join(BASE_DEMO_PATH, "cam_high.mp4"), # repeat cam_high
cam_right_path = os.path.join(BASE_DEMO_PATH, "cam_high.mp4"), # repeat cam_high
out_root = OUTPUT_ROOT,
task = TASK_INSTRUCTION,
frame_interval = 10, # modify frame_interval as desired, but it shouldn't be set too small if using 'incremental'.
batch_size = 1, # please increase batch_size > 1, if you have enough GPU memory.
goal_image = GOAL_IMAGE_PATH,
eval_mode = PREDICTION_MODE,
visualize = True
)
print(f"Episode ({BASE_DEMO_PATH}) processed with single-view {PREDICTION_MODE}-mode. Output at: {output_dir}")
📑 Citation
If you find our work helpful, feel free to cite it:
@article{tan2025robo,
title={Robo-Dopamine: General Process Reward Modeling for High-Precision Robotic Manipulation},
author={Tan, Huajie and Chen, Sixiang and Xu, Yijie and Wang, Zixiao and Ji, Yuheng and Chi, Cheng and Lyu, Yaoxu and Zhao, Zhongxia and Chen, Xiansheng and Co, Peterson and others},
journal={arXiv preprint arXiv:2512.23703},
year={2025}
}