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tanhuajie2001/
Robo-Dopamine-GRM-3B

Name: Robo-Dopamine-GRM-3B
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Robo-Dopamine: General Process Reward Modeling for High-Precision Robotic Manipulation

Joy is dopamine’s handiwork—whether in humans or in robotics.

arXiv

Dataset

🗞️ News

2026-03-02: 🤗 We released Robo-Dopamine-GRM-8B model
2026-02-22: 🔥🔥🔥 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.

Logo

🛠️ Setup

bash

# 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

1. Example for GRM Incremental-Mode

python

import os
from examples.inference import GRMInference

model = GRMInference("tanhuajie2001/Robo-Dopamine-GRM-3B")

TASK_INSTRUCTION = "organize the table"
BASE_DEMO_PATH = "./examples/demo_table"
GOAL_IMAGE_PATH = "./examples/demo_table/goal_image.png" 
OUTPUT_ROOT = "./results"

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 = 30,
    batch_size     = 1,
    goal_image     = GOAL_IMAGE_PATH,
    eval_mode      = "incremental",
    visualize      = True
)

print(f"Episode ({BASE_DEMO_PATH}) processed with Incremental-Mode. Output at: {output_dir}")

2. Example for GRM Forward-Mode

python

import os
from examples.inference import GRMInference

model = GRMInference("tanhuajie2001/Robo-Dopamine-GRM-3B")

TASK_INSTRUCTION = "organize the table"
BASE_DEMO_PATH = "./examples/demo_table"
GOAL_IMAGE_PATH = "./examples/demo_table/goal_image.png" 


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 = 30,
    batch_size     = 1,
    goal_image     = GOAL_IMAGE_PATH,
    eval_mode      = "forward",
    visualize      = True
)

print(f"Episode ({BASE_DEMO_PATH}) processed with Forward-Mode. Output at: {output_dir}")

3. Example for GRM Backward-Mode

python

import os
from examples.inference import GRMInference

model = GRMInference("tanhuajie2001/Robo-Dopamine-GRM-3B")

TASK_INSTRUCTION = "organize the table"
BASE_DEMO_PATH = "./examples/demo_table"
GOAL_IMAGE_PATH = "./examples/demo_table/goal_image.png" 
OUTPUT_ROOT = "./results"

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 = 30,
    batch_size     = 1,
    goal_image     = GOAL_IMAGE_PATH,
    eval_mode      = "backward",
    visualize      = True
)

print(f"Episode ({BASE_DEMO_PATH}) processed with Backward-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}
}