---
license: mit
pipeline_tag: robotics
tags:
- robotics
- imitation-learning
- diffusion-policy
---

# SeedPolicy: Horizon Scaling via Self-Evolving Diffusion Policy for Robot Manipulation

This repository contains the pre-trained model checkpoints for the tasks highlighted in the paper **SeedPolicy: Horizon Scaling via Self-Evolving Diffusion Policy for Robot Manipulation**.

## 📄 Associated Paper & Links

* **Hugging Face Paper Page:** [https://huggingface.co/papers/2603.05117](https://huggingface.co/papers/2603.05117)
* **GitHub Repository:** [https://github.com/Youqiang-Gui/SeedPolicy](https://github.com/Youqiang-Gui/SeedPolicy)

## 💡 Overview

SeedPolicy introduces **Self-Evolving Gated Attention (SEGA)**, a temporal module that maintains a time-evolving latent state via gated attention. This enables efficient recurrent updates that compress long-horizon observations into a fixed-size representation while filtering irrelevant temporal information. Integrating SEGA into Diffusion Policy (DP) resolves temporal modeling bottlenecks and enables scalable horizon extension for long-horizon robotic manipulation tasks.

## 🛠️ Usage

Detailed installation and data generation instructions are available in the [official GitHub repository](https://github.com/Youqiang-Gui/SeedPolicy).

### 1. Train Policy
```bash
bash train.sh ${task_name} ${task_config} ${expert_data_num} ${seed} ${action_dim} ${gpu_id} ${config_name}

# Example:
# bash train.sh beat_block_hammer demo_clean 50 0 14 0 train_diffusion_transformer_hybrid_workspace
```

### 2. Eval Policy
```bash
bash eval.sh ${task_name} ${task_config} ${ckpt_setting} ${expert_data_num} ${seed} ${gpu_id} ${config_name} ${timestamp}

# Example 1: Standard Evaluation
# bash eval.sh beat_block_hammer demo_clean demo_clean 50 0 0 train_diffusion_transformer_hybrid_workspace "'20260106-143723'"

# Example 2: Generalization Evaluation
# To evaluate a policy trained on the `demo_clean` setting and tested on the `demo_randomized` setting, run:
# bash eval.sh beat_block_hammer demo_randomized demo_clean 50 0 0 train_diffusion_transformer_hybrid_workspace "'20260106-143723'"
```

The evaluation results, including videos, will be saved in the `eval_result` directory under the project root.

## 😺 Acknowledgements
Our code is generally built upon: [Diffusion Policy](https://github.com/real-stanford/diffusion_policy) and [RoboTwin 2.0](https://github.com/RoboTwin-Platform/RoboTwin). Specifically, the implementation of our state update code references [CUT3R](https://github.com/CUT3R/CUT3R) and [TTT3R](https://github.com/Inception3D/TTT3R).