| 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). |