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---
datasets:
- lerobot/pusht
library_name: lerobot
license: apache-2.0
model_name: diffusion
pipeline_tag: robotics
tags:
- lerobot
- robotics
- diffusion
- pusht
- imitation-learning
- phase-1
---
# 🦾 Diffusion Policy for Push-T (Phase 1: 100k Steps)
[](https://github.com/huggingface/lerobot)
[](https://huggingface.co/datasets/lerobot/pusht)
[](https://www.uestc.edu.cn/)
[](https://huggingface.co/Lemon-03/DP_PushT_test)
> **Summary:** This model represents the **initial training phase (0 - 100k steps)** of a Diffusion Policy on the Push-T task. It serves as the pre-trained foundation for further fine-tuning. While it demonstrates strong trajectory learning capabilities, it has not yet fully converged to high success rates.
- **🧩 Task**: Push-T (Simulated)
- **🧠 Algorithm**: [Diffusion Policy](https://huggingface.co/papers/2303.04137) (DDPM)
- **🔄 Training Steps**: 100,000 (Initial Phase)
- **🎓 Author**: Graduate Student, **UESTC** (University of Electronic Science and Technology of China)
---
## ⚠️ Note on Performance & Fine-tuning
This checkpoint represents the **intermediate state** of our research.
While it achieves high movement precision (**Avg Max Reward: 0.71**), the strict success threshold of the Push-T task results in a lower success rate at this stage.
### 🚀 **Upgrade Available:**
We performed **Resume Training (Fine-tuning)** based on this checkpoint for another 100k steps, achieving significantly better results.
👉 **Check out the final model here:** [**Lemon-03/DP_PushT_test_Resume**](https://huggingface.co/Lemon-03/DP_PushT_test_Resume)
---
## 🔬 Benchmark Results (Phase 1)
Evaluated on **50 episodes** in the `Push-T` environment using LeRobot.
| Metric | Value | Status |
| :--- | :---: | :---: |
| **Success Rate** | **4.0%** | 🚧 (Under-trained) |
| **Avg Max Reward** | **0.71** | 📈 (High Precision) |
| **Avg Sum Reward** | **115.03** | ✅ (Good Trajectory) |
> **Analysis:** The model has successfully learned the multimodal distribution of the demonstration data and can push the T-block close to the target (Reward 0.71). However, it lacks the final fine-grained adjustment capabilities required for the >95% overlap success criteria. This motivated the subsequent **Phase 2 (Resume Training)**.
---
## ⚙️ Model Details
| Parameter | Description |
| :--- | :--- |
| **Architecture** | ResNet18 (Vision Backbone) + U-Net (Diffusion Head) |
| **Prediction Horizon** | 16 steps |
| **Observation History** | 2 steps |
| **Action Steps** | 8 steps |
---
## 🔧 Training Configuration (Reference)
For reproducibility, here are the key parameters used during this initial training session:
- **Batch Size**: 8 (Effective)
- **Optimizer**: AdamW (`lr=1e-4`)
- **Scheduler**: Cosine with warmup
- **Vision**: ResNet18 with random crop (84x84)
#### Original Training Command (My Training Mode)
```bash
python -m lerobot.scripts.lerobot_train \
--policy.type diffusion \
--env.type pusht \
--dataset.repo_id lerobot/pusht \
--wandb.enable true \
--job_name DP_PushT \
--policy.repo_id Lemon-03/DP_PushT_test \
--eval.batch_size 8
````
-----
## 🚀 Evaluate (My Evaluation Mode)
Run the following command in your terminal to evaluate the model for 50 episodes and save the visualization videos:
```bash
python -m lerobot.scripts.lerobot_eval \
--policy.type diffusion \
--policy.pretrained_path outputs/train/2025-12-02/14-33-35_DP_PushT/checkpoints/last/pretrained_model \
--eval.n_episodes 50 \
--eval.batch_size 10 \
--env.type pusht \
--env.task PushT-v0
```
You can evaluate this checkpoint to reproduce the Phase 1 results:
```bash
python -m lerobot.scripts.lerobot_eval \
--policy.type diffusion \
--policy.pretrained_path Lemon-03/DP_PushT_test \
--eval.n_episodes 50 \
--eval.batch_size 10 \
--env.type pusht \
--env.task PushT-v0
``` |