Lemon-03
/

DP_PushT_test_Resume

@@ -1,31 +1,18 @@
 ---
 datasets:
   - lerobot/pusht
 library_name: lerobot
 license: apache-2.0
 model_name: diffusion
 pipeline_tag: robotics
 tags:
   - lerobot
   - robotics
   - diffusion
   - pusht
   - imitation-learning
   - benchmark
 ---
@@ -45,189 +32,96 @@ tags:
 > **Summary:** This model demonstrates the capabilities of **Diffusion Policy** on the precision-demanding **Push-T** task. It was trained using the [LeRobot](https://github.com/huggingface/lerobot) framework as part of a thesis research project benchmarking Imitation Learning algorithms.
 - **🧩 Task**: Push-T (Simulated)
 - **🧠 Algorithm**: [Diffusion Policy](https://huggingface.co/papers/2303.04137) (DDPM)
 - **🔄 Training Steps**: 200,000 (Fine-tuned via Resume)
 - **🎓 Author**: Graduate Student, **UESTC** (University of Electronic Science and Technology of China)
 ---
 ## 🔬 Benchmark Results (vs ACT)
 Compared to the ACT baseline (which achieved **0%** success rate in our controlled experiments), this Diffusion Policy model demonstrates significantly better control precision and trajectory stability.
 ### 📊 Evaluation Metrics (50 Episodes)
 | Metric | Value | Comparison to ACT Baseline | Status |
 | :--- | :---: | :--- | :---: |
 | **Success Rate** | **14.0%** | **Significant Improvement** (ACT: 0%) | 🏆 |
 | **Avg Max Reward** | **0.81** | **+58% Higher Precision** (ACT: ~0.51) | 📈 |
 | **Avg Sum Reward** | **130.46** | **+147% More Stable** (ACT: ~52.7) | ✅ |
 > **Note:** The Push-T environment requires **>95% target coverage** for success. An average max reward of `0.81` indicates the policy consistently moves the block very close to the target position, proving strong manipulation capabilities despite the strict success threshold.
 ---
 ## ⚙️ 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 Strategy**:
   - Phase 1: Initial training (100,000 steps) -> Model: `Lemon-03/DP_PushT_test`
   - Phase 2: Resume/Fine-tuning (+100,000 steps) -> Model: `Lemon-03/DP_PushT_test_Resume`
   - **Total**: 200,000 steps
 ---
 ## 🔧 Training Configuration (Reference)
 For reproducibility, here are the key parameters used during the training session:
   - **Batch Size**: 64
   - **Optimizer**: AdamW (`lr=1e-4`)
   - **Scheduler**: Cosine with warmup
   - **Vision**: ResNet18 with random crop (84x84)
   - **Precision**: Mixed Precision (AMP) enabled
 #### Original Training Command (My Resume Mode)
 ```bash
 python -m lerobot.scripts.lerobot_train \
   --policy.type diffusion \
   --env.type pusht \
   --dataset.repo_id lerobot/pusht \
   --wandb.enable true \
   --eval.batch_size 8 \
   --job_name DP_PushT_Resume \
   --policy.repo_id Lemon-03/DP_PushT_test_Resume \
   --policy.pretrained_path outputs/train/2025-12-02/14-33-35_DP_PushT/checkpoints/last/pretrained_model \
   --steps 100000
 ```
 ---
 ## 🚀 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/2025-12-04/14-47-37_DP_PushT_Resume/checkpoints/last/pretrained_model \
   --eval.n_episodes 50 \
   --eval.batch_size 10 \
   --env.type pusht \
   --env.task PushT-v0
 ```
 To evaluate this model locally, run the following command:
 ```bash
 python -m lerobot.scripts.lerobot_eval \
   --policy.type diffusion \
   --policy.pretrained_path Lemon-03/DP_PushT_test_Resume \
   --eval.n_episodes 50 \
   --eval.batch_size 10 \
   --env.type pusht \
   --env.task PushT-v0
 ```
 -----

 ---
 datasets:
   - lerobot/pusht
 library_name: lerobot
 license: apache-2.0
 model_name: diffusion
 pipeline_tag: robotics
 tags:
   - lerobot
   - robotics
   - diffusion
   - pusht
   - imitation-learning
   - benchmark
 ---
 > **Summary:** This model demonstrates the capabilities of **Diffusion Policy** on the precision-demanding **Push-T** task. It was trained using the [LeRobot](https://github.com/huggingface/lerobot) framework as part of a thesis research project benchmarking Imitation Learning algorithms.
 - **🧩 Task**: Push-T (Simulated)
 - **🧠 Algorithm**: [Diffusion Policy](https://huggingface.co/papers/2303.04137) (DDPM)
 - **🔄 Training Steps**: 200,000 (Fine-tuned via Resume)
 - **🎓 Author**: Graduate Student, **UESTC** (University of Electronic Science and Technology of China)
 ---
 ## 🔬 Benchmark Results (vs ACT)
 Compared to the ACT baseline (which achieved **0%** success rate in our controlled experiments), this Diffusion Policy model demonstrates significantly better control precision and trajectory stability.
 ### 📊 Evaluation Metrics (50 Episodes)
 | Metric | Value | Comparison to ACT Baseline | Status |
 | :--- | :---: | :--- | :---: |
 | **Success Rate** | **14.0%** | **Significant Improvement** (ACT: 0%) | 🏆 |
 | **Avg Max Reward** | **0.81** | **+58% Higher Precision** (ACT: ~0.51) | 📈 |
 | **Avg Sum Reward** | **130.46** | **+147% More Stable** (ACT: ~52.7) | ✅ |
 > **Note:** The Push-T environment requires **>95% target coverage** for success. An average max reward of `0.81` indicates the policy consistently moves the block very close to the target position, proving strong manipulation capabilities despite the strict success threshold.
 ---
 ## ⚙️ 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 Strategy**:
   - Phase 1: Initial training (100,000 steps) -> Model: `Lemon-03/DP_PushT_test`
   - Phase 2: Resume/Fine-tuning (+100,000 steps) -> Model: `Lemon-03/DP_PushT_test_Resume`
   - **Total**: 200,000 steps
 ---
 ## 🔧 Training Configuration (Reference)
 For reproducibility, here are the key parameters used during the training session:
   - **Batch Size**: 64
   - **Optimizer**: AdamW (`lr=1e-4`)
   - **Scheduler**: Cosine with warmup
   - **Vision**: ResNet18 with random crop (84x84)
   - **Precision**: Mixed Precision (AMP) enabled
 #### Original Training Command (My Resume Mode)
 ```bash
 python -m lerobot.scripts.lerobot_train \
   --policy.type diffusion \
   --env.type pusht \
   --dataset.repo_id lerobot/pusht \
   --wandb.enable true \
   --eval.batch_size 8 \
   --job_name DP_PushT_Resume \
   --policy.repo_id Lemon-03/DP_PushT_test_Resume \
   --policy.pretrained_path outputs/train/2025-12-02/14-33-35_DP_PushT/checkpoints/last/pretrained_model \
   --steps 100000
 ```
 ---
 ## 🚀 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/2025-12-04/14-47-37_DP_PushT_Resume/checkpoints/last/pretrained_model \
   --eval.n_episodes 50 \
   --eval.batch_size 10 \
   --env.type pusht \
   --env.task PushT-v0
 ```
 To evaluate this model locally, run the following command:
 ```bash
 python -m lerobot.scripts.lerobot_eval \
   --policy.type diffusion \
   --policy.pretrained_path Lemon-03/DP_PushT_test_Resume \
   --eval.n_episodes 50 \
   --eval.batch_size 10 \
   --env.type pusht \
   --env.task PushT-v0
 ```
 -----