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DP_PushT_test_Resume

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Safetensors
diffusion
pusht
imitation-learning
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  # 🦾 Diffusion Policy for Push-T (200k Steps)
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  [![LeRobot](https://img.shields.io/badge/Library-LeRobot-yellow)](https://github.com/huggingface/lerobot)
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  [![Task](https://img.shields.io/badge/Task-Push--T-blue)](https://huggingface.co/datasets/lerobot/pusht)
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  [![UESTC](https://img.shields.io/badge/Author-UESTC_Graduate-red)](https://www.uestc.edu.cn/)
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  [![License](https://img.shields.io/badge/License-Apache_2.0-green)](https://www.apache.org/licenses/LICENSE-2.0)
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  > **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.
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  - **🧩 Task**: Push-T (Simulated)
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  - **🧠 Algorithm**: [Diffusion Policy](https://huggingface.co/papers/2303.04137) (DDPM)
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  - **🎓 Author**: Graduate Student, **UESTC** (University of Electronic Science and Technology of China)
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  ## 🔬 Benchmark Results (vs ACT)
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  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.
 
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  # 🦾 Diffusion Policy for Push-T (200k Steps)
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  [![LeRobot](https://img.shields.io/badge/Library-LeRobot-yellow)](https://github.com/huggingface/lerobot)
 
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  [![Task](https://img.shields.io/badge/Task-Push--T-blue)](https://huggingface.co/datasets/lerobot/pusht)
 
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  [![UESTC](https://img.shields.io/badge/Author-UESTC_Graduate-red)](https://www.uestc.edu.cn/)
 
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  [![License](https://img.shields.io/badge/License-Apache_2.0-green)](https://www.apache.org/licenses/LICENSE-2.0)
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  > **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.
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  - **🧩 Task**: Push-T (Simulated)
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  - **🧠 Algorithm**: [Diffusion Policy](https://huggingface.co/papers/2303.04137) (DDPM)
 
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  - **🎓 Author**: Graduate Student, **UESTC** (University of Electronic Science and Technology of China)
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  ## 🔬 Benchmark Results (vs ACT)
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  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.