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README.md

@@ -1,3 +1,174 @@
 ---
 license: apache-2.0
+language:
+- en
+library_name: pytorch
+model_name: Copper-Policy
+tags:
+- robotics
+- robot-manipulation
+- world-action-model
+- libero
+- libero-plus
+- robotwin
+- arxiv:2609.32779
 ---
+
+<div align="center">
+
+<h1><img src="assets/copper.png" width="32" height="32" align="absmiddle" alt="Copper-Policy logo"> Copper-Policy</h1>
+
+### Focus on the Representation for Robust Robot Manipulation
+
+[English](README.md) | [中文](README_zh.md)
+
+[📄 Paper](https://arxiv.org/abs/2609.32779) · [🌐 Project Page](https://zexinfeng-cn.github.io/works/copper-policy/) · [💻 Inference Code](https://github.com/Mark4551124015/Copper-Policy)
+
+</div>
+
+![Copper-Policy overview](assets/teaser.png)
+
+## ✨ Model description
+
+Copper-Policy jointly learns a compact world representation and a robot policy. Task-conditioned future embedding prediction shapes the representation, while current-frame visual features preserve spatial detail for action execution.
+
+This repository contains two inference checkpoints for the 2B-parameter policy:
+
+| Checkpoint | Benchmarks | RGB views | Action dimension | Proprio dimension | Action horizon |
+| --- | --- | :---: | :---: | :---: | :---: |
+| `libero/policy.pt` | LIBERO / LIBERO-Plus | 2 | 7 | 8 | 32 |
+| `robotwin/policy.pt` | RoboTwin clean / randomized | 3 | 14 | 14 | 32 |
+
+**LIBERO-Plus uses the LIBERO checkpoint.** Clean and randomized RoboTwin evaluations use the same RoboTwin checkpoint.
+
+Inference code is available in [Copper-Policy](https://github.com/Mark4551124015/Copper-Policy). **Training code — coming soon.**
+
+## 📦 Files
+
+```text
+.
+├── README.md
+├── README_zh.md
+├── LICENSE
+├── .gitattributes
+├── SHA256SUMS
+├── release_manifest.json
+├── assets/
+├── libero/
+│   ├── policy.pt
+│   └── policy.json
+└── robotwin/
+    ├── policy.pt
+    └── policy.json
+```
+
+Each checkpoint is approximately 4.06 GB; both together require approximately 8.12 GB. `policy.json` describes each model, and `release_manifest.json` lists both checkpoints. `SHA256SUMS` contains their SHA-256 checksums.
+
+The `.pt` files are PyTorch checkpoint dictionaries with these top-level keys:
+
+```text
+format_version
+state_dict
+model_config
+config_overrides
+extra
+```
+
+They include policy weights, architecture and inference settings, normalization statistics, and legacy metadata required for compatibility. EMA compactor weights and future-target metadata are retained. They contain no optimizer state, learning rate, epoch, training batch-size or gradient-accumulation settings, and no training dataset paths.
+
+These are custom Copper-Policy checkpoints; load them with the linked inference code.
+
+## 🚀 Download and evaluate
+
+### 1. Install the inference code
+
+```bash
+git clone https://github.com/Mark4551124015/Copper-Policy.git
+cd Copper-Policy
+uv sync --frozen --all-extras --inexact
+```
+
+Follow the GitHub [installation instructions](https://github.com/Mark4551124015/Copper-Policy#-installation) to prepare simulator dependencies and assets. Linux x86_64, NVIDIA GPUs, and ffmpeg are required. Our tested environment uses Python 3.10.16 and PyTorch 2.9.0 / CUDA 13.0; adapt the PyTorch/CUDA versions, package index, toolkit and GPU architecture to your hardware and driver, following the code repository instructions.
+
+### 2. Download policy checkpoints and encoders
+
+Set `COPPER_MODEL_REPO` to the Hugging Face repository hosting this card:
+
+```bash
+export COPPER_MODEL_REPO="Mark455/Copper-Policy"
+uv run --frozen --all-extras --no-sync hf download "$COPPER_MODEL_REPO" \
+  libero/policy.pt robotwin/policy.pt SHA256SUMS \
+  --local-dir pretrained_weights/copper_policy
+
+uv run --frozen --all-extras --no-sync python -m tools.download_weights all --yes
+```
+
+Download only `libero/policy.pt` or `robotwin/policy.pt` if you need one benchmark family. Both policies use these public encoder presets:
+
+| Component | Preset / source |
+| --- | --- |
+| World encoder | [V-JEPA 2.1](https://github.com/facebookresearch/vjepa2), `vjepa2_1_vit_large_384` |
+| Spatial vision encoder | [DINOv2](https://huggingface.co/facebook/dinov2-with-registers-large) |
+| Text encoder + tokenizer | [Wan 2.2](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers) |
+
+No training data or separate Wan transformer initialization weights are needed for inference.
+
+### 3. Evaluate
+
+```bash
+uv run --frozen --all-extras --no-sync bash test_libero.sh --all-tasks
+uv run --frozen --all-extras --no-sync bash test_libero_plus.sh --all-tasks
+uv run --frozen --all-extras --no-sync bash test_robotwin_clean.sh --all-tasks
+uv run --frozen --all-extras --no-sync bash test_robotwin_rand.sh --all-tasks
+```
+
+| Inference setting | LIBERO / LIBERO-Plus | RoboTwin |
+| --- | :---: | :---: |
+| Denoising steps | 10 | 10 |
+| Replan steps | 10 | 24 |
+| Seed | 7 | 0 |
+| Compilation / rollout videos | Enabled | Enabled |
+
+By default, evaluations use eight GPUs and keep one model resident per GPU across tasks. Set `EVAL_GPU_IDS=0` for a single GPU. Task scores, logs and rollout videos are saved under `outputs/`.
+
+<details>
+<summary><b>Note: LIBERO language mapping</b></summary>
+
+Language instructions in the LIBERO training dataset can differ from those provided by the evaluation benchmarks. The inference code maps exact plaintext benchmark prompts to training instructions. **LIBERO-Plus language perturbations are not mapped**; unmatched prompts also retain the benchmark text.
+
+</details>
+
+## 📊 Reported results
+
+| Benchmark | Success rate |
+| --- | :---: |
+| LIBERO | 97.25% |
+| LIBERO-Plus | 80.85% |
+| RoboTwin clean | 70.84% |
+| RoboTwin randomized | 12.98% |
+
+These are results reported in the [paper and project page](https://zexinfeng-cn.github.io/works/copper-policy/#main-result). Each evaluation run records its own measured scores.
+
+## Intended use and limitations
+
+These checkpoints are intended for research on simulated robot manipulation and policy evaluation on the listed benchmarks. They are specialized to the observation and action conventions of their benchmark family. Real-robot deployment requires a compatible robot-specific checkpoint and integration.
+
+## 📚 Citation
+
+```bibtex
+@misc{feng2026copperpolicyfocusrepresentationrobust,
+  title={Copper-Policy: Focus on the Representation for Robust Robot Manipulation},
+  author={Zexin Feng and Yixu Feng and Lingyu Xiao and Shang Su and Kexin Zheng and Chang Xu and Mengkai Shi and Shuo Feng and Xintao Yan},
+  year={2026},
+  eprint={2609.32779},
+  archivePrefix={arXiv},
+  primaryClass={cs.RO},
+  url={https://arxiv.org/abs/2609.32779}
+}
+```
+
+## 🙏 Acknowledgements and license
+
+The implementation builds on [FastWAM](https://github.com/yuantianyuan01/FastWAM) and [LingBot-VA](https://github.com/robbyant/lingbot-va). Dataset conversion to LMDB uses our [lerobot-tools](https://github.com/Mark4551124015/lerobot-tools). Codex helped organize the open-source release.
+
+This release uses the [Apache 2.0 license](LICENSE). External encoders, simulators and assets retain their respective licenses.

README_zh.md

@@ -0,0 +1,139 @@
+<div align="center">
+
+<h1><img src="assets/copper.png" width="32" height="32" align="absmiddle" alt="Copper-Policy logo"> Copper-Policy</h1>
+
+### Focus on the Representation for Robust Robot Manipulation
+
+[English](README.md) | [中文](README_zh.md)
+
+[📄 论文](https://arxiv.org/abs/2609.32779) · [🌐 项目主页](https://zexinfeng-cn.github.io/works/copper-policy/) · [💻 推理代码](https://github.com/Mark4551124015/Copper-Policy)
+
+</div>
+
+![Copper-Policy overview](assets/teaser.png)
+
+## ✨ 模型介绍
+
+Copper-Policy 联合学习紧凑的世界表征与机器人策略。任务条件下的未来嵌入预测用于学习表征，当前帧的视觉特征保留执行动作所需的空间细节。
+
+本仓库提供两份约 2B 参数的推理模型：
+
+| 文件 | 测评环境 | RGB 视角数 | 动作维度 | 本体状态维度 | 动作预测长度 |
+| --- | --- | :---: | :---: | :---: | :---: |
+| `libero/policy.pt` | LIBERO / LIBERO-Plus | 2 | 7 | 8 | 32 |
+| `robotwin/policy.pt` | RoboTwin clean / randomized | 3 | 14 | 14 | 32 |
+
+**LIBERO 与 LIBERO-Plus 共用同一份权重。RoboTwin clean 与 randomized 共用同一份权重。**
+
+推理代码见 [Copper-Policy](https://github.com/Mark4551124015/Copper-Policy)。**训练代码 coming soon。**
+
+## 📦 文件说明
+
+每份 `policy.pt` 约 4.06 GB，两份合计约 8.12 GB。对应目录中的 `policy.json` 提供模型与推理参数，`release_manifest.json` 汇总两份模型，`SHA256SUMS` 提供 SHA-256 校验值。
+
+Checkpoint 顶层 keys 为：
+
+```text
+format_version
+state_dict
+model_config
+config_overrides
+extra
+```
+
+文件包含策略权重、结构及推理配置、归一化统计和兼容性元数据；保留 EMA compactor 权重与 future-target 元数据。不包含优化器状态、学习率、epoch、训练 batch size、梯度累积配置或训练数据集路径。请使用本项目的推理代码加载。
+
+## 🚀 下载与测评
+
+### 1. 安装推理代码
+
+```bash
+git clone https://github.com/Mark4551124015/Copper-Policy.git
+cd Copper-Policy
+uv sync --frozen --all-extras --inexact
+```
+
+按照 [代码仓库安装说明](https://github.com/Mark4551124015/Copper-Policy#-installation) 准备模拟器依赖和资源。需要 Linux x86_64、NVIDIA GPU 与 ffmpeg。我们验证的环境使用 Python 3.10.16、PyTorch 2.9.0 / CUDA 13.0；请按照代码仓库说明，根据自己的硬件和驱动适配 PyTorch/CUDA 版本、软件源、toolkit 与 GPU 架构。
+
+### 2. 下载策略与编码器
+
+将 `COPPER_MODEL_REPO` 设置为承载本 model card 的 Hugging Face 仓库 ID：
+
+```bash
+export COPPER_MODEL_REPO="Mark455/Copper-Policy"
+uv run --frozen --all-extras --no-sync hf download "$COPPER_MODEL_REPO" \
+  libero/policy.pt robotwin/policy.pt SHA256SUMS \
+  --local-dir pretrained_weights/copper_policy
+
+uv run --frozen --all-extras --no-sync python -m tools.download_weights all --yes
+```
+
+只使用一个测评环境时，可只下载对应的 `policy.pt`。两个模型共用以下公开编码器：
+
+| 组件 | 预设 / 来源 |
+| --- | --- |
+| World encoder | [V-JEPA 2.1](https://github.com/facebookresearch/vjepa2)，`vjepa2_1_vit_large_384` |
+| 空间视觉编码器 | [DINOv2](https://huggingface.co/facebook/dinov2-with-registers-large) |
+| 文本编码器与 tokenizer | [Wan 2.2](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers) |
+
+推理不需要训练数据或单独的 Wan transformer 初始化权重。
+
+### 3. 运行测评
+
+```bash
+uv run --frozen --all-extras --no-sync bash test_libero.sh --all-tasks
+uv run --frozen --all-extras --no-sync bash test_libero_plus.sh --all-tasks
+uv run --frozen --all-extras --no-sync bash test_robotwin_clean.sh --all-tasks
+uv run --frozen --all-extras --no-sync bash test_robotwin_rand.sh --all-tasks
+```
+
+| 推理设置 | LIBERO / LIBERO-Plus | RoboTwin |
+| --- | :---: | :---: |
+| 去噪步数 | 10 | 10 |
+| Replan steps | 10 | 24 |
+| Seed | 7 | 0 |
+| 编译 / 保存视频 | 开启 | 开启 |
+
+默认使用 8 张 GPU，每张卡跨任务复用一个模型。设置 `EVAL_GPU_IDS=0` 可使用单卡。成功率、日志与视频输出到 `outputs/`。
+
+<details>
+<summary><b>Note：LIBERO 语言映射</b></summary>
+
+LIBERO 训练数据中的语言指令有时与 benchmark 提供的指令不同。推理代码通过明文到明文的精确查表，将 benchmark 指令映射回训练指令。**LIBERO-Plus 的 language 扰动不做映射**；找不到映射时也直接使用 benchmark 原始指令。
+
+</details>
+
+## 📊 论文报告结果
+
+| Benchmark | 成功率 |
+| --- | :---: |
+| LIBERO | 97.25% |
+| LIBERO-Plus | 80.85% |
+| RoboTwin clean | 70.84% |
+| RoboTwin randomized | 12.98% |
+
+以上为[论文与项目主页](https://zexinfeng-cn.github.io/works/copper-policy/#main-result)报告的结果。每次测评会记录该次实际运行的分数。
+
+## 使用范围
+
+模型用于上述模拟机器人环境的研究与测评，输入和动作约定与对应 benchmark 绑定。真实机器人部署需要适配机器人本身的权重与接口。
+
+## 📚 引用
+
+```bibtex
+@misc{feng2026copperpolicyfocusrepresentationrobust,
+  title={Copper-Policy: Focus on the Representation for Robust Robot Manipulation},
+  author={Zexin Feng and Yixu Feng and Lingyu Xiao and Shang Su and Kexin Zheng and Chang Xu and Mengkai Shi and Shuo Feng and Xintao Yan},
+  year={2026},
+  eprint={2609.32779},
+  archivePrefix={arXiv},
+  primaryClass={cs.RO},
+  url={https://arxiv.org/abs/2609.32779}
+}
+```
+
+## 🙏 致谢与许可证
+
+实现基于 [FastWAM](https://github.com/yuantianyuan01/FastWAM) 与 [LingBot-VA](https://github.com/robbyant/lingbot-va)。数据集转为 LMDB 使用我们自己的 [lerobot-tools](https://github.com/Mark4551124015/lerobot-tools)。Codex 协助整理了开源代码。
+
+本次发布使用 [Apache 2.0](LICENSE) 许可证。外部编码器、模拟器与资源遵循各自的许可证。

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