Upload 4 files

LEHOME_REPO_README.md

@@ -0,0 +1,184 @@
+<p align="center">
+  <h1 align="center">
+    LeHome Challenge 2026
+  </h1>
+  <h2 align="center">
+    Challenge on Garment Manipulation Skill Learning in Household Scenarios
+  </h2>
+
+  <h3 align="center">
+    <a href="https://lehome-challenge.com/">Competition Website</a>
+  </h3>
+</p>
+
+<div align="center">
+
+[![Python](https://img.shields.io/badge/python-3.11-blue.svg)](https://www.python.org/)
+[![Isaac Lab](https://img.shields.io/badge/Isaac%20Lab-2.3.1-green.svg)](https://isaac-sim.github.io/IsaacLab/main/index.html)
+[![LeRobot](https://img.shields.io/badge/LeRobot-0.4.2-yellow.svg)](https://github.com/huggingface/lerobot)
+[![License](https://img.shields.io/badge/license-Apache%202.0-red.svg)](LICENSE)
+[![ICRA](https://img.shields.io/badge/ICRA-2026-orange.svg)](https://2026.ieee-icra.org/program/competitions/)
+
+</div>
+
+## 📑 Table of Contents
+
+- [📑 Table of Contents](#-table-of-contents)
+- [🚀 Quick Start](#-quick-start)
+  - [1. Installation](#1-installation)
+    - [Use UV](#use-uv)
+    - [Use Docker](#use-docker)
+  - [2. Assets \& Data Preparation](#2-assets--data-preparation)
+    - [Download Simulation Assets](#download-simulation-assets)
+    - [Download Example Dataset](#download-example-dataset)
+    - [Collect Your Own Data](#collect-your-own-data)
+  - [3. Train](#3-train)
+    - [Quick Start](#quick-start)
+  - [4. Eval](#4-eval)
+    - [Common Options](#common-options)
+    - [Garment Test Configuration](#garment-test-configuration)
+- [📮 Submission](#-submission)
+- [🧩 Acknowledgments](#-acknowledgments)
+
+## 🚀 Quick Start
+
+> ⚠️ **IMPORTANT**: 
+> For Ubuntu version and GPU-related settings, please refer to the [IsaacSim 5.1.0 Documentation](https://docs.isaacsim.omniverse.nvidia.com/5.1.0/installation/requirements.html). And the simulation currently only supports CPU devices.
+
+### 1. Installation
+We offer two installation methods: UV and Docker for submission and local evaluation.
+
+#### Use UV
+
+The simulation environment is based on the IssacLab and LeRobot repositories; please refer to [UV installation guide](docs/installation.md).
+
+#### Use Docker
+
+***Docker is not ready yet; please use uv for Installation for now.***
+
+### 2. Assets & Data Preparation
+
+#### Download Simulation Assets
+
+Download the required simulation assets (scenes, objects, robots) from HuggingFace:
+
+```bash
+# This creates the Assets/ directory with all required simulation resources
+hf download lehome/asset_challenge --repo-type dataset --local-dir Assets
+```
+
+#### Download Example Dataset
+
+We provide demonstrations for four types of garments. Download from HuggingFace:
+
+```bash
+hf download lehome/dataset_challenge_merged --repo-type dataset --local-dir Datasets/example
+```
+
+If you need depth information or individual data for each garment. Download from HuggingFace:
+
+```bash
+hf download lehome/dataset_challenge --repo-type dataset --local-dir Datasets/example
+```
+
+#### Collect Your Own Data
+For detailed instructions on teleoperation data collection and dataset processing, please refer to our [Dataset Collection and Processing Guide](docs/datasets.md) ( using SO101 Leader is strongly recommended).
+
+### 3. Train
+
+We provide several training examples; the models and training framework are from LeRobot.
+
+#### Quick Start
+
+Train using one of the pre-configured training files:
+
+```bash
+lerobot-train --config_path=configs/train_<policy>.yaml
+```
+
+**Available config files:**
+- `configs/train_act.yaml` - ACT 
+- `configs/train_dp.yaml` - DP
+- `configs/train_smolvla.yaml` - SmolVLA 
+
+**Key configuration options:**
+- **Dataset path**: Update `dataset.root` to point to your dataset
+- **Input/Output features**: Specify which observations and actions to use
+- **Training parameters**: Adjust `batch_size`, `steps`, `save_freq`, etc.
+- **Output directory**: Modify `output_dir` to save models elsewhere
+
+> 📖 **For detailed training instructions, feature selection guide, and configuration options, see our [Training Guide](docs/training.md).**
+
+### 4. Eval
+
+Evaluate your trained policy on the challenge garments. The framework supports LeRobot policies and custom implementations.
+
+**Examples:**
+
+```bash
+# Evaluate using LeRobot policy 
+# Note: --policy_path and --dataset_root are required parameters for LeRobot policies, ready to run once the dataset and model checkpoints are prepared.
+python -m scripts.eval \
+    --policy_type lerobot \
+    --policy_path outputs/train/act_top_long/checkpoints/last/pretrained_model \
+    --garment_type "top_long" \
+    --dataset_root Datasets/example/top_long_merged \
+    --num_episodes 2 \
+    --enable_cameras \
+    --device cpu    
+
+# Evaluate custom policy
+# Note: Participants can define their own model loading logic within the policy class. Provides flexibility for participants to implement specialized loading and inference logic.
+python -m scripts.eval \
+    --policy_type custom \
+    --garment_type "top_long" \
+    --num_episodes 5 \
+    --enable_cameras \
+    --device cpu
+```
+
+#### Common Options
+
+| Parameter | Description | Default | Required For |
+|-----------|-------------|---------|--------------|
+| `--policy_type` | Policy type: `lerobot`, `custom` | `lerobot` | All |
+| `--policy_path` | Path to model checkpoint | - | All (passed as `model_path` for custom) |
+| `--dataset_root` | Dataset path (for metadata) | - | **LeRobot only** |
+| `--garment_type` | Type of garments: `top_long`, `top_short`, `pant_long`, `pant_short`, `custom` | `top_long` | All |
+| `--num_episodes` | Episodes per garment | `5` | All |
+| `--max_steps` | Max steps per episode | `600` | All |
+| `--save_video` | Save evaluation videos | | All |
+| `--video_dir` | Directory to save evaluation videos | `outputs/eval_videos` | `--save_video` |
+| `--enable_cameras` | Enable camera rendering | | All |
+| `--device` | Device for inference: only `cpu` |'cpu'| All |
+| `--headless` | Used for evaluation without GUI | disabled | All |
+
+**Parameter Descriptions:**
+
+* **Required for LeRobot Policy**: `--policy_path` (model path) and `--dataset_root` (dataset path, used for loading metadata).
+* **Custom Policy**: `--policy_path` is passed to the policy constructor as `model_path`. Participants can define their own model loading logic (refer to `scripts/eval_policy/example_participant_policy.py`).
+
+
+#### Garment Test Configuration
+Evaluation is performed on the `Release` set of garments. Under the directory `Assets/objects/Challenge_Garment/Release`, each garment category folder contains a corresponding text file listing the garment names (e.g., `Top_Long/Top_Long.txt`).
+
+*   **Evaluate a Category**: Set `--garment_type` to `top_long`, `top_short`, `pant_long`, or `pant_short` to evaluate all garments within that category.
+*   **Evaluate Specific Garments**: Edit `Assets/objects/Challenge_Garment/Release/Release_test_list.txt` to include only the garments you want to test, then run with `--garment_type custom`.
+
+> 📖 **For detailed policy evaluation guide**, see [eval_guide](docs/policy_eval.md).
+
+
+## 📮 Submission
+
+Once you are satisfied with your model's performance, follow these steps to submit your results to the competition leaderboard:
+
+>Submission instructions will be available on the [competition website](https://lehome-challenge.com/).
+
+## 🧩 Acknowledgments
+
+This project stands on the shoulders of giants. We utilize and build upon the following excellent open-source projects:
+
+- **[Isaac Sim](https://docs.isaacsim.omniverse.nvidia.com/5.1.0/index.html)** - For photorealistic physics simulation
+- **[Isaac Lab](https://isaac-sim.github.io/IsaacLab/main/index.html)** - For modular robot learning environments
+- **[LeRobot](https://github.com/huggingface/lerobot)** - For state-of-the-art Imitation Learning algorithms
+- **[Marble](https://marble.worldlabs.ai/)** - For diverse simulation scene generation

README.md

@@ -1,3 +1,61 @@
----
-license: mit
----
+---
+license: apache-2.0
+tags:
+- lerobot
+- lehome
+- act
+- robotics
+---
+
+# ACT Policy For LeHome Challenge
+
+This repository contains a LeRobot-format ACT checkpoint exported from:
+
+- Experiment: `outputs/train/act_top_longee_depth_bs32`
+- Checkpoint: `outputs/train/act_top_longee_depth_bs32/checkpoints/0000125000`
+- Team: `HandX`
+- Registration ID: `r26`
+- Contact: `xtli312@163.com`
+
+## Expected Layout
+
+- `pretrained_model/`
+- `train_act_1.yaml`
+- `LEHOME_REPO_README.md`
+
+## Evaluation
+
+Use this checkpoint with the LeHome evaluation script:
+
+```bash
+python -m scripts.eval \
+    --policy_type lerobot \
+    --policy_path "<downloaded_bundle>/pretrained_model" \
+    --dataset_root "Datasets/example/top_long_merged" \
+    --garment_type "custom" \
+    --num_episodes 5 \
+    --task_description "fold the garment on the table" \
+    --enable_cameras \
+    --device cpu
+```
+
+## Required Dependencies
+
+- Python 3.11
+- Isaac Sim 5.1.0
+- Isaac Lab 2.3.1
+- `lerobot`
+- LeHome repository source code
+
+## Notes
+
+- The challenge organizers also need the official assets and dataset metadata.
+- If you upload this bundle to Hugging Face, point the Google Form `Policy Submission` field to that repository URL.
+- Local self-reported evaluation on `Release_test_list.txt` with 5 episodes per garment:
+  - Total Episodes: `15`
+  - Average Return: `183.60 +- 67.87`
+  - Success Rate: `20.00%`
+  - `Top_Long_Seen_6`: `20.00%` success, `224.89` avg return
+  - `Top_Long_Seen_7`: `0.00%` success, `205.17` avg return
+  - `Top_Long_Unseen_1`: `40.00%` success, `120.74` avg return
+- Generated on 2026-04-02T12:02:18.

UPLOAD_TO_HF.md

@@ -0,0 +1,14 @@
+# Upload Instructions
+
+From the repository root:
+
+```bash
+huggingface-cli repo create <your-hf-repo-name> --type model
+huggingface-cli upload <your-username>/<your-hf-repo-name> "/data3/lxt-24/lehome-challenge/submission/hf/act_top_longee_depth_bs32_0000125000_hf_bundle" .
+```
+
+After upload, use:
+
+`https://huggingface.co/<your-username>/<your-hf-repo-name>`
+
+as the `Policy Submission` link in the leaderboard form.

train_act_1.yaml

@@ -0,0 +1,39 @@
+dataset:
+  repo_id: repo_act
+  root: Datasets/example/top_long_merged
+
+policy:
+  type: act
+  device: cuda
+  push_to_hub: false
+  
+  input_features:
+    observation.ee_pose:
+      type: STATE
+      shape: [16]
+    observation.images.top_rgb:
+      type: VISUAL
+      shape: [3, 480, 640]
+    observation.images.left_rgb:
+      type: VISUAL
+      shape: [3, 480, 640]
+    observation.images.right_rgb:
+      type: VISUAL
+      shape: [3, 480, 640]
+    observation.top_depth:
+      type: STATE
+      shape: [1, 480, 640]
+  
+  output_features:
+    action:
+      type: ACTION
+      shape: [12]
+
+output_dir: outputs/train/act_top_longee_depth_bs32
+batch_size: 32
+steps: 3000000000
+save_freq: 5000
+log_freq: 1000
+
+wandb:
+  enable: false