Improve dataset card for EC-Diffuser: Add robotics task, links, description, and comprehensive sample usage

This PR significantly improves the dataset card for the `EC-Diffuser` dataset by:
- Adding the `task_categories` metadata set to `robotics`, improving discoverability.
- Including relevant `tags` such as `multi-object-manipulation`, `diffusion-models`, `behavioral-cloning`, and `object-centric` for better categorization.
- Providing a clear description of the dataset and the EC-Diffuser approach.
- Adding direct links to the associated Hugging Face paper (https://huggingface.co/papers/2412.18907), the project website (https://sites.google.com/view/ec-diffuser), and the GitHub code repository (https://github.com/carl-qi/EC-Diffuser).
- Incorporating a comprehensive "Sample Usage" section, directly sourced from the project's GitHub README, which includes steps for environment setup ("Installation"), "Downloading Datasets", "Evaluating a Pretrained Agent", and "Training an Agent" to help users get started quickly.

README.md

@@ -1,3 +1,120 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- robotics
+tags:
+- multi-object-manipulation
+- diffusion-models
+- behavioral-cloning
+- object-centric
+---
+
+# EC-Diffuser Dataset
+
+This repository contains the datasets, pretrained agents, and Deep Latent Predictor (DLP) representations for the paper [EC-Diffuser: Multi-Object Manipulation via Entity-Centric Behavior Generation](https://huggingface.co/papers/2412.18907).
+
+EC-Diffuser proposes a novel behavioral cloning (BC) approach that leverages object-centric representations and an entity-centric Transformer with diffusion-based optimization. This enables efficient learning from offline image data for multi-object manipulation tasks, leading to substantial performance improvements and compositional generalization to novel object configurations and goals.
+
+*   **Paper:** [EC-Diffuser: Multi-Object Manipulation via Entity-Centric Behavior Generation](https://huggingface.co/papers/2412.18907)
+*   **Project Website:** [https://sites.google.com/view/ec-diffuser](https://sites.google.com/view/ec-diffuser)
+*   **Code Repository:** [https://github.com/carl-qi/EC-Diffuser](https://github.com/carl-qi/EC-Diffuser)
+
+## Sample Usage
+
+The datasets, pretrained agents, and DLP representations provided here are intended for use with the official [EC-Diffuser code repository](https://github.com/carl-qi/EC-Diffuser). Below are instructions for setting up the environment, downloading the data, and using the provided scripts for evaluation and training.
+
+### Installation
+
+Follow these steps to set up the environment (tested on Python 3.8):
+
+1.  **Create and activate a Conda environment:**
+
+    ```bash
+    conda create -n dlp python=3.8
+    conda activate dlp
+    ```
+
+2.  **Install main dependencies:**
+    The full list of dependencies can be found in the `requirements.txt` file within the [code repository](https://github.com/carl-qi/EC-Diffuser).
+
+3.  **Install Diffuser-related packages:**
+
+    ```bash
+    cd diffuser
+    pip install -e .
+    cd ../
+    ```
+
+4.  **Setup for the FrankaKitchen environment:**
+
+    Install D4RL by cloning the repository:
+
+    ```bash
+    git clone https://github.com/Farama-Foundation/d4rl.git
+    cd d4rl
+    pip install -e .
+    cd ../
+    ```
+
+5.  **Finalize environment setup:**
+
+    Run the provided setup script:
+
+    ```bash
+    bash setup_env.sh
+    ```
+
+    *(If the script requires sourcing, you can also run: `source setup_env.sh`)*
+
+### Downloading Datasets
+
+Download the required datasets, pretrained agents, and DLP representations from this Hugging Face dataset repository:
+
+```bash
+git lfs install
+git clone https://huggingface.co/datasets/carlq/ecdiffuser-data
+```
+
+### Evaluating a Pretrained Agent
+
+You can evaluate the pretrained agents with the following commands. Replace `CUDA_VISIBLE_DEVICES=0,1` with the GPU devices you wish to use (Note IsaacGym env has to be on GPU 0).
+
+-   **PushCube Agent:**
+
+    ```bash
+    CUDA_VISIBLE_DEVICES=0,1 python diffuser/scripts/eval_agent.py --config config.plan_pandapush_pint --num_entity 3 --planning_only
+    ```
+
+-   **PushT Agent:**
+
+    ```bash
+    CUDA_VISIBLE_DEVICES=0,1 python diffuser/scripts/eval_agent.py --config config.plan_pandapush_pint --push_t --num_entity 3 --push_t_num_color 1 --planning_only
+    ```
+
+-   **FrankaKitchen Agent:**
+
+    ```bash
+    CUDA_VISIBLE_DEVICES=0,1 python diffuser/scripts/eval_agent.py --config config.plan_pandapush_pint --kitchen --planning_only
+    ```
+
+### Training an Agent
+
+Train your own agents using the commands below. Replace `CUDA_VISIBLE_DEVICES=0,1` with the GPU devices you wish to use (Note IsaacGym env has to be on GPU 0).
+
+-   **Train a PushCube Agent (3 cubes):**
+
+    ```bash
+    CUDA_VISIBLE_DEVICES=0,1 python diffuser/scripts/train.py --config config.pandapush_pint --num_entity 3
+    ```
+
+-   **Train a PushT Agent (1 T-shaped object):**
+
+    ```bash
+    CUDA_VISIBLE_DEVICES=0,1 python diffuser/scripts/train.py --config config.pandapush_pint --push_t --num_entity 1
+    ```
+
+-   **Train a FrankaKitchen Agent:**
+
+    ```bash
+    CUDA_VISIBLE_DEVICES=0,1 python diffuser/scripts/train_kitchen.py --config config.pandapush_pint --kitchen
+    ```