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---
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
``` |