---
license: apache-2.0
---

# Emma-X (7B)
Model Overview

EMMA-X is an Embodied Multimodal Action (VLA) Model designed to bridge the gap between Visual-Language Models (VLMs) and robotic control tasks. EMMA-X generalizes effectively across diverse environments, objects, and instructions while excelling at long-horizon spatial reasoning and grounded task planning using a novel Trajectory Segmentation Strategy.

•	Hierarchical Embodiment Dataset:
EMMA-X is trained on a dataset derived from BridgeV2, containing 60,000 robot manipulation trajectories. Trained using a hierarchical dataset with visual grounded chain-of-thought reasoning, EMMA-X's output will include the following components:

Grounded Chain-of-Thought Reasoning:
Helps break down tasks into smaller, manageable subtasks, ensuring accurate task execution by mitigating hallucination in reasoning.

Gripper Position Guidance: Affordance point inside the image.

Look-Ahead Spatial Reasoning:
Enables the model to plan actions while considering spatial guidance for effective planning, enhancing long-horizon task performance. 

Action: Action policy in 7-dimensional vector to control the robot ([WidowX-6Dof](https://www.trossenrobotics.com/widowx-250)).

## Model Card
- **Developed by:** SUTD Declare Lab
- **Model type:** Vision-language-action (language, image => reasoning, robot actions)
- **Language(s) (NLP):** en
- **License:** Apache-2.0
- **Finetuned from:** [`openvla-7B`](https://huggingface.co/openvla/openvla-7b/)
- **Pretraining Dataset:** Augmented version of [Bridge V2](https://rail-berkeley.github.io/bridgedata/), for more info check our repository.
- **Repository:** [https://github.com/declare-lab/Emma-X/](https://github.com/declare-lab/Emma-X/)
- **Paper:** [Emma-X: An Embodied Multimodal Action Model with Grounded Chain of Thought and Look-ahead Spatial Reasoning]()
- **Project Page & Videos:** []()

## Getting Started
```python
# Install minimal dependencies (`torch`, `transformers`, `timm`, `tokenizers`, ...)
# > pip install -r https://raw.githubusercontent.com/openvla/openvla/main/requirements-min.txt
from transformers import AutoModelForVision2Seq, AutoProcessor
from PIL import Image

import torch

# Load Emma-X
vla = AutoModelForVision2Seq.from_pretrained(
    "declare-lab/Emma-X",
    attn_implementation="flash_attention_2",  # [Optional] Requires `flash_attn`
    torch_dtype=torch.bfloat16, 
    low_cpu_mem_usage=True, 
    trust_remote_code=True
).to("cuda:0")

# Grab image input & format prompt of size 224x224
image: Image.Image = get_from_camera(...)
prompt = "In: What action should the robot take to achieve the instruction\nINSTRUCTION: \n{<Instruction here>}\n\nOut: "

# Predict Action (action is a 7 dimensional vector to control the robot)
action, grounded_reasoning = vla.generate_actions(
    image=image, prompt_text=prompt, type="act", do_sample=False,
    max_new_tokens=512, do_sample=False
)

print("Grounded Reasoning:", grounded_reasoning)
# Execute...
robot.act(action, ...)
```