declare-lab / Emma-X

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 ).

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
• Pretraining Dataset: Augmented version of Bridge V2 , for more info check our repository.
• Repository: 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

# 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, ...)