update README.md

README.md

@@ -5,25 +5,25 @@ license: mit
 
 # IflyBotVLM
 
-## Introduction
+## 🔥Introduction
 
 We introduce IflyBotVLM, a general-purpose Vision-Language-Model (VLM) specifically engineered for the domain of Embodied Intelligence. The primary objective of this model is to bridge the cross-modal semantic gap between high-dimensional environmental perception and low-level robot motion control. It achieves this by abstracting complex scene information into an "Operational Language" that is body-agnostic and transferable, thus enabling seamless perception-to-action closed-loop coordination.
 
 The architecture of IflyBotVLM is designed to realize four critical functional capabilities in the embodied domain:
 
-**Spatial Understanding and Metric**: Provides the model with the capacity to understand spatial relationships and perform relative position estimation among objects in the environment.
+**🧭Spatial Understanding and Metric**: Provides the model with the capacity to understand spatial relationships and perform relative position estimation among objects in the environment.
 
-**Interactive Target Grounding**: Supports diverse grounding mechanisms, including 2D/3D object detection in the visual modality, language-based object and spatial referring, and the prediction of critical object affordance regions.
+**🎯Interactive Target Grounding**: Supports diverse grounding mechanisms, including 2D/3D object detection in the visual modality, language-based object and spatial referring, and the prediction of critical object affordance regions.
 
-**Action Abstraction and Control Parameter Generation**: Generates outputs directly relevant to the manipulation domain, providing grasp poses and manipulation trajectories.
+**🤖Action Abstraction and Control Parameter Generation**: Generates outputs directly relevant to the manipulation domain, providing grasp poses and manipulation trajectories.
 
-**Task Planning**: Leveraging the current scene comprehension, this module performs multi-step prediction to decompose complex tasks into a sequence of atomic skills, fundamentally supporting the robust execution of long-horizon tasks.
+**📋Task Planning**: Leveraging the current scene comprehension, this module performs multi-step prediction to decompose complex tasks into a sequence of atomic skills, fundamentally supporting the robust execution of long-horizon tasks.
 
 We anticipate that IflyBotVLM will serve as an efficient and scalable foundation model, driving the advancement of embodied AI from single-task capabilities toward generalist intelligent agents.
 
 ![image/png](https://huggingface.co/datasets/IflyBot/IflyBotVLM-Repo/resolve/main/images/radar_performance.png)
 
-## Model Architecture
+## 🏗️Model Architecture
 
 IflyBotVLM inherits the robust, three-stage "ViT-Projector-LLM" paradigm from established Vision-Language Models. It integrates a dedicated, incrementally pre-trained Visual Encoder with an advanced Language Model via a simple, randomly initialized MLP projector for efficient feature alignment.
 
@@ -31,7 +31,7 @@ The core enhancement lies in the ViT's Positional Encoding (PE) layer. Instead o
 
 ![image/png](https://huggingface.co/datasets/IflyBot/IflyBotVLM-Repo/resolve/main/images/architecture.png)
 
-## Model Performance
+## 📊Model Performance
 
 IflyBotVLM demonstrates superior performance across various challenging benchmarks.
 
@@ -41,7 +41,7 @@ IflyBotVLM demonstrates superior performance across various challenging benchmar
 
 IflyBotVLM-8B achieves state-of-the-art (SOTA) or near-SOTA performance on ten spatial comprehension, spatial perception, and temporal task planning benchmarks: Where2Place, Refspatial-bench, ShareRobot-affordance, ShareRobot-trajectory, BLINK(spatial), EmbSpatial, ERQA, CVBench, SAT, EgoPlan2.
 
-## Quick Start
+## 🚀Quick Start
 
 ### Using 🤗  Transformers to Chat