Embodied Intelligence: Core Concepts, Three Elements, and Four Functional Modules

Introduction – Embodied intelligence combines a physical body with AI, adapting machine‑learning algorithms to interact with the physical world. Unlike software‑only agents such as ChatGPT, embodied agents embed large models in hardware, using sensors to perceive, plan, remember, and act in real environments.

Three Essential Elements – The body (hardware platform), intelligence (large models, speech, vision, control, navigation algorithms), and environment (the physical world) are tightly coupled, forming the foundation of advanced intelligence.

Four Functional Modules

Perception Module

The perception module gathers and processes information through various sensors:

Visible‑light camera – captures color images.

Infrared camera – provides thermal imaging, night vision, and can see through smoke.

Depth camera – measures distance to each pixel for 3‑D scene reconstruction.

LiDAR – emits laser pulses to generate high‑precision 3‑D point clouds.

Ultrasonic sensor – measures distance for obstacle avoidance.

Pressure sensor – detects force on robot limbs for walking and grasping.

Microphone – captures audio.

Specialized sensors such as electronic noses or humidity sensors can be added for specific applications, and perception data is processed by algorithms (e.g., YOLO for object detection, SLAM for navigation) that may be domain‑specific or require strong generalization.

Decision Module (Large Model)

The decision module receives perception data, performs planning and reasoning, and issues commands to the action module. Early systems relied on hand‑crafted rules; modern approaches use reinforcement learning (PPO, Q‑learning) and, increasingly, multimodal large models. These models shift from AI‑generated content (AIGC) to AI‑generated actions (AIGA), enabling vision‑language‑action (VLA) and vision‑language‑navigation (VLN) capabilities.

Action Module

The action module executes commands through three strategies:

Decision model calls pre‑programmed motion or manipulation algorithms.

Decision model collaborates with perception‑driven visual‑language models to adapt actions in real‑time.

End‑to‑end VLA/VLN models directly generate executable actions from language and visual inputs.

These approaches balance controllability, development effort, and generalization across environments.

Feedback Module

The feedback module closes the loop by feeding experience back to perception, decision, and action components, improving adaptability and intelligence. It can reinforce perception sensitivity, adjust decision parameters based on task success, and enable dynamic replanning for navigation or manipulation.

Overall, the four modules form a closed perception‑decision‑action‑feedback cycle that progressively integrates and enhances embodied AI capabilities.