I. Perception System
Hardware Sensors
| Type | Sensors | Applications |
|---|
| Vision sensor | Monocular/binocular cameras, panoramic cameras, event cameras | Environment perception |
| Distance sensor | LiDAR, millimeter-wave radar, ultrasonic | Ranging and obstacle avoidance |
| Motion sensor | IMU, wheel encoder, GPS/Beidou | Localization and navigation |
| Tactile sensor | Pressure sensor array, torque sensor, flexible electronic skin | Force feedback |
Perception Algorithms
- Environment understanding: 3D SLAM (e.g., ORB-SLAM3, LIO-SAM), semantic segmentation, depth estimation
- Object recognition: Object detection (YOLO, Faster R-CNN), object classification and tracking
- Multi-modal fusion: Sensor calibration and registration, multi-source data fusion
II. Decision and Control
High-level Decision System
- Deep learning models: CNN for visual input, RNN for sequential information
- Planning algorithms: A*, D* path planning, STRIPS, PDDL task planning
- Reinforcement learning: Q-learning, policy gradient methods
Low-level Control System
- Feedback control: PID control, adaptive control
- Advanced control: Model Predictive Control (MPC), sliding mode control
- Motion control: Inverse kinematics solving, trajectory interpolation
Architecture Type Comparison
| Architecture | Advantages | Applicable Scenarios |
|---|
| Hierarchical architecture | Clear modularity | Structured environments |
| End-to-end architecture | Fast response | Dynamic complex environments |
| Distributed architecture | Strong robustness | Multi-agent systems |
III. Learning and Adaptation
Main Learning Methods
- Deep Reinforcement Learning (Deep RL): Obtain optimal policies through “action-observation-feedback” cycles
- Imitation learning: Obtain initial policies by observing human expert demonstrations
- Evolutionary algorithms: Simulate natural selection to optimize policies
Training Solutions
- High-fidelity simulation environments (PyBullet, MuJoCo)
- Transfer learning (simulation to reality)
- Progressive training strategies
IV. Multi-modal Perception and Interaction
Perception Modalities
- Visual: RGB-D cameras, stereo vision, object recognition, semantic segmentation
- Auditory: Microphone array, sound source localization, speech recognition, emotion recognition
- Tactile: Force/torque sensors, tactile sensor arrays
Fusion Technologies
- Early fusion (data layer)
- Mid-level fusion (feature layer)
- Late fusion (decision layer)
Interaction Methods
- Voice interaction
- Visual interaction
- Gesture interaction
- Tactile feedback
V. Embodied Large Models
Core Components
- Large Language Models (LLM): GPT-4, PaLM
- Vision-Language Models (VLM): CLIP, Flamingo
Architecture Layers
- Cognitive layer: Natural language instruction processing
- Planning layer: Task execution plan generation
- Execution layer: Motion execution control
Application Prospects
- Home services
- Industrial scenarios
- Special environments (disaster rescue, space exploration)
VI. System Closed-loop Summary
Embodied AI is a “perception-cognition-action-learning” closed-loop system:
- Multi-modal perception: Various sensing devices such as vision, touch, and hearing collect environmental information
- Advanced cognitive models: Deep learning and large language models for environment understanding and decision-making
- Motion control system: Transform decisions into precise physical actions
- Continuous learning mechanism: Online learning, simulation training, experience replay achieve system iteration and optimization