Methods for Obtaining Multimodal Data
Concept Overview
Multimodal models require large amounts of data from different sources, such as text, images, audio, video, and interaction signals. For embodied intelligent robots, the way multimodal data is collected strongly affects model performance, realism, scalability, and deployment potential. This section introduces three major approaches for obtaining multimodal data: real environment collection, simulation and synthesis data, and integration of public datasets.
Key Idea
Different data acquisition methods provide different trade-offs:
- Real-world collection offers authenticity
- Simulation and synthesis offer controllability and scalability
- Public dataset integration offers large-scale and low-cost resources
In practice, embodied AI systems often combine these methods to balance realism, diversity, and efficiency.
Main Methods for Obtaining Multimodal Data
1. Real Environment Collection
Advantages
- High authenticity
- Natural modal alignment
Disadvantages
- High cost
- Limited data volume
- Labeling difficulties
Example
- GuideDog
2. Simulation and Synthesis Data
Advantages
- Strong controllability
- Data diversity
- Automatic labeling
Disadvantages
- Lack of authenticity
- Modal limitations
- Technical dependence
Example
- Unicorn
3. Integration of Public Datasets
Advantages
- Large scale
- Low cost
- Semantic richness
Disadvantages
- Weak task relevance
- Unequal quality
- Privacy and copyright issues
Example
- SPIDER
Why This Matters for Embodied Intelligent Robots
For embodied intelligent robots, multimodal data is the basis of perception, grounding, and action learning. Different data sources contribute in different ways:
- Real-world data improves deployment realism
- Simulation data supports scalable robot training
- Public datasets provide broad semantic and multimodal knowledge
A practical robot learning pipeline often combines all three to reduce cost while maintaining performance and generalization.
Summary
There are three major ways to obtain multimodal data: real environment collection, simulation and synthesis, and public dataset integration. Each method has clear strengths and weaknesses. For embodied intelligent robots, choosing the right combination of data sources is essential for building effective and scalable multimodal systems.
Common Mistakes
- Assuming real-world data is always sufficient by itself
- Ignoring the sim-to-real gap in synthetic data
- Using public datasets without checking task relevance
- Focusing only on dataset size while ignoring data quality and alignment