Part 5/10:
Data Generation: As robots require substantial datasets to learn effectively, creating this data in real-world environments is time-consuming and costly. Nvidia is pushing forward with systems that can generate synthetic training data efficiently.
Model Architecture: The development of robust models is critical for enabling smart learning in robots. Huang stressed the importance of designing suitable software to optimize robot performance based on the data gathered.
Scaling: Understanding how to scale robot intelligence—by manipulating data volume or computational power—is fundamental to achieving smarter machines.