According to @StanfordAILab, recent research highlighted in @XiaomengXu11's blog post demonstrates that data-driven AI methods can significantly expand robot learning beyond standard platforms to include robots of diverse shapes and sizes (source: Stanford AI Lab, June 30, 2025). By leveraging large datasets and advanced machine learning algorithms, unconventional robots—such as soft robots or modular systems—can achieve complex tasks that were previously unattainable with traditional hardware-focused design and control. This evolution opens up new business opportunities in custom robotics development, flexible automation, and adaptive manufacturing, addressing a wider array of industry needs (source: Stanford AI Lab, June 30, 2025).

According to @GoogleDeepMind, Gemini AI enables robots to quickly adapt to unfamiliar physical activities, such as performing a basketball slam dunk on the first attempt. This advancement demonstrates how large AI models can facilitate real-time learning and adaptation in robotics, significantly reducing the time and data needed for training new tasks. The business impact is substantial, as such adaptive AI can accelerate automation in sectors requiring physical dexterity, including logistics, manufacturing, and service industries. This development highlights market opportunities for companies seeking to deploy robots capable of handling dynamic, unstructured environments with minimal manual programming (source: Google DeepMind Twitter, June 27, 2025).