Recent breakthroughs in generative AI have centered largely on language and imagery—from chatbots that compose sonnets and analyze text to voice models that mimic human speech and tools that transform prompts into vivid artwork. But global chip giant Nvidia is now making a bolder claim: the next chapter of AI is about systems that take action in high-stakes, real-world scenarios.
At the recent International Conference on Learning Representations (ICLR 2025) in Singapore, Nvidia unveiled more than 70 research papers showcasing advances in AI systems designed to perform complex tasks beyond the digital realm.
Driving this shift are agentic and foundational AI models. Nvidia’s latest research highlights how combining these models can influence the physical world—spanning adaptive robotics, protein design, and real-time reconstruction of dynamic environments for autonomous vehicles. As demand for AI grows across industries, Nvidia is positioning itself as a core infrastructure provider powering this new era of intelligent action.
Bryan Catanzaro, vice president of applied deep learning research at Nvidia, described the company’s new direction as a full-stack AI initiative.
“We aim to accelerate every level of the computing stack to amplify the impact and utility of AI across industries,” he tells Fast Company. “For AI to be truly useful, it must evolve beyond traditional applications and engage meaningfully with real-world use cases. That means building systems capable of reasoning, decision-making, and interacting with the real-world environment to solve practical problems.”
Among the research presented, four models stood out—one of the most promising being Skill Reuse via Skill Adaptation (SRSA).
This AI framework enables robots to handle unfamiliar tasks without retraining from scratch—a longstanding hurdle in robotics. While most robotic AI systems have focused on basic tasks like picking up objects, more complex jobs such as precision assembly on factory lines remain difficult. Nvidia’s SRSA model aims to overcome that challenge by leveraging a library of previously learned skills to help robots adapt more quickly.
