Researchers are rushing to build AI-powered robots. But will they work?
AI has transformed the digital landscape, yet challenges persist. The limitations of AI are becoming increasingly evident. It is essential to strike a balance between the virtual and physical realms. As AI continues to evolve, the complexities of its impact on reality become more pronounced. The intersection of AI and the real world presents both opportunities and obstacles.
The Future of AI in Robotics
STANFORD, Calif. — Artificial intelligence can find you a recipe or generate a picture, but it can't hang a picture on a wall or cook you dinner. Chelsea Finn wants that to change. Finn, an engineer and researcher at Stanford University, believes that AI may be on the cusp of powering a new era in robotics. "In the long term we want to develop software that would allow the robots to operate intelligently in any situation," she says. A company she co-founded has already demonstrated a general-purpose AI robot that can fold laundry, among other tasks.
The Gap Between Expectation and Reality
There are fewer parts of science and engineering that have a larger gap between expectation and reality than robotics. The very word "robot" was coined by Karel Čapek, a Czech writer who, in the 1920s, wrote a play that imagined human-like beings that could carry out any task their owner commanded. In reality, robots have had a great deal of trouble doing even trivial jobs. Machines are at their best when they perform highly repetitive movements in a carefully controlled environment–for example, on an automotive assembly line inside a factory–but the world is filled with unexpected obstacles and uncommon objects.
AI-Powered Robotics Advancements
In Finn's laboratory at Stanford University, graduate student Moo Jin Kim demonstrates how AI-powered robots at least have the potential to fix some of those problems. Kim has been developing a program called "OpenVLA," which stands for Vision, Language, Action. Regular robots must be carefully programmed, but this robot is powered by a teachable AI neural network. The neural network operates similarly to how the human brain might work. Kim can train the OpenVLA model to do different tasks by showing it and reinforcing the connections that matter.
Challenges and Opportunities
Stanford researcher Chelsea Finn's company, Physical Intelligence, is working towards training robots to quickly adapt to various tasks. However, compiling real-world training data for robots is a challenging task. Ken Goldberg from UC Berkeley and Pulkit Agrawal from MIT have differing views on using simulation to train AI neural networks for robotics. Simulation has shown promise but also has limitations when it comes to real-world applications.
The Road Ahead
Researchers like Matthew Johnson-Roberson from Carnegie Mellon University emphasize the need for fundamental research into how neural networks can process complex tasks involving space and time. Despite the challenges, the integration of AI in robotics is believed to bring significant advancements. AI-driven systems are already making an impact in areas such as package sorting and automation. While the field of robotics still faces obstacles, the potential for AI to enhance and augment human labor is promising.