The Real-World Challenge of Training AI
As artificial intelligence continues to master digital tasks, a new frontier has emerged: teaching robots how to function in the physical world. Across the globe, from India to the United States, humans are now playing a critical role in building what experts call “physical AI.” This new wave of development seeks to bring humanoid robots into homes, workplaces, and factories by training them to move and act like people.
In the southern Indian town of Karur, 28-year-old Naveen Kumar begins his day not at a hotel, but at a data lab. His task? Folding hundreds of hand towels while wearing a GoPro camera on his forehead. Each movement—lifting, shaking, folding—is meticulously recorded to create training data for robots.
The Role of Human Demonstrations
Kumar works for Objectways, a data annotation startup that specializes in generating physical behavior data for AI. His team of engineers, although not trained in hospitality, follow precise protocols to perform everyday tasks. Any deviation means starting over. “Sometimes we have to delete nearly 150 or 200 videos because of silly errors,” Kumar explains.
The captured videos are then reviewed and annotated. Every movement—whether an arm moves left or right, or a towel is folded in three parts—is labeled. These datasets become training tools for robots that need to understand human gestures and physical interaction with objects.
Big Tech’s Investment in Robotics
Companies like Tesla, Boston Dynamics, Nvidia, and Google are racing to develop the next generation of humanoid robots. Tesla already showcases its Optimus robots at company events. Meanwhile, Nvidia estimates the humanoid robotics market could reach $38 billion within the next decade.
Startups are also getting in on the action. San Francisco-based Encord, for instance, partners with Objectways to collect human demonstration data. Other emerging firms like Physical Intelligence and Dyna Robotics are building foundational models to bridge the digital and physical AI divide.
Teleoperation and Remote Control
One key method for training humanoid robots is teleoperation—where humans guide robots using controllers. Ali Ansari, founder of Micro1, explains that robots can be trained to perform tasks like picking up a cup or making tea by mimicking human-controlled actions. These sessions are recorded, and AI systems learn from both successful and failed attempts.
In fact, some warehouses in Eastern Europe are being developed specifically for this kind of data collection. Operators sit with joysticks, guiding robots located in other countries. This practice has given rise to what some are calling “arm farms.”
Physical AI: A New Gold Rush
As the demand for humanoid robots grows, so does the need for high-quality training data. Companies like Deepen AI are combining real and synthetic data collected from human demonstrations and staged environments. Although much of this data is still being gathered outside of Western countries, improvements in automation and simulation are gradually reducing that dependency.
Still, capturing physical data is not without challenges. Dev Mandal, a young entrepreneur in Bengaluru, tried to capitalize on the demand by offering affordable labor to record physical tasks. However, he found the requirements too specific. “Everything, down to the color of the table, had to be specified,” he said. This level of precision made it difficult to scale his business.
Human Data Collection at Scale
To further fuel this data-intensive process, companies are now collecting human behavior data using wearable technology. Micro1 pays people in Brazil, Argentina, India, and the U.S. to wear smart glasses that record their daily actions. Figure AI, based in San José, has partnered with Brookfield to capture movements inside 100,000 homes, using part of its $1 billion funding to gather first-person footage.
Meta-backed Scale AI has also joined the race, recording over 100,000 hours of training footage for robotics at its San Francisco lab. The goal is to provide a comprehensive view of how humans move and interact with their environment.
The Future of Human-Robot Collaboration
Back in Karur, Objectways continues to expand its training operations. Founder Ravi Shankar says the company has recently annotated videos of robotic arms folding boxes, sorting clothes, and identifying objects by color. In a recent project, his team labeled 15,000 videos of robots learning to fold and sort towels and garments.
“Sometimes the robot’s arms throw the clothes and won’t fold properly,” says Kavin, a 27-year-old employee. “But they’re learning quickly. In five or 10 years, they’ll be able to do all the jobs and there will be none left for us.”
While some fear widespread job displacement, others believe humanoid robots will relieve humans of repetitive tasks, potentially lowering labor costs and freeing people to focus on more meaningful work. As AI and robotics continue to converge, the next decade may well be defined by how effectively we teach machines to be more like us.
This article is inspired by content from Original Source. It has been rephrased for originality. Images are credited to the original source.