Encord is exploring Brain Wave Data as a new way to improve physical AI training for robots and intelligent machines. The company believes Brain Wave Data could help overcome one of the industry’s biggest challenges by creating richer training datasets for robotics systems operating in real-world environments.
As artificial intelligence expands beyond software into physical machines, robotics developers continue searching for better training methods. Although AI models continue improving rapidly, many experts now believe limited real-world training data remains the greatest obstacle to building capable robots. Therefore, companies increasingly focus on collecting higher-quality information instead of relying only on larger AI models.
Encord operates a specialized robotics facility in San Leandro, California, where workers perform carefully designed physical tasks while wearing advanced equipment. During demonstrations, operators complete activities such as removing blocks from a Jenga tower while cameras record every movement. At the same time, specialized sensors monitor brain activity throughout each action, creating an entirely new layer of information for researchers.
The brain-monitoring technology comes from German startup Zander Labs, which specializes in neuroscience solutions. Its headset measures electrical brain activity and helps researchers identify mental states including attention, surprise, intention, and recognition of mistakes. Consequently, developers hope these insights will improve robotic decision-making during complex physical tasks.
Currently, both companies are evaluating whether this additional information genuinely improves robotic learning. Instead of immediately expanding the project, they first plan to compare model performance using datasets that include brain activity alongside traditional visual recordings.
Researchers believe changing levels of brain activity may reveal when humans apply greater concentration during difficult tasks. Therefore, robotics developers could identify situations requiring more advanced reasoning or additional computational effort inside future AI systems.
Encord originally focused on helping companies organize, label, and evaluate machine vision datasets. However, customer demand shifted as robotics companies increasingly adopted end-to-end AI learning for manipulation tasks. As a result, Encord expanded into producing entirely new datasets rather than simply managing existing information.
According to company leaders, sufficient robotics training data simply does not exist today. Unlike internet text, physical interactions require people, equipment, locations, and repeated demonstrations before developers can create valuable datasets for AI training.
Although developers often use simulation and recorded videos, these approaches cannot fully replicate real-world conditions. Consequently, companies continue investing in human demonstrations that capture natural movements, object handling, and physical decision-making during everyday activities.
Many robotics firms now rely on egocentric video collected from wearable cameras attached to workers. These recordings show tasks directly from the operator’s perspective while additional cameras capture surrounding movements for greater context and accuracy.
Encord also collects remote robot operation data alongside wearable recordings. Meanwhile, its California facility continues testing emerging technologies including muscle sensors attached to workers’ forearms. These sensors detect electrical muscle signals, allowing researchers to estimate hand positions even when cameras cannot capture every movement.
Inside the facility, operators complete numerous household and industrial activities. They pour coffee, stack poker chips, organize household items, manipulate cables, and perform other detailed tasks requiring precision and coordination. Each demonstration helps create valuable examples for future robotics systems.
Every dataset also includes detailed descriptions explaining exactly what occurs throughout each action. These annotations help AI systems understand not only movement but also the purpose behind every physical interaction. Consequently, developers expect stronger learning performance from carefully labeled information.
Even so, producing these specialized datasets remains expensive compared with collecting internet text for language models. Every robotics demonstration requires trained personnel, specialized equipment, and careful quality control before engineers can use the resulting information.
Despite these higher costs, industry leaders increasingly believe premium datasets offer stronger long-term value than massive quantities of lower-quality recordings. Therefore, companies continue investing in better collection methods as competition in physical AI accelerates.
Encord believes its position between multiple robotics developers provides valuable insight into industry trends. By observing successful data collection techniques across different customers, the company hopes to refine future datasets while supporting increasingly capable robotic systems.
As robotics technology advances, companies continue searching for innovative methods to improve machine learning. If ongoing testing proves successful, Brain Wave Data may become another valuable tool helping robots better understand complex human actions and perform physical tasks with greater accuracy.
