Are brain waves the next unlock for physical AI?
Forget YouTube videos-frontier physical AI models need multiple camera angles, dense annotation, and soon, brain wave readings. The frontier of physical AI is a Jenga game in a warehouse in San Leandro, California. That warehouse is occupied by Encord , a company that builds data tooling used to train AI models.
Key Takeaways
- Andrew Ceja is a pilot-the company's term for its robotic trainers-and he's carefully pulling wooden blocks from a tottering tower while wearing a headset with a camera that tracks what he sees.
That alone is fairly common for collecting robot training data, but this headset includes sensors that measure his brain waves as he carefully disassembles the block tower.
- The brain wave headset Ceja is wearing was built by Zander Labs , a German neuroscience startup that's betting measuring brain activity - to deduce mental states like error, intent and surprise - can create a more useful data set to train models.
Encord's work with Zander is currently a trial run; Encord says the goal is to build an initial brain wave-tagged data set, run it through customer robotics models, and evaluate whether it actually improves performance before deciding whether to scale it up.
- Encord was founded to help companies building machine-vision applications annotate data and evaluate models.
- Finding the same raw materials to teach neural networks about physical manipulation is challenging: self-driving car companies collect it themselves, but that's hard to scale.
Training from video can work, but it lacks the fidelity of real world data.
- When TechCrunch visited, pilots were using leader-follower rigs - paired robotic arms, one controlled directly by a human operator and one that mimics its movements -to create data about tasks like pouring coffee from a pot into mugs (very sloshy) and stacking poker chips.
Andrew Ceja is a pilot-the company's term for its robotic trainers-and he's carefully pulling wooden blocks from a tottering tower while wearing a headset with a camera that tracks what he sees. That alone is fairly common for collecting robot training data, but this headset includes sensors that measure his brain waves as he carefully disassembles the block tower. Encord is one of a small but growing number of startups betting that the next real constraint on humanoid and warehouse robotics won't be model architecture but instead the sheer scarcity of real-world physical training data.
Rather than just helping robotics companies manage the data they have, Encord is building a business around manufacturing the data they don't. The brain wave headset Ceja is wearing was built by Zander Labs , a German neuroscience startup that's betting measuring brain activity - to deduce mental states like error, intent and surprise - can create a more useful data set to train models. Encord's work with Zander is currently a trial run; Encord says the goal is to build an initial brain wave-tagged data set, run it through customer robotics models, and evaluate whether it actually improves performance before deciding whether to scale it up.
Lucas Gehrke, a Zander neuroscientist supervising the work, says that the amount of brain activity used at any point during a given task offers clues for model builders trying to figure out when they need to deploy their highest-effort models. This is the "bleeding edge" of the effort to solve the robotics data bottleneck, according to Vineeth Velmurugan, Encord's head of robot learning. A veteran of OpenAI's robot lab and Berkshire Grey, the warehouse automation firm, Velmurugan joined Encord to build the company's internal data-creation team.
For more details please read the original article at TechCrunch AI.
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