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Axis Robotics
2026-08-12 07:39:00

Axis Robotics founder says robot training data is drawing capital fast as Physical AI demand widens

Robot training data has moved from a quiet infrastructure layer into one of the busiest segments tied to embodied AI, and Axis Robotics wants to position itself at the center of that shift. In an interview with PANews, founder Chris Feng said the core issue is the scale of the data gap: while GPT-3 needed about 15 million hours of human internet data, robotics may require 100 million hours or more because physical-world tasks involve space, motion, object states, and the consequences of actions in changing environments. Feng described today’s data supply stack as a tradeoff between three main sources: real-robot teleoperation, simulation, and first-person human video. Real-robot data offers the closest match to deployment hardware but is expensive, with one hour of high-quality teleoperation data costing as much as $200 by industry estimates cited in the interview. Simulation scales better and is easier to validate, though the sim-to-real gap remains a central constraint. First-person data can expand coverage across real environments at lower cost, but its value depends heavily on future robot form factors and camera placement. Axis says it has collected more than 2.2 million robot trajectories and 28,000 cumulative data hours through browser-based teleoperation. The company also uses blockchain on Base to record relationships between tasks, verified data, and contributors, aiming to support incentive design and provenance tracking. Over the next six to 12 months, Axis plans to broaden its data products, triple its community size in half a year, and raise annual recurring revenue to between $500,000 and $1 million while trying to join supplier lists at leading robot model companies.

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Axis Robotics founder says robot training data is drawing capital fast as Physical AI demand widens
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NVIDIA
2026-08-07 00:20:04

NVIDIA unveils Alpamayo 2 Super and spotlights Taiwan partners in robotics push

NVIDIA has open-sourced Alpamayo 2 Super, a new inference model for autonomous driving and embodied AI, as the company sharpens its push into physical AI and robotics. The model has 34 billion parameters in total, combining a 32 billion-parameter NVIDIA Cosmos 3 Super Reasoner visual-language reasoning core with a 2 billion-parameter Diffusion Action Expert. According to the company, Alpamayo 2 Super adds Chain-of-Causation reasoning, allowing it to simulate edge cases in a virtual physical world within milliseconds and derive optimal decisions. It scored 79.2 on the LingoQA autonomous driving benchmark. CEO Jensen Huang said physical AI and robotics represent the next major wave of artificial intelligence, adding that self-driving vehicles are essentially robots with four wheels. The report also mapped out Taiwan’s role in NVIDIA’s ecosystem expansion, naming TSMC for foundry work on chips including Thor and Jetson, Foxconn and Quanta for system integration and mass assembly, and several industrial PC makers for edge control hardware. Other companies cited include Solomon in 3D AI vision sensing, HIWIN and related firms in precision motion components, Mirle in pneumatic control, and Delta Electronics in lightweight power modules. The article added that the 2026 Taiwan Automation Intelligence and Robot Show, scheduled for Aug. 19, could add to market attention on the sector.

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