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What a week at @NVIDIAGTC.

New partnerships with @Teradyneinc, @Universal_Robot, @nebiusai, @nvidia, @cevalogistics, and @Zalando. Cortex live on the floor handling shoebox returns, and a spot in @JensenHuang’s keynote.

All while shipping more robots than ever.

#GTCBerlin

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One of our favorite moments from the FLEX research: the grasp slips, and the robot tries again.

That recovery isn’t in any of the demonstrations. Nobody taught Cortex, the AI model behind our robots, to do it. It learned through practice.

@sigmundhh is presenting the FLEX paper at the R2RL workshop at @corl_conf this Thursday, Nov 12. Poster session 10:30–11:00. If you’re in Austin, come say hi and hear how robots can learn from their own practice.

#CoRL2026

Great to have the @NVIDIARobotics team at our office to see Cortex, our physical AI model, in action. Already looking forward to the next visit.

Before practice: 60–70%. After practice: 85–100%.

That’s what FLEX, our new learning method, did for Cortex 2.6. The robot improved from a few dozen attempts of its own, with nobody stepping in.

Why it matters: today’s robot models, like VLAs and world-action models, learn almost entirely by imitation. That produces impressive behavior, but not reliable behavior. One small mistake puts the robot somewhere no demonstration covered, and errors compound.

FLEX allows the robot to learn from its own experience, building on what the pretrained model already knows. This sets a clear course toward robots that learn efficiently while producing value in the real world.

Full results in link below.