Modeling and Understanding Neural Weights of Humanoid Policies with Diffusion
Researchers generate deployable Unitree G1 humanoid controllers by diffusing learned policy weights.
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Researchers generate deployable Unitree G1 humanoid controllers by diffusing learned policy weights.
Zero-shot transfer to Unitree G1 reached 90.0% on SIMPLE and 62.5% on real loco-manipulation tasks.
Reflex enables a Unitree G1 to catch thrown boxes from egocentric RGB-D, with 65% success at 2.5 meters.
A flow-matching student distilled by DAgger runs on proprioception alone and was tested on the Unitree-G1.
Researchers present TANGO, a whole-body vision-language-action model enabling humanoid robots to traverse cluttered spaces from language instructions.
Researchers introduce TANGO, a whole-body vision-language-action model enabling zero-shot language-guided humanoid navigation on the Unitree G1 robot.

Researcher chained two Unitree G1 flaws (CVE-2026-76639, CVE-2026-76640) to gain unauthenticated root access, with compromised robots able to infect others via Bluetooth.
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