Social Humanoid enables coordinated human-humanoid interactions
Social Humanoid generates socially responsive whole-body motion for Unitree G1 humanoid robot across 260 hours of interactions.
Social Humanoid generates socially responsive whole-body motion for Unitree G1 humanoid robot across 260 hours of interactions.
On 3,000 LIBERO-Pro episodes it reports 95.9% success, 25% fewer control steps, and 17% less control time than waiting on the brain.
On a Unitree G1, the method supports button presses and slider moves at millimeter scale.
The three-stage pipeline reports state-of-the-art simulation results and higher Unitree G1 pick-and-drop success.
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.
NVIDIA showed developers using frontier AI agents with Omniverse libraries to build robotics, driving, and digital-twin simulations.
Contextual safety filtering grounds language rules in trajectories and cuts motion danger events by up to 90%.
FAITH filters robot actions with a learned safety value, preserving high task return without feasible-start safety violations.
Reflex enables a Unitree G1 to catch thrown boxes from egocentric RGB-D, with 65% success at 2.5 meters.
Long-WAM scales robot world-action context, lifting RoboCasa success from 63.3% to 78.7%.
Long-WAM raises robot task success by scaling causal world-action context with autoregressive video pretraining.
InterEvolve evolves reward programs at test time so humanoid controllers solve new loco-manipulation tasks without retraining.
GGSD uses game self-play to learn a small set of human-playable motor skills for embodied agents.
Stanford and Caltech's HomeBody uses GPT-6 Astra to let a Unitree G1 tidy an unfamiliar kitchen.
Rolling-WAM speeds robotic replanning 4.5x by spreading joint denoising across successive cycles.
Rolling-WAM speeds robot replanning 4.5x by staggering joint video-action denoising across cycles.
ForgetMimic unlearns selected humanoid motions on Unitree robots while keeping other behaviors.
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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