# Humanoid Whole-Body Control

Humanoids asked to sit down or walk to a named object need paired supervision mapping pixels and free-form language to whole-body joint commands, and that data has so far come from teleoperation or wearable capture, where an hour of data costs at least an hour of human time. HALO moves data generation into simulation, synthesizing egocentric vision-language-motion episodes across a thousand procedurally generated homes, then uses a layered stack that splits a free-form request into subtasks, generates five-second whole-body motion plans, and executes them with a perceptive tracker. Without seeing a real home in training, it transfers zero-shot to six unscanned, unmapped real homes, succeeding in 91% of 360 trials.

Humanoids asked to sit down or walk to a named object need paired supervision mapping pixels and free-form language to whole-body joint commands, and that data has so far come from teleoperation or wearable capture, where an hour of data costs at least an hour of human time. HALO moves data generation into simulation, synthesizing egocentric vision-language-motion episodes across a thousand procedurally generated homes, then uses a layered stack that splits a free-form request into subtasks, generates five-second whole-body motion plans, and executes them with a perceptive tracker. Without seeing a real home in training, it transfers zero-shot to six unscanned, unmapped real homes, succeeding in 91% of 360 trials.

CrossBFM freezes the backward map of a Behavior Foundation Model pretrained on the Unitree G1 and, using the frame-level cross-embodiment correspondence supplied by retargeted data, distills that latent space onto three humanoids (Inhouse M3, Booster T1, Fourier N1) with a unified encoder that has no robot-specific parameters, training the encoder in under one GPU-hour and each tracker in about 10 more GPU-hours; all three prompting modes transfer (motion tracking, goal reaching between poses, reward optimization), the latent-conditioned policy loses only 0.

CrossBFM freezes the backward map of a Behavior Foundation Model pretrained on the Unitree G1 and, using the frame-level cross-embodiment correspondence supplied by retargeted data, distills that latent space onto three humanoids (Inhouse M3, Booster T1, Fourier N1) with a unified encoder that has no robot-specific parameters, training the encoder in under one GPU-hour and each tracker in about 10 more GPU-hours; all three prompting modes transfer (motion tracking, goal reaching between poses, reward optimization), the latent-conditioned policy loses only 0.