Publication
CrossBFM: Distilling a Shared Latent Behavior Space Across Humanoid Embodiments
Tan-Dzung Do; Phuong Tuan Dat; Nico Bohlinger; Cuc T. Trinh; Siwei Ju; Vien Anh Ngo; Jan Peters; Xinchao Wang; An T. Le
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2609.38087, Pages 1-23, arXiv, 2026.
Abstract
Behavior Foundation Models (BFMs) give humanoids a promptable policy over
a latent behavior space, enabling one single vector to represent a motion to im-
itate, a pose to reach, or a reward to maximize. Forward-Backward representa-
tions successfully produce such spaces, but at the cost of hundreds of GPU-hours
for a single robot. Moreover, when the training process is repeated for a second
robot, it produces a second space unrelated to the first, resulting in embodiment-
specific latents that do not unify or transfer. We address these problems with
CROSSBFM, treating the latent space as the transferable asset for various embod-
iments. As retargeting provides frame-level cross-embodiment correspondence,
we propose a unified encoder architecture with no robot-specific parameters for
distilling the behavior space to address all training embodiments simultaneously
in less than a GPU-hour. Following this encoder, latent-conditioned trackers turn
the distilled latent into whole-body control in a conventional PPO training manner
in just 10 more GPU-hours. On three distilled humanoids, all three prompting
modes transfer: motion tracking with latent-conditioned policy losing only 0.025
rad to its joint-conditioned counterpart, smooth goal reaching between poses with
no falls, and reward optimization for all 41 reward prompts. Our experiments
further reveal that 1) regressing the encoder on a quarter of the motion corpus
costs only 5% of tracking performance and 2) training the encoder on a subset
of robots and evaluating on an unseen one recovers up to 89% of the tracking
performance of seen robots, demonstrating cross-embodiment generalization to
morphologically similar robots. We also verify the pipeline on real robots across
all three prompting modes and with flow-based generated latents.