CrossBFM: Distilling a Shared Latent Behavior Space Across Humanoid Embodiments
Behavior Foundation Models (BFMs) give humanoids a promptable policy over a latent behavior space, enabling one single vector to represent a motion to imitate, a pose to reach, or a reward to maximize. Forward-Backward representations successfully produce such spaces, but at the cost of hundreds of GPU-hours for a single robot. Moreover, when the training process is repeated fo
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Behavior Foundation Models (BFMs) give humanoids a promptable policy over a latent behavior space, enabling one single vector to represent a motion to imitate, a pose to reach, or a reward to maximize. Forward-Backward representations successfully produce such spaces, but at the cost of hundreds of GPU-hours for a single robot. Moreover, when the training process is repeated fo
This research addresses a critical challenge in robotics by enabling the transfer of learned behaviors across different humanoid embodiments, significantly reducing training costs and accelerating the development of versatile robotic agents. For ML engineers, understanding how to create and leverage shared latent spaces for complex behaviors across diverse hardware is key to building scalable and adaptable AI systems.