One Space to Control Them All: Unifying Robot Control with Shared Learning
![The architecture learns a unified latent representation of motion by decoupling a shared latent space into five body-segment subspaces - left arm, right arm, trunk, left leg, and right leg - and employing robot-specific embedding layers [latex]E_r[/latex] to project varying pose dimensionalities into a common feature space, subsequently reconstructed via inverse mappings [latex]D_r[/latex], thereby enabling cross-embodiment motion modeling across humans and diverse robotic platforms.](https://arxiv.org/html/2601.15419v1/figures/modeloverview.png)
Researchers have developed a new framework that allows robots with different physical forms to share a common understanding of movement, simplifying cross-embodiment control.
![The architecture learns a unified latent representation of motion by decoupling a shared latent space into five body-segment subspaces - left arm, right arm, trunk, left leg, and right leg - and employing robot-specific embedding layers [latex]E_r[/latex] to project varying pose dimensionalities into a common feature space, subsequently reconstructed via inverse mappings [latex]D_r[/latex], thereby enabling cross-embodiment motion modeling across humans and diverse robotic platforms.](https://arxiv.org/html/2601.15419v1/figures/modeloverview.png)
Researchers have developed a new framework that allows robots with different physical forms to share a common understanding of movement, simplifying cross-embodiment control.
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