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SOURCE-LINKED SIGNAL / PAPER / RESEARCH

Skeleton-Grounded Latent Action Model for Robot World Models

SkeLAM is a skeleton-guided latent action model for controllable robot world modeling. It decomposes latent actions into a kinematic component supervised by robot skeletons and a residual component capturing other visual dynamics. Cross-modal swap reconstruction makes RGB-derived and skeleton-derived kinematic representations interchangeable, and a lightweight adapter maps robot actions into the kinematic space. The paper reports results on DROID relative to DreamDojo, including lower end-effector ADE and LPIPS.

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  • Fetched source page+30
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  • Original non-discussion link+5

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What the source says

SkeLAM is a skeleton-guided latent action model for controllable robot world modeling. It decomposes latent actions into a kinematic component supervised by robot skeletons and a residual component capturing other visual dynamics. Cross-modal swap reconstruction makes RGB-derived and skeleton-derived kinematic representations interchangeable, and a lightweight adapter maps robot actions into the kinematic space. The paper reports results on DROID relative to DreamDojo, including lower end-effector ADE and LPIPS.

Key findings from the stored brief

  • Latent actions are split into a skeleton-grounded kinematic component and a residual visual-dynamics component.
  • On DROID, the paper reports 19.5% lower end-effector ADE and 11.1% lower LPIPS than DreamDojo.
  • The paper reports zero-shot human-to-robot motion transfer in generated rollouts.
  • Policy evaluation reportedly shows agreement between predicted and real-robot success rates.
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Source claims not independently checked; reported gains are from the authors' own comparisons on DROID.

This is a source-based abstract, not independent validation of the source's claims.

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