Skeleton-Grounded Latent Action Model for Robot World Models

arXiv 2026 38.3 method, application

TLDR

SkeLAM decomposes latent actions into skeleton-guided kinematic and residual components, enabling controllable robot world models that generate future observations from actions.

Reasoning

The paper introduces a novel skeleton-guided latent action decomposition with cross-modal swap reconstruction and adapter-based action conditioning, supported by strong DROID benchmark results and zero-shot transfer. Weaknesses include limited discussion of limitations and the abstract truncating policy evaluation details.

Read-first score

Read-first score 38.3, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 53.

Recency 5%

100

Uses a gentle age decay so recent papers surface without erasing older foundations. 2026

Topical relevance 25%

75.7

Uses existing LLM keyword relevance scores normalized to 0-100. world model,world simulator,generative world model,interactive world model,video world model,world dynamics prediction,model-based reinforcement learning world model

Methodology quality 15%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=evaluation,result

Reproducibility 15%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=none

Graph prestige 15%

5.7

PageRank-style prestige over the local citation graph built from available OpenAlex references. local_pagerank=0.0016

Citation impact 15%

Uses OpenAlex-shaped citation metadata as a bibliometric attention signal, separate from paper quality. cited_by_count=0

Citation velocity 10%

Citation velocity estimates citations per publication-year to reduce old-paper bias. velocity=0.00

Field roles

Frontier

Rank sensitivity

Stability: volatile; rank range: 498.

Keyword Scores

world model

world dynamics prediction

generative world model

interactive world model

video world model

world simulator

model-based reinforcement learning world model

Deep Analysis

Innovations

Decomposes latent actions into a skeleton-grounded kinematic component and a residual component to separate robot motion from complementary visual dynamics.
Uses cross-modal swap reconstruction to encourage RGB-derived and skeleton-derived kinematic representations to be interchangeable during video reconstruction.
Introduces separate conditioning pathways and branch dropout to reduce dependence on future-derived residual information.
Maps robot actions to kinematic space via a lightweight adapter, followed by world-model fine-tuning for controllable generation.

Methodology

SkeLAM learns robot world models from videos on DROID by decomposing latent actions into skeleton-supervised kinematic and residual components. It uses cross-modal swap reconstruction, separate conditioning pathways, and branch dropout, then maps robot actions to kinematic space with a lightweight adapter and fine-tunes the world model. Evaluation compares against DreamDojo using end-effector ADE and LPIPS, with motion probing, qualitative transfer, and real-robot policy success agreement.

Key Results

On DROID, SkeLAM reduces end-effector ADE by 19.5% and LPIPS by 11.1% compared with DreamDojo. Motion probing supports kinematic specialization, qualitative results demonstrate zero-shot human-to-robot motion transfer, and policy evaluation shows agreement between predicted and real-robot success rates.

Resources

Paper