World models for robotic manipulation: A survey

Analysis

Why This Paper Matters

World models—learned predictive models of an environment's dynamics—are a foundational idea in model-based reinforcement learning and embodied AI. For robotic manipulation, the ability to predict how objects will move in response to actions is central to planning, control, and sample-efficient learning. This survey addresses a timely need: as world models grow in capability, the literature has become fragmented across task-specific and general approaches, making it difficult for practitioners to see the bigger picture.

By focusing specifically on robotic manipulation, the survey carves out a domain where physical interaction, contact dynamics, and high-dimensional visual observations create distinct challenges. This focus is valuable because manipulation is one of the most commercially and scientifically important applications of embodied AI, from warehouse automation to household robotics.

The survey's framing of world models as evolving from task-specific predictors toward more general systems suggests a maturing field. This narrative helps researchers position their work and anticipate where the field is heading, which is especially useful for practitioners deciding where to invest effort.

Technical Contributions

• Comprehensive survey scope: The paper surveys world models for robotic manipulation, consolidating methods that predict future states or observations to support manipulation.
• Evolutionary framing: It identifies a trend from task-specific world models—trained for narrow skills—toward more general models that can transfer across tasks.
• Capability-centric view: World models are presented as a means to provide predictive capability, connecting them to planning and control in manipulation.
• Organization of the field: The survey structures existing work, likely categorizing approaches by representation, learning objective, or application, though the truncated abstract limits detail.

Results

As a survey, this paper does not report new experimental metrics or benchmark comparisons. Its primary result is a synthesized observation: world models for robotic manipulation are evolving from task-specific to more general formulations. The abstract provides no quantitative evidence, and the paper has zero citations at the time of analysis, so its impact cannot yet be measured. Readers should consult the full text for detailed comparisons and any reported performance trends.

Significance

The broader significance of this survey lies in its potential to accelerate progress in embodied AI. By mapping the landscape of world models for manipulation, it can help researchers identify gaps, avoid redundant work, and build on common foundations. If the trend toward general world models continues, it could enable robots to learn manipulation skills more efficiently and transfer them across tasks—a key step toward versatile, autonomous systems. The survey also highlights the importance of predictive modeling as a bridge between reinforcement learning and robotics, reinforcing a direction that may shape future research in both fields.