Do Robotic World Models Really Follow Actions? Diagnosing and Aligning Action-Conditioned Generation for Policy Learning

Key points

In addition to demonstrated actions, WorldEcho probes world models with four kinds of non-expert action queries: replay from other states, local perturbations, policy rollouts, and sampling from the feasible space.
WorldSync broadens the distribution of action outcomes, anchors intermediate video representations to robot motion with an Action-Forcing Expert, and aligns changes caused by altering actions with changes in the actual future.
Across 50 RoboTwin tasks, WorldSync achieved the lowest error accounting for visual integrity, 0.0661, and the highest video pass rate, 84.51%.
Using this model as a learned simulator for two rounds of policy improvement raised simulated success rates from 51–52% to 65% and real-robot cup stacking from 48% to 68%, exceeding CtrlWorld by 8–9 and 12 points, respectively.
However, its advantage was uneven across individual metrics: Cosmos-Predict2.5 achieved a better raw NDTW of 0.013, and the authors stated that extending the approach to long-duration interactions and multiple robot embodiments remains future work.

External research summaries. These are not HDATF publications or measured product results.