Beyond One-Step Accuracy: State-Affine Latent Transition for Reliable Visual Planning
Joint-embedding world models enable visual planning by learning action-conditioned dynamics in latent space. Yet they are commonly trained for one-step prediction on encoded states, while planning recursively applies the learned transition to its own predictions. One-step accuracy therefore does not capture how prediction errors propagate under recursive rollout. We decompose multi-step rollout error into the errors introduced at individual steps and their propagation through subsequent transitions. We show that state-affine dynamics are precisely the differentiable transitions with state-independent Jacobians, eliminating the nonlinear propagation residual and making the error propagatio...
| Authors | Boyuan Zhang, Yingjun Du, Xiantong Zhen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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