arXiv:2605. 08732v2 Announce Type: replace-cross Abstract: Modern vision-based world models can represent observations as compact yet expressive latent manifolds, but fast goal-oriented planning in these spaces remains challenging.
arXiv:2606.23079v2 Announce Type: replace-cross Abstract: Neural world models coupled with model predictive control (MPC) replan at every environment step to bound accumulated prediction error, but t...
Neural world models coupled with model predictive control (MPC) replan at every environment step to bound accumulated prediction error, but this incurs substantial computational overhead. Reusing a cached plan reduces this overhead, yet its effectiveness depends on how prediction mismatch propagates through the local dynamics.
The Representation World Model (RWM) learns states, transitions, and executable plans directly within a representation space, bypassing traditional explicit dynamics models and action-space search. It uses inverse-dynamics supervision along latent paths to shape the representation geometry, enabling direct planning by constructing a latent path between current and goal states and recovering actions via inverse dynamics. Experiments on continuous-control benchmarks and robotic manipulation tasks demonstrate RWM’s effectiveness and potential for complex embodied control.
arXiv:2604. 03208v2 Announce Type: replace Abstract: World models are a promising path to zero-shot embodied control through planning.
arXiv:2603. 12231v2 Announce Type: replace Abstract: Learning good representations is essential for latent planning with world models.
arXiv:2608. 16287v1 Announce Type: new Abstract: Joint-embedding predictive world models plan by scoring predicted terminal embeddings against a goal embedding using a cost defined on the representation itself.
arXiv:2608. 14125v1 Announce Type: new Abstract: LeWM is a lightweight visual world model that learns latent dynamics end-to-end from pixels and ranks candidate action sequences by the distance between their predicted endpoints and the goal.
arXiv:2608.24855v1 Announce Type: new Abstract: Latent world models are inherently strong encoders that transform image pixel to latent embedding, yet existing world models still rely on online traje...
The paper introduces the Dual-Latent World Model (Dual-WM), which separates local execution and long-range planning into distinct latent spaces and dynamics models. A new learning method, Long-Horizon Representation Learning with Weighted Rollout (LoRe), supervises predictions at both levels using exponential horizon weights. Experiments on five goal-conditioned visual control tasks show that Dual-WM improves success rates over strong baselines, especially at longer horizons.
arXiv:2609.13845v1 Announce Type: cross Abstract: World models trained with joint-embedding predictive architectures learn compact, structured latent representations from physical interaction, yet pl...
The paper introduces SCALE (State-CAlibrated Latent Embeddings), a technique that aligns pairwise latent distances with task-relevant state-space distances in joint-embedding predictive world models. By adding SCALE to the LeWorldModel (LeWM) objective, the authors preserve LeWM’s architecture while ensuring that latent geometry reflects meaningful task outcomes. Experiments demonstrate that SCALE improves planning success across manipulation and navigation tasks with various solvers, and the authors analyze how the method reshapes representation geometry to support planning.