Robotic Manipulation › Summary

• Contact can change the relevant dynamics or make outcomes uncertain, but not every contact model has a hard discontinuity or requires distributional output.
• Explicit, latent, object-centric, and hybrid states trade off observability, structure, and estimation error; none dominates by definition.
• Grasping, pushing, and dexterous manipulation overlap and may call for different predictors or a shared model, depending on the task.
• Visual foresight predicts action-conditioned observations, not hidden physical state; its planning value must be tested downstream.
• Affordance estimates can be learned without a world model. If paired with one, compare their predicted effects rather than assuming consistency.
• The code builds a miniature planar pushing model and a naive baseline. Its contact feature gives only a marginal one-step gain and slightly worse mean rollout error in this seeded run; the imperfect model is also used for a coarse plan and an illustrative affordance map.
• Separate one-step prediction, recursive prediction, and closed-loop task success, as in Part XI.

The planned next chapter, Locomotion, Humanoids, and Navigation, examines whole-body contact and spatial control within Part X.