World model precision depends on planning task and search strategy
What Must a World Model Distinguish for Planning?
Machine LearningRobotics
Summary
When computers plan actions by imagining what will happen, they don’t always need to remember every little physical detail. The authors explain how different planning goals require different levels of detail in these world models. Sometimes a rough idea is enough for making decisions, while other times more detail is necessary. They also show that how the planning process searches for good actions affects what details the model needs to keep. Their experiments with robots and physics help understand how these ideas work in practice.
What this means in practice
- •For robotic system designers: Design world models that adapt detail level based on task requirements to improve robot planning efficiency and accuracy.
- •For game ai developers: Create game AI that selectively simulates environments with necessary detail for decision making, making planning faster without reducing quality.
Authors
Rongzhe Wei, Hans Hao-Hsun Hsu, Peizhi Niu, Yifan Li, Pan Li
Abstract
World models simulate the consequences of action candidates, but good planning need not preserve every physical distinction required for accurate prediction. We formalize this gap through a hierarchy of mechanism, response, and decision sufficiency. Given a candidate set, the planning query determines which physical variations matter and how precisely they must be preserved: coarse decisions can discard much of the information needed for prediction, whereas fine decisions may require nearly the same resolution. In practice, planners often adaptively search to construct candidates, and information unnecessary for final selection may still be needed to discover good candidates. What a world model must preserve therefore depends on the query, the candidate set, and the planner. We study these effects in a collision system, nonlinear dynamics, and robotic planning. These varying requirements raise a design question: where should query information enter the planning system? A model that jointly generates actions and outcomes conditioned on the query achieves lower regret than an action-conditioned world model on seen objectives, but this advantage largely disappears when generalizing to unseen objectives. Motivated by this, we propose a modular design in which the query determines where to look and an action-conditioned model predicts what will happen, allowing the same predictions to be reused across objectives.