Lifting Embodied World Models for Planning and Control
World models of embodied agents predict future observations conditioned on an action taken by the agent. For complex embodiments, action spaces are high-dimensional and difficult to specify: for example, precisely controlling a human agent requires specifying the motion of each joint. This makes the world model hard to control and expensive to plan with as search-based methods like CEM scale poorly with action dimensionality. To address this issue, we train a lightweight policy that maps high-level actions to sequences of low-level joint actions. Composing this policy with the frozen world model produces a lifted world model that predicts a sequence of future observations from a single hi...
| Authors | Alex N. Wang, Trevor Darrell, Pavel Izmailov et al. |
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| Primary category | cs.CV |
| Published | 28 April 2026 |
| Last updated at source | 21 July 2026 |
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