CausalWAM identifies true causes of actions for better robot control
CausalWAM: Learning the Causes of Actions in World Action Models
Artificial IntelligenceRobotics
Summary
Predicting what happens in a scene and deciding which parts actually cause actions is hard. The authors show that if you make the model pick only a few important causes, it can figure out exactly which parts really drive actions. They build a system called CausalWAM that fixes this in existing models by separating cause from other details. This helps robots and simulations act more efficiently and accurately when learning from examples.
What this means in practice
- •For robot developers: Improve robot learning by identifying which hidden variables truly cause actions, leading to more effective control in real-world tasks.
- •For simulation engineers: Enhance scene prediction models by separating cause of actions from other changes, enabling more efficient action generation in simulated environments.
Authors
Juntao Cheng, Jinzhou Tang, Fan Feng, Henrik I. Christensen, Yujia Zheng
Abstract
World action models (WAMs) learn latent variables from observations and use these to both predict the evolution of the scene and to generate actions. The prediction has to be of the whole scene, whereas only a portion of the scene causes actions. WAMs fitted to demonstrations do not, in general, guarantee that they will generate actions solely from the latent causes of the demonstrated actions (the action-relevant latent variables), as the model could just as well learn to imitate in this way whilst entangling the action-relevant latent variables with the nuisance variables. We therefore separate out what causes action from what prediction depends on. We prove that under a sparsity constraint the latent variables used to generate actions are an invertible function of the action-relevant latent variables alone and the true structure is identified up to a permutation of the latent variables. This requires neither sufficient temporal dependence nor any assumptions on how the unobserved regimes evolve and holds even when the action-relevant latent variables are statistically dependent on the rest. We therefore propose CWAM (Causal WAM), which incorporates the constraint into a pretrained WAM by adding a sparse routing path to its action head, with the world modeling objective remaining unchanged. Experiments on both synthetic data and real-world robots validate the theory as well as showcase the improved efficiency in closed-loop success.