From Imagined Futures to Executable Actions: Mixture of Latent Actions for Robot Manipulation

TL;DR

This paper introduces MoLA, a control-oriented framework that converts imagined future videos into executable actions for robot manipulation by using a mixture of inverse dynamics models.

Contribution

MoLA leverages a mixture of pretrained inverse dynamics models to transform visual predictions into structured, action-centric representations, improving control stability and generalization.

Findings

Achieves higher task success rates on simulated benchmarks.

Improves temporal consistency in robot actions.

Demonstrates effective real-world robot manipulation.

Abstract

Video generation models offer a promising imagination mechanism for robot manipulation by predicting long-horizon future observations, but effectively exploiting these imagined futures for action execution remains challenging. Existing approaches either condition policies on predicted frames or directly decode generated videos into actions, both suffering from a mismatch between visual realism and control relevance. As a result, predicted observations emphasize perceptual fidelity rather than action-centric causes of state transitions, leading to indirect and unstable control. To address this gap, we propose MoLA (Mixture of Latent Actions), a control-oriented interface that transforms imagined future videos into executable representations. Instead of passing predicted frames directly to the policy, MoLA leverages a mixture of pretrained inverse dynamics models to infer a mixture of latent actions implied by generated visual transitions. These modality-aware inverse dynamics models capture complementary semantic, depth, and flow cues, providing a structured and physically grounded action representation that bridges video imagination and policy execution. We evaluate our approach on simulated benchmarks (LIBERO, CALVIN, and LIBERO-Plus) and real-world robot manipulation tasks, achieving consistent gains in task success, temporal consistency, and generalization.