RoboStereo: Dual-Tower 4D Embodied World Models for Unified Policy Optimization

arXiv cs.CV / 3/16/2026

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Key Points

RoboStereo introduces a symmetric dual-tower 4D embodied world model with bidirectional cross-modal enhancement to ensure spatiotemporal geometric consistency and reduce physics hallucinations during imagined rollouts.
The paper presents the first unified framework for world-model-based policy optimization, including Test-Time Policy Augmentation (TTPA), Imitative-Evolutionary Policy Learning (IEPL), and Open-Exploration Policy Learning (OEPL).
Experiments report state-of-the-art generation quality and over 97% average relative improvement on fine-grained manipulation tasks, demonstrating the effectiveness of the unified approach.
The work has implications for scalable embodied AI research and downstream robotics and policy-learning workflows by enabling safer verification, improved imitation learning, and autonomous skill discovery.

Abstract

Scalable Embodied AI faces fundamental constraints due to prohibitive costs and safety risks of real-world interaction. While Embodied World Models (EWMs) offer promise through imagined rollouts, existing approaches suffer from geometric hallucinations and lack unified optimization frameworks for practical policy improvement. We introduce RoboStereo, a symmetric dual-tower 4D world model that employs bidirectional cross-modal enhancement to ensure spatiotemporal geometric consistency and alleviate physics hallucinations. Building upon this high-fidelity 4D simulator, we present the first unified framework for world-model-based policy optimization: (1) Test-Time Policy Augmentation (TTPA) for pre-execution verification, (2) Imitative-Evolutionary Policy Learning (IEPL) leveraging visual perceptual rewards to learn from expert demonstrations, and (3) Open-Exploration Policy Learning (OEPL) enabling autonomous skill discovery and self-correction. Comprehensive experiments demonstrate RoboStereo achieves state-of-the-art generation quality, with our unified framework delivering >97% average relative improvement on fine-grained manipulation tasks.