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Toward Embodied World Agents via Embodied-Planning Dataset and Interactive World Models

NeurIPS 2026 · Multi-agent systems and games › Human-AI and goal-conditioned · approach: Embodied agent learning

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Proposes an embodied-planning dataset and interactive world models toward general embodied world agents operating across diverse 3D worlds.

Problem

Most embodied agents are confined to specific tasks in a single scene and transfer poorly to new environments; general embodied agents for open-world settings remain under-explored.

Approach

Combines an embodied-planning dataset with interactive world models to build agents that perceive, reason, and act across diverse 3D worlds. Details beyond the abstract are not available.

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