GenEx: Generating an Explorable World (2412.09624v3)

Abstract: Understanding, navigating, and exploring the 3D physical real world has long been a central challenge in the development of artificial intelligence. In this work, we take a step toward this goal by introducing GenEx, a system capable of planning complex embodied world exploration, guided by its generative imagination that forms priors (expectations) about the surrounding environments. GenEx generates an entire 3D-consistent imaginative environment from as little as a single RGB image, bringing it to life through panoramic video streams. Leveraging scalable 3D world data curated from Unreal Engine, our generative model is rounded in the physical world. It captures a continuous 360-degree environment with little effort, offering a boundless landscape for AI agents to explore and interact with. GenEx achieves high-quality world generation, robust loop consistency over long trajectories, and demonstrates strong 3D capabilities such as consistency and active 3D mapping. Powered by generative imagination of the world, GPT-assisted agents are equipped to perform complex embodied tasks, including both goal-agnostic exploration and goal-driven navigation. These agents utilize predictive expectation regarding unseen parts of the physical world to refine their beliefs, simulate different outcomes based on potential decisions, and make more informed choices. In summary, we demonstrate that GenEx provides a transformative platform for advancing embodied AI in imaginative spaces and brings potential for extending these capabilities to real-world exploration.

GenEx: Advancing Embodied AI through Generative Exploration

The paper introduces GenEx, a significant contribution to generative artificial intelligence and embodied AI. GenEx is a platform designed to generate and facilitate exploration within 3D-consistent imaginative environments from as little as a single RGB image. Utilizing a symbiotic system combining imaginative 3D environments with embodied agents, GenEx proposes a transformative approach to AI's interaction and understanding of complex environments.

Overview of GenEx

GenEx's novelty lies in its ability to generate expansive and dynamic 3D environments using generative imaginations. These imaginations are structured as priors about the surrounding physical world, enabling AI agents to perform both goal-agnostic exploration and goal-driven navigation. The system employs scalable 3D world data from Unreal Engine to ground its generative model in reality, capturing a continuous 360-degree environment with minimal effort.

One of the notable achievements of GenEx is its capability to deliver high-quality world generation with robust loop consistency over long trajectories, demonstrating significant 3D capabilities such as consistency and active 3D mapping. The generation of explorable environments is a substantial step forward, enabling agents to have predictive expectations regarding unseen parts of the world and refine their beliefs and decisions based on simulated outcomes.

Generating an Explorable World

In technical terms, the GenEx framework initializes an explorable generative world from a single image by transforming it into a panoramic 360-degree environment. The transition from static images to dynamic world exploration is powered through a video generation model that adheres to spherical-consistency learning techniques, maintaining 3D coherence through rotational transformations on a spherical coordinate system. This approach ensures continuous and seamless exploration, leveraging action-driven panoramic video generation to simulate movement and interaction within the imaginative world.

Exploration Policies and Modes

GenEx supports various exploration modes, which broaden the horizon for AI agents to understand and interact with their environments. These include interactive user-directed exploration, GPT-assisted free exploration for autonomous navigation, and goal-driven navigation that guides agents to specific targets.

The exploration policy formulated herein employs a sophisticated strategy to determine the best course of action based on current observations and exploration modes, allowing for intelligent adaptation to new conditions and stimuli.

Implications for Embodied AI

The GenEx framework extends its innovative approach further into the field of decision-making for embodied AI. The Imagination-Augmented Policy outlined in the paper allows AI to simulate exploration outcomes without physically traversing them, providing a critical step toward efficient and resource-friendly AI implementations in real-world scenarios. This policy enables AI agents to make informed decisions by integrating both real and imagined observations, significantly enhancing the quality of decision-making.

Moreover, the multi-agent imagination-augmented policy extends the single-agent framework to scenarios requiring coordination and interaction between multiple AI agents, enriching their collaborative capabilities and broadening the scope of possible applications.

Future Developments

The research presented opens avenues for future development in AI, where adaptive exploration in unpredictable environments becomes more efficient and realistic. Extending GenEx's core functionalities could lead to applications across various fields, including interactive gaming, VR/AR experiences, and complex real-world navigation scenarios. Key challenges remain in bridging the gap between virtual and real-world environments, where ongoing advancements in sim-to-real adaptation could play a pivotal role.

Conclusion

GenEx marks a significant advancement in the field of embodied AI by enabling systems to generate, explore, and interact with detailed 3D environments. By leveraging generative imaginations, GenEx facilitates more informed and effective decision-making processes, demonstrating the potential for expanded applications in AI-driven exploration and interaction contexts. As the field progresses, GenEx's framework provides a robust foundation upon which future embodied AI systems can be built.