Abstract
Despite advancements in LLMs and Large Multimodal Models (LMMs), their integration into language-grounded, human-like embodied agents remains incomplete, hindering complex real-life task performance in physical environments. Existing integrations often feature limited open sourcing, challenging collective progress in this field. We introduce LEGENT, an open, scalable platform for developing embodied agents using LLMs and LMMs. LEGENT offers a dual approach: a rich, interactive 3D environment with communicable and actionable agents, paired with a user-friendly interface, and a sophisticated data generation pipeline utilizing advanced algorithms to exploit supervision from simulated worlds at scale. In our experiments, an embryonic vision-language-action model trained on LEGENT-generated data surpasses GPT-4V in embodied tasks, showcasing promising generalization capabilities.
Overview
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LEGENT provides an open-source platform integrating LLMs and Large Multimodal Models (LMMs) with embodied agents within realistic 3D environments, aimed at pushing the boundaries of AI applications.
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The platform features a comprehensive data generation pipeline and interactive 3D environments that support diverse scene creation, agent design, and robust training mechanisms.
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Through experimental validation, agents trained on LEGENT have outperformed existing models in tasks involving navigation and interaction, showing promising generalization capabilities in novel settings.
LEGENT: An Innovative Platform for Training Embodied Agents Using LLMs and LMMs
Introduction
The integration of LLMs and Large Multimodal Models (LMMs) into embodied agents operating in realistic 3D environments offers promising advancements in AI applications. The newly introduced open-source platform, LEGENT, addresses the complexities of such integrations, providing a dual-part solution featuring a rich interactive 3D environment alongside a sophisticated data generation pipeline. This pipeline leverages advanced algorithms to optimize training based on simulated scenarios.
Key Features of LEGENT
LEGENT encompasses robust tools and features designed to foster the development of sophisticated embodied agents:
- Interactive 3D Environment: LEGENT provides a diverse and realistic simulation space where agents can perform actions, interact through language, and execute tasks in real-time.
- Advanced Data Generation: The platform supports high-volume, varied data production essential for training robust models. This includes scene creation and agent trajectory planning with the utilization of state-of-the-art techniques.
- Accessibility and Scalability: Designed with openness in mind, LEGENT offers an intuitive user interface that welcomes users with different levels of expertise in working with 3D environments.
Experimental Validation
Embodied agents trained on data generated through LEGENT have demonstrated superior performance over models like GPT-4V in tasks requiring navigation and interactive capabilities. Notably, these agents exhibit compelling generalization, capable of adapting to unencountered settings effectively.
Technical Implementation
Scene and Agent Design
- Realistic Physics and Diverse Rendering: LEGENT's scene design mimics real-world physics, supports various rendering styles, and allows for interactive object manipulation, contributing significantly to the realism and applicative potential of the training environment.
- Agent Design: Agents in LEGENT are equipped with egocentric vision and can engage in bidirectional natural language interactions. Their ability to perform a range of actions, from simple navigation to complex task execution, is pivotal for comprehensive training.
Interface Usability
- Ease of Use: The platform is structured to facilitate easy integration of LLMs and LMMs, providing comprehensive support through documentation and a Python-based toolkit for environment-agent interactions.
- Customization and Flexibility: Users can customize scenes and agent behaviors to fit specific research needs or experimental setups, enhancing the adaptability of LEGENT for various applications.
Data Generation Mechanisms
LEGENT's data generation pipeline is potent in its capability to generate diverse, realistic scenarios that can be leveraged for extensive model training:
- Scene Generation: Two main methodologies are used; procedural generation for efficiently creating scalable environments, and language-guided generation that aligns scene setup with textual descriptions using LLMs.
- Task-Specific Scene Adaptation: Both scene and task generation are adaptable based on specific requirements, facilitating targeted training exercises and enhancing the overall efficiency and effectiveness of model training processes.
Future Directions and Implications
The ongoing development of LEGENT aims at various enhancements to enrich its ecosystem. Future updates anticipate more diverse data generation techniques, increased scalability in model training, and refined integration of more realistic physical interactions. These advancements are expected to not only propel research in embodied AI but also improve the practical deployment of these technologies in real-world applications.
Conclusion
LEGENT serves as a groundbreaking platform enabling the advanced training of embodied agents through an integrated approach combining LLMs, LMMs, and an immersive 3D environment. This combination promises significant strides in the development of AI that can understand and interact within physical spaces effectively, bridging a critical gap in current technology deployments.
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