From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessons

Abstract: We examine the capability of Multimodal LLMs (MLLMs) to tackle diverse domains that extend beyond the traditional language and vision tasks these models are typically trained on. Specifically, our focus lies in areas such as Embodied AI, Games, UI Control, and Planning. To this end, we introduce a process of adapting an MLLM to a Generalist Embodied Agent (GEA). GEA is a single unified model capable of grounding itself across these varied domains through a multi-embodiment action tokenizer. GEA is trained with supervised learning on a large dataset of embodied experiences and with online RL in interactive simulators. We explore the data and algorithmic choices necessary to develop such a model. Our findings reveal the importance of training with cross-domain data and online RL for building generalist agents. The final GEA model achieves strong generalization performance to unseen tasks across diverse benchmarks compared to other generalist models and benchmark-specific approaches.

• The paper introduces a process to transform multimodal LLMs into GEAs via multi-embodiment action tokenization and a two-stage training pipeline.
• It demonstrates that reinforcement learning significantly enhances the agent's adaptability, achieving 90% success in manipulation and 44% of expert scores in gaming benchmarks.
• The study emphasizes the value of cross-domain data utilization and standardized action spaces to develop versatile AI systems across varied tasks.

From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessons

The research paper "From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessons" critically explores the transformative potential of Multimodal LLMs (MLLMs) beyond their prevalent applications in language and vision tasks, venturing into the domains of Embodied AI, Games, UI Control, and Planning. The core focus is on adapting MLLMs into Generalist Embodied Agents (GEAs), which are designed to operate effectively across diverse environments and tasks.

Adaptation to Generalist Embodied Agents

The study introduces a structured adaptation process of MLLMs into GEAs utilizing a multi-embodiment action tokenizer. This adaptation process is pivotal as it allows the model to ground itself across varied domains encompassing manipulation, navigation, video gaming, and UI control. Training GEAs involves a two-stage process: supervised learning on a vast dataset of 2.2 million trajectories and subsequent online reinforcement learning (RL) in interactive simulators. These stages are crucial in overcoming the limitations of dataset diversity and inherent robustness typically observed in initial training phases.

Empirical Performance and Generalization

The results presented in the paper outline the GEA's impressive generalization capabilities across multiple benchmarks without requiring domain-specific architectures. For example, in the manipulation-based CALVIN benchmark, GEA achieves a 90% success rate, outperforming other methods by significant margins and challenging specialist systems. In the Procgen gaming benchmark, GEA reaches 44% of expert scores, demonstrating a notable improvement over previous models, thus reinforcing the value of cross-domain training.

Methodological Insights

Several methodological insights are revealed through the empirical evaluations:

1. Cross-Domain Data Utilization: The importance of training with diverse datasets is evident in the performance gains across different tasks, suggesting a substantial cross-domain generalization effect.
2. Role of Reinforcement Learning: The integration of online RL is pivotal for enhancing the agent's ability to recover from errors and adapt to new scenarios, outperforming approaches restricted to supervised learning.
3. Multi-Embodiment Action Tokenization: This technique helps in standardizing action spaces across various embodiments, enhancing the model's adaptability and scalability across tasks.

Theoretical and Practical Implications

From a theoretical perspective, this work underscores the potential of leveraging foundational models, such as MLLMs, for creating versatile AI agents. It opens avenues for developing unified models capable of operating effectively across different domains without being restricted to the semantics of language and vision alone. Practically, this progress implies a significant step toward the realization of AI systems that can seamlessly transition between virtual tasks like gaming or web navigation and physical tasks involving robotics and autonomous navigation.

Future Directions

While GEAs have exhibited substantial capabilities, several challenges remain for future research. The scalability of these agents to even more complex tasks and environments, especially those requiring intricate motor skills or understanding ambiguous human instructions, warrants further exploration. Additionally, extending reinforcement learning methodologies to broader domains, refining action tokenization techniques, and exploring more granular architectural improvements could potentially elevate the generalization and efficiency of such agents.

In conclusion, by presenting a methodological framework and empirical evidence, this paper significantly contributes to the ongoing discourse on advancing AI from task-specific applications towards the development of truly generalist agents. This foundational work sets the stage for ensuing developments in AI that aspire to seamlessly blend perceptive and deliberative capabilities.