Foundation models have transformed computer vision and natural language processing, and researchers now propose applying these principles to develop foundation agents. These AI systems are designed for open-ended decision-making tasks in physical environments.
In a recent position paper, scientists from the University of Chinese Academy of Sciences define foundation agents as “generally capable agents across physical and virtual worlds.” They suggest that these agents could lead to a paradigm shift in decision-making, similar to how large language models (LLMs) have revolutionized linguistic and knowledge-centric tasks.
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Foundation agents are poised to simplify the creation of versatile AI systems that can significantly impact fields currently dependent on rigid, task-specific AI solutions.
The Challenges of AI Decision-Making
Traditional AI decision-making approaches have notable limitations. Expert systems depend on formal human knowledge and manually created rules. Reinforcement learning (RL) systems require extensive training from scratch for each new task, limiting their generalization capabilities. Imitation learning (IL) necessitates considerable human effort to prepare training examples.
In contrast, LLMs and vision language models (VLMs) can quickly adapt to different tasks with minimal fine-tuning. The researchers believe that, with necessary modifications, these methods can be adapted to develop foundation agents capable of addressing open-ended decision-making tasks in both physical and virtual realms.
Key Characteristics of Foundation Agents
The researchers highlight three essential characteristics of foundation agents:
1. Unified Representation: A combined depiction of environment states, agent actions, and feedback signals.
2. Unified Policy Interface: Applicable to a broad spectrum of tasks and domains, including robotics, gaming, healthcare, and more.
3. Reasoned Decision-Making Process: Decisions based on an understanding of world knowledge, environmental factors, and interactions with other agents.
According to the researchers, “These characteristics empower foundation agents with multi-modal perception, adaptability across tasks and domains, and the ability to generalize with few or no examples.”
A Roadmap for Foundation Agents
The proposed roadmap for foundation agent development includes three critical components:
1. Data Collection: Large-scale interactive data must be gathered from both internet and real-world environments. In scenarios where data acquisition is challenging, simulators and generative models like Sora may be employed.
2. Pre-training on Unlabeled Data: Foundation agents should be pre-trained using unlabeled data to develop useful decision-making knowledge. This prepares the models for fine-tuning on smaller, specific datasets, enabling quicker adaptation to new tasks.
3. Alignment with LLMs: Foundation agents should be integrated with large language models to incorporate world knowledge and human values into their decision-making processes.
Challenges and Opportunities for Foundation Agents
Developing foundation agents introduces unique challenges not encountered with language and vision models. The details of the physical world involve low-level information rather than high-level abstractions, complicating the creation of unified representations for decision-making variables.
Moreover, the substantial domain variations among decision-making scenarios hinder the development of a cohesive policy interface. While a unified foundation model could encompass all modalities and environments, this may also introduce complexity, potentially affecting interpretability.
Foundation agents must engage actively in dynamic decision-making processes, a departure from the primarily content-focused roles of language and vision models. Researchers propose various avenues for bridging the gap between existing foundation models and agents capable of navigating evolving tasks and environments.
Significant advances are underway in robotics, where control systems and foundation models converge to create adaptable systems that can generalize across unencountered tasks. These systems utilize the extensive commonsense knowledge from LLMs and VLMs to make informed decisions in unfamiliar situations.
Another vital area of exploration is autonomous driving, where researchers investigate how large language models can enhance driving systems by incorporating commonsense knowledge and human cognitive capabilities. Other fields, including healthcare and scientific research, also stand to benefit from foundation agents collaborating with human experts.
“Foundation agents possess the potential to transform decision-making processes, much like foundation models have impacted language and vision,” the researchers assert. “Their advanced perception, adaptability, and reasoning abilities not only address the limitations of conventional RL but also unlock the full capabilities of foundation agents in real-world applications.”
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