A call for embodied AI (2402.03824v4)

Abstract: We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence, juxtaposing it against current AI advancements, particularly LLMs. We traverse the evolution of the embodiment concept across diverse fields - philosophy, psychology, neuroscience, and robotics - to highlight how EAI distinguishes itself from the classical paradigm of static learning. By broadening the scope of Embodied AI, we introduce a theoretical framework based on cognitive architectures, emphasizing perception, action, memory, and learning as essential components of an embodied agent. This framework is aligned with Friston's active inference principle, offering a comprehensive approach to EAI development. Despite the progress made in the field of AI, substantial challenges, such as the formulation of a novel AI learning theory and the innovation of advanced hardware, persist. Our discussion lays down a foundational guideline for future Embodied AI research. Highlighting the importance of creating Embodied AI agents capable of seamless communication, collaboration, and coexistence with humans and other intelligent entities within real-world environments, we aim to steer the AI community towards addressing the multifaceted challenges and seizing the opportunities that lie ahead in the quest for AGI.

An Overview of "A Call for Embodied AI"

The paper "A Call for Embodied AI" advocates for the development of Embodied Artificial Intelligence (E-AI) as a crucial progression towards achieving AGI. The authors assert that current AI paradigms, particularly LLMs, fall short of encapsulating true intelligence due to their static nature and limited capacity for real-world interaction and learning. E-AI, they propose, offers a pathway to overcoming these limitations by integrating cognitive architectures that emphasize perception, action, memory, and learning in a dynamic and adaptable manner.

Theoretical Foundation and Framework

The paper begins by tracing the evolution of the embodiment concept through various fields such as philosophy, psychology, neuroscience, and robotics. To provide a structured approach to E-AI, the authors propose a theoretical framework aligned with Friston’s active inference principle. This framework emphasizes the necessity of integrating perception, action, memory, and learning to create comprehensive embodied agents. Such agents are designed to interact continuously with their environments, enabling them to adapt and evolve beyond the capabilities of static AI models.

Key Components of Embodied AI

The authors identify four pivotal components of E-AI:

1. Perception: The ability of an agent to sense and interpret environmental stimuli, transforming raw sensory data into actionable insights.
2. Action: The dual capability to decide on and execute actions within an environment, balancing reactive and goal-oriented responses.
3. Memory: The systems that support retention of past experiences, comprising both short-term and long-term components. Episodic memory, in particular, allows agents to recall specific events, facilitating learning from experience.
4. Learning: Continuous adaptation and acquisition of knowledge essential for dynamic intelligence. This involves refining models based on experience and interaction.

These components contribute to the development of agents capable of autonomous and informed interaction with their surroundings, a fundamental departure from the traditional static learning paradigms.

Challenges and Roadmap

Developing E-AI is accompanied by several challenges. The authors highlight the need for new learning theories to accommodate the dynamic data environments that embodied agents operate within. Furthermore, managing noise and uncertainty, ethical and meaningful interaction with humans, overcoming hardware limitations, and bridging the reality gap presented by simulators are pivotal areas that require innovative solutions.

The paper emphasizes the importance of simulators in training E-AI agents, identifying them as instrumental in offering controlled yet expansive environments where agents can learn and test their interactions before real-world deployment. Nevertheless, simulators must evolve to encompass a wider range of environments and reduce the discrepancy between simulated and real-world performance, known as the reality gap.

Implications and Future Directions

The implications of advancing E-AI are profound, impacting both theoretical understanding and practical applications of AI. E-AI promises significant strides in human-AI interaction, potentially leading to agents that can navigate complex tasks with enhanced efficiency and comprehension. By addressing current limitations and focusing on the dynamic interplay between an agent and its environment, E-AI proposes a sustainable trajectory towards the realization of AGI.

The paper sets a comprehensive agenda for AI research, advocating for a collective emphasis on overcoming the multifaceted challenges facing E-AI. This approach not only enhances our theoretical understanding of intelligence but also propels AI towards more human-like cognitive abilities.

In conclusion, "A Call for Embodied AI" provides an insightful discourse into the future of AI, framing E-AI as an essential step towards achieving AGI. Through a robust theoretical framework and identification of critical challenges, the paper charts a path for future innovations in AI that align more closely with human cognition and interaction.