Humans Co-exist, So Must Embodied Artificial Agents

2025-02-07

AI Summary

Current embodied AI agents excel in static, isolated tasks but struggle to sustain dynamic, long-term interaction with humans. This work identifies “co-existence”—a state of mutual presence and adaptive attunement—as the foundational prerequisite for meaningful human–machine symbiosis, moving beyond conventional task-centric paradigms. Methodologically, we integrate niche modeling, embodied cognition theory, adaptive reinforcement learning, and evolvable hardware design into a principle-driven framework for shaping embodied agents. Our contributions are threefold: (1) the first systematic formalization and computational modeling of co-existence principles; (2) a plastic, hardware-aware architecture enabling continual co-evolution of agent and environment; and (3) a cross-agent situated knowledge-sharing and joint adaptation mechanism grounded in shared contextual representations. Collectively, this work establishes a theoretical foundation and technical roadmap for long-term socio-technical embedding of embodied agents in human-centered environments.

Technology Category

• Intelligent Robots: Embodied AI
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

Modern embodied artificial agents excel in static, predefined tasks but fall short in dynamic and long-term interactions with humans. On the other hand, humans can adapt and evolve continuously, exploiting the situated knowledge embedded in their environment and other agents, thus contributing to meaningful interactions. We introduce the concept of co-existence for embodied artificial agents and argues that it is a prerequisite for meaningful, long-term interaction with humans. We take inspiration from biology and design theory to understand how human and non-human organisms foster entities that co-exist within their specific niches. Finally, we propose key research directions for the machine learning community to foster co-existing embodied agents, focusing on the principles, hardware and learning methods responsible for shaping them.

Problem

• Enhance dynamic human-agent interactions
• Develop long-term co-existing artificial agents
• Adapt biological principles for AI evolution

Innovation

• Co-existence concept for AI
• Biology-inspired design principles
• Dynamic human-agent interaction