Environmental Embodied AI – Communications of the ACM

What is Environmental Embodied AI?

While embodied AI is commonly understood as general-purpose intelligence that empowers various forms of robotics¹, we believe that its scope extends significantly beyond robotic platforms alone. Embodied AI, as we define it, refers to intelligent systems capable of learning from and actively interacting with their environments, continuously adapting based on real-time sensor feedback and context-driven decision-making.

Specifically, we define Environmental Embodied AI as an intelligent virtual agent capable of real-time perception, learning, and interaction with its surrounding environment through sensor inputs, enabling it to actuate environmental elements, e.g. lighting, temperature, sound, etc., for targeted objectives such as resource optimization, energy efficiency, and sustainability enhancement.

Distinct from traditional embodied AI systems primarily associated with robotic platforms, Environmental Embodied AI specifically emphasizes non-robotic applications, employing virtual agents to directly influence physical or operational states within environments. These intelligent systems autonomously analyze environmental data, dynamically adapting behaviors to optimize outcomes and significantly reduce ecological footprints, inherently supporting environmentally sustainable practices.

Potential Applications of Environmental Embodied AI

Environmental Embodied AI finds extensive applications across diverse fields. To name just a few:

Within smart agriculture, Environmental Embodied AI agents leverage sensor networks and drone imagery to monitor crop health, soil conditions, and weather patterns². These intelligent agents manage irrigation schedules, fertilizer application, and pest control activities with precision, optimizing agricultural productivity and resource usage. The primary optimization objectives include maximizing crop yield, minimizing water and chemical inputs, and enhancing overall environmental sustainability.

Environmental monitoring and protection applications include Environmental Embodied AI agents deployed for ongoing ecosystem surveillance, pollution detection, and natural disaster response³. These agents continuously gather and analyze environmental data, quickly identifying risks such as pollution spills or biodiversity threats, and autonomously initiating remediation actions. Optimization objectives here focus on maximizing ecosystem health, minimizing response time to environmental hazards, and effectively managing environmental resources.

In smart building automation, Environmental Embodied AI adaptively manages internal building environments including ventilation, temperature control, and occupancy-based resource allocation⁴. Using predictive maintenance algorithms, these intelligent systems proactively identify and resolve potential equipment failures, enhancing operational reliability and reducing downtime. The optimization objectives involve achieving maximum occupant comfort, resource efficiency, and cost-effectiveness, significantly improving the building of operational sustainability.

Graph-Based Collective Adaptation

To effectively realize these potential applications, Collective Adaptation emerges as the key enabling technology underpinning Environmental Embodied AI due to its ability to leverage decentralized interactions among individual agents to achieve coordinated and adaptive responses to complex environmental conditions⁵.

In natural environments, most adaptivity emerges from self-organized swarms. For instance, ant and bacteria swarms exhibit complex behaviors that far exceed the intelligence of each individual⁶. Similarly, the nervous systems of higher animals are also collective systems of neurons. Collective adaptation refers to the phenomenon in which an entire group adapts to novel tasks through the adaptive behaviors of its individual members⁷.

In real-world environmental systems. we believe collective adaptation allows for scalable, efficient, and robust management of complex systems like power grids, traffic networks, and building HVAC systems, all of which involve numerous interconnected nodes and varying operational states.

As shown in Figure 1, Collective adaptation operates through an Adaptive Foundation Unit (AFU) that individually processes sensor and communication inputs to produce control and communication outputs. Each agent possesses mechanisms to identify other agents, establish communication pathways dynamically, and maintain histories of input-output interactions, enabling real-time adaptation to changing conditions.

Unlike traditional static graph neural networks (GNNs) that rely on predefined communication rules, these AFUs can autonomously determine their interaction patterns and adapt these interactions over time. They are designed to be generalizable and robust, functioning effectively across diverse network topologies and task specifications.

Recent advancements suggest training AFUs independently in suitable meta-environments rather than in an end-to-end collective manner, inspired by emergent collective organization observed in large language models (LLMs). This approach significantly reduces training complexity and enhances scalability, crucial for practical deployment in diverse applications⁸.

Societal Impacts

The title of this article, “Environmental Embodied AI,” was intentionally crafted with dual meanings. On one level, it represents virtual intelligent agents capable of performing meaningful actions upon their environments through sensor-driven decision processes.

On another level, it signifies embodied AI technologies designed explicitly with environmental sustainability in mind—technologies whose very applications, such as intelligent HVAC systems, lighting controls, or energy management agents, inherently promote ecological responsibility by significantly reducing energy consumption and environmental impact.

For instance, in smart agriculture, AI-powered irrigation systems have been shown to reduce water consumption by up to 27.6% compared to conventional methods⁹. Environmental Embodied AI will further enhance water savings by enabling real-time adaptive decision-making, responding dynamically to changing soil conditions, weather patterns, and plant water needs, thereby optimizing irrigation even more precisely and sustainably.

References:

1. Wang, F. and Liu, S., 2025. Putting the Smarts into Robot Bodies. Communications, 68(3).
2. Lipper, L., Thornton, P., Campbell, B.M., Baedeker, T., Braimoh, A., Bwalya, M., Caron, P., Cattaneo, A., Garrity, D., Henry, K. and Hottle, R., 2014. Climate-smart agriculture for food security. Nature climate change, 4(12), pp.1068-1072.
3. Artiola, J.F., Pepper, I.L., and Brusseau, M.L. eds., 2004. Environmental monitoring and characterization. Academic Press.
4. Vakiloroaya, V., Samali, B., Fakhar, A., and Pishghadam, K., 2014. A review of different strategies for HVAC energy saving. Energy conversion and management, 77, pp.738-754.
5. Ha, D. and Tang, Y., 2022. Collective intelligence for deep learning: A survey of recent developments. Collective Intelligence, 1(1), p.26339137221114874.
6. Benjamin, F.J., Kaaronen, R.O., Moser, C., Rorot, W., Tan, J., Varma, V., Williams, T., and Youngblood, M., 2023. All intelligence is collective intelligence. Journal of Multiscale Neuroscience, 2(1), pp.169-191.
7. Galesic, M., Barkoczi, D., Berdahl, A.M., Biro, D., Carbone, G., Giannoccaro, I., Goldstone, R.L., Gonzalez, C., Kandler, A., Kao, A.B., and Kendal, R., 2023. Beyond collective intelligence: Collective adaptation. Journal of the Royal Society Interface, 20(200), p.20220736.
8. Hong, S., Zheng, X., Chen, J., Cheng, Y., Wang, J., Zhang, C., Wang, Z., Yau, S.K.S., Lin, Z., Zhou, L., and Ran, C., 2023. Metagpt: Meta programming for multi-agent collaborative framework. arXiv preprint arXiv:2308.00352, 3(4)
9. Preite, L. and Vignali, G., 2024. Artificial intelligence to optimize water consumption in agriculture: A predictive algorithm-based irrigation management system. Computers and Electronics in Agriculture, 223, p.109126.

Fan Wang is a researcher at China’s Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS). He specializes in Reinforcement Learning, Natural Language Processing, AI for Sciences, and Robotics.

Shaoshan Liu is a member of the ACM U.S. Technology Policy Committee, and of the U.S. National Academy of Public Administration’s Technology Leadership Panel Advisory Group. His educational background includes a Ph.D. in Computer Engineering from the University of California, Irvine, and a Master of Public Administration (MPA) degree from the Harvard Kennedy School.