· 6 min read
The surge in generative and embodied AI, together with advances in simulation and sensor technologies, has pushed robotics far beyond factory floors. Robots now pack boxes in warehouses, monitor crops, deliver parcels, inspect offshore wind turbines, and assist in hospitals and eldercare facilities. They have become part of the everyday landscape of production, logistics, and services, often invisible to consumers yet indispensable to modern economies.
The next step in this evolution is the rise of humanoid robots, or humanoids, already being tested in a growing number of applications. Unlike the specialized or stationary robots that dominate industrial settings, humanoids are designed to move and operate in environments built for humans and continuously refine their skills. They can open doors, climb stairs, use tools, and execute complex manual tasks through observation and simulation. Their significance lies not only in what they can do, but also in where they can do it, essentially anywhere humans already work.
How humanoids learn is key to understanding their potential. At first, they rely on imitation, much like children observing their parents. To collect data for imitation, human behavior is captured through motion sensors and video recordings, whether of a greenhouse worker harvesting strawberries or a technician replacing an air filter. Beyond imitation, humanoids use reinforcement learning, practicing tasks virtually through digital twins of their working environments. Every successful action is rewarded, while errors are penalized, gradually refining performance. Advanced platforms such as NVIDIA’s Isaac Sim and standards like OpenUSD enable developers to build highly realistic training environments.
This approach is already finding applications in climate-related industries. The EU’s Destination Earth initiative aims to build a digital twin of the planet to model, monitor, and simulate natural processes, hazards, and human activity. At the corporate level, Akselos applies digital twins and physics-based simulations to protect energy assets and other critical infrastructure. Through such simulations, one humanoid can accumulate the equivalent of years of practice in just a few hours, either using domain randomization or detailed digital twins of real environments. Training then moves into the physical world through sim-to-real transfer, and once a skill is mastered, it can be shared instantly with thousands of other robots.
While this process sounds straightforward, barriers to mass deployment remain. High-quality observational data is limited, computing power is costly, and training consumes significant energy. Yet progress is rapid, and humanoids are expected to spread widely, especially where benefits outweigh costs.
Climate change, a systemic crisis cutting across energy, food systems, cities, and disaster management, demands solutions that are efficient, safe, and scalable. The Climate Robotics Network notes that robots already support climate action through aerial, ocean, and ground systems. What makes humanoids distinct is their ability to integrate directly into human-designed environments without rebuilding infrastructure. They can accelerate climate solutions instead of slowing them with costly redesigns. In my opinion, their contribution can be understood through three main functions: replacing human labor, improving efficiency, and providing continuous data and analysis, which are all essential for building climate resilience.
Replacing human labor is critical where work is hazardous, repetitive, or in short supply. In disaster zones, humanoids could one day take over frontline tasks such as fighting wildfires, conducting flood rescues, dismantling toxic infrastructure, or addressing radiation leaks. Accounting for the economic price of human life, through measures such as the Value of Statistical Life (VSL), deploying humanoids in dangerous tasks could also prove economically beneficial for governments, corporations, and insurers that bear the costs of compensation in the event of fatalities or disability payments.
In agriculture, humanoids could assume roles now filled by seasonal or migrant workers, performing cycles of planting, pruning, and maintenance. As extreme heat becomes more common, they could replace workers in regions where prolonged outdoor labor is unsafe.
The potential becomes even clearer in medicine. Training a specialized physician takes nearly a decade; once a humanoid can perform procedures with comparable precision and reliability, that skill could be replicated instantly across thousands of systems. What takes humans years to master can be shared among machines in seconds. As climate change reshapes labor needs and demands new technical capabilities, humanoids could help close the gap between the long time required to train people and the urgency to act, including in medical emergency response.
Infrastructure maintenance offers another promising field. Humanoids could replace workers in night-time repairs, install smart meters, or carry out offshore tasks such as maintaining wind turbines and repairing subsea cables. At greater depths, they may even support coral-reef restoration, where human divers cannot operate safely for extended periods.
Efficiency represents the second major contribution. Much of the climate challenge is not about inventing new technologies but about making existing ones perform better. Humanoids could reduce downtime and resource waste by fitting seamlessly into human-centered environments. They could support precision agriculture in irregular plots where wheeled machines or drones cannot operate and maintain unconventional solar-panel designs that require human-like dexterity. In waste management, humanoids could relieve people from exhausting and hazardous sorting work in recycling facilities. Across these examples, the benefits are clear: lower resource use, fewer chemical exposures, and reduced emissions.
The third contribution lies in their ability to generate data and insights around the clock. Effective climate management depends on reliable, continuous information. Without it, we cannot know whether a project truly reduces emissions or whether systems perform as intended. Humanoids can collect data wherever they work, even in hazardous or remote places where human presence is limited. They can measure soil health, track emissions, monitor ocean conditions, and inspect industrial sites, producing dense, accurate datasets that enable more informed decisions.
Because real-world data is scarce, developers increasingly rely on AI-generated synthetic data to train humanoids. Yet synthetic data remains imperfect: it often simplifies the complexity and unpredictability of real environments, producing simulations that are skewed. This limitation becomes critical in the context of climate change, where extreme events are evolving faster than our ability to model them. Many of these scenarios have never been fully observed, let alone precisely simulated. Training humanoids to operate reliably under conditions that defy past experience remains one of the greatest challenges in their development.
The road ahead is far from simple. Humanoids are still slower than humans, and many demonstration videos exaggerate their speed. Outdoor environments pose reliability challenges, with dust, heat, and salinity affecting sensors and hardware. Costs remain high, making adoption viable only where utilization is constant and returns are clear. Their own environmental footprint must also be addressed, from the energy and rare materials required for production to end-of-life disposal. Social implications cannot be ignored either: questions of job displacement, responsibility for costs, and cybersecurity and privacy risks will require careful consideration.
For these reasons, the first widespread deployments will likely occur in controlled environments where returns are most visible, greenhouses, warehouses, and night-time maintenance in public facilities. Humanoid robots will not solve climate change; they are not disruptors in the field. But they can serve as enablers, a force multiplier, replacing dangerous or scarce labor, improving the efficiency of systems, and generating the data needed for accountability and progress. They may first appear in mundane settings, but as costs fall and capabilities improve, they could take on far harder challenges in the field of climate action.
illuminem Voices is a democratic space presenting the thoughts and opinions of leading Sustainability & Energy writers, their opinions do not necessarily represent those of illuminem.
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