I had an interesting conversation with ChatGPT over the weekend.

I asked: “Can you assist me with writing a paper on artificial intelligence and robotics?” In just a few seconds, I had a draft outline and a thought-provoking suggestion.

“Would you like to focus on a specific section or angle, or do you have other ideas in mind for the paper?” asked ChatGPT. “I can help you flesh out any part of it!”

So, I decided to explore the robotics angle. I shared my view: “Making robots that mimic human or animal movement may not be efficient or sustainable. It could be overly expensive. Imagine if we had built airplanes that flapped their wings to mimic birds, or cars that had mechanical legs instead of wheels to mimic horses. Expand on this perspective.”

What followed was a fascinating response generated entirely by ChatGPT (with a few interjections from me in italics). I’ve added some links for those who might want to explore these ideas further.

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One of the fundamental challenges in robotics design is deciding whether to mimic nature’s solutions or to create entirely new, mechanically optimized systems. While biomimicry has led to fascinating breakthroughs, it can often be inefficient, overly complex, or costly. For example, early roboticists were inspired by the agility of animals and the dexterity of humans, but designing robots that replicate such movements has proven challenging. Instead, focusing on mechanical efficiency, rather than biological imitation, has often resulted in more practical solutions. (I immediately asked ChatGPT if biomimicry has helped in Machine Learning. The response was enlightening.)

1. Airplanes and the Fallacy of Wing-Flapping

When engineers first sought to achieve powered flight, they naturally looked to birds for inspiration. Birds use flapping wings to achieve both lift and propulsion, which is ideal for their biological makeup, but not for machines. Early attempts to build flying machines that mimicked wing-flapping, like ornithopters, were complex, inefficient, and failed to gain widespread adoption. It was only when engineers moved away from biomimicry and embraced fixed-wing designs that powered flight became viable. Fixed-wing airplanes, propelled by engines, are simpler to control, require less energy, and are more reliable than flapping-wing designs. (I guess mimicry was not entirely useless here. We kept the ‘wings’ idea, only that they didn’t’ flap).

By departing from nature (Well, not entirely), aviation engineers found a solution that was superior in efficiency, scalability, and cost-effectiveness. This same logic applies to robotics: while it is tempting to imitate biological movement, there are often more efficient solutions tailored to the strengths of mechanical systems. For instance, drones today use rotors rather than flapping wings, offering greater stability and maneuverability for their size.

2. Cars and Mechanical Legs: A Hypothetical Inefficiency

Imagine if early automotive engineers had tried to create cars with mechanical legs to mimic horses, which were the dominant mode of transportation at the time. While horses are adept at moving on uneven terrain, mechanical legs would introduce complexity, require constant maintenance, and result in energy inefficiency compared to wheels.

Wheels, a far simpler design, offer a smooth and energy-efficient means of transportation, especially on paved roads. They allow for continuous motion with minimal friction, making cars far more efficient than any legged design could ever be in typical driving environments. Even modern roboticists who work on legged robots often face significant challenges with energy consumption, balance, and complexity when compared to wheeled systems. In robotics, wheels or tracked designs are still the preferred solution for environments where smooth, consistent surfaces are available, as they maximize efficiency and reduce mechanical wear.

3. Walking Robots: When Mimicking Nature Makes Sense

There are, however, scenarios where biomimetic approaches can be useful, particularly in environments that are difficult for wheeled or tracked systems to navigate. For example, Boston Dynamics’ Spot robot uses a quadrupedal design, which allows it to traverse rough, uneven terrain more easily than wheeled robots could. In these cases, mimicking animal locomotion has merit because the environment demands adaptability that wheels or tracks cannot provide. Spot excels in environments such as construction sites or disaster zones, where agility and stability on uneven ground are paramount.

Yet, even in these cases, the robotic systems do not exactly mimic nature. While animals use muscles and tendons for movement, Spot relies on electric motors and advanced control algorithms to achieve a similar effect, and it sacrifices some biological characteristics in favor of engineering optimizations. Despite the advantages, legged robots are generally slower, less energy-efficient, and more expensive than their wheeled counterparts, which highlights the limitations of biomimicry when applied too broadly.

4. The Case of Swimming Robots: Leveraging Nature’s Lessons

In underwater robotics, engineers have often found that mimicking fish and marine mammals can be an effective approach. Unlike in air or on land, water offers a unique resistance that is well-suited for certain types of propulsion systems, such as fins and flippers. Robotic fish, like the RoboTuna developed at MIT, mimic the natural undulating motion of fish to move efficiently through water. The streamlined body of fish, perfected through millions of years of evolution, is a prime example of where biomimicry offers clear advantages.

Even so, engineers often blend natural inspiration with mechanical improvements. Rather than fully replicating the anatomy of fish, these robots incorporate technologies like servomotors and rigid structural components to enhance durability and control—elements not found in nature but essential for artificial systems.

5. AI-Driven Optimization: Beyond Biomimicry

As artificial intelligence (AI) becomes more advanced, robots can now autonomously explore optimized movement patterns that are more efficient than those found in nature. Instead of relying solely on mimicking human or animal motion, AI can be used to discover novel forms of movement. For example, Roboat, an autonomous boat developed at MIT, doesn’t try to mimic marine animals but instead uses AI to optimize its propeller movements for energy efficiency and precise control in urban waterways.

Evolutionary algorithms, a subset of AI, can allow robots to explore a wide range of potential movement patterns. Through trial and error, these systems can discover movements that are well-suited for a robot’s design and operational environment, often yielding designs that bear little resemblance to biological counterparts. This approach pushes robotics design into uncharted territory, where the focus shifts from biomimicry to pure functional efficiency.

6. Sustainability and Cost-Effectiveness in Robotic Design

Focusing on efficiency and mechanical optimization is not only about performance; it’s also about sustainability. Robots that are designed with simplicity in mind tend to use less energy, require fewer resources to manufacture, and have lower maintenance costs. For instance, simple wheeled robots consume far less energy than complex legged robots, making them more suitable for long-term deployment in fields like logistics, where energy efficiency is paramount.

Moreover, the development costs of complex robots often make them impractical for widespread use. Designing a humanoid robot, for example, that can perfectly mimic human movement might sound impressive, but it’s far more cost-effective to use specialized robots with wheels or tracks for specific tasks like material handling, security patrolling, or cleaning.

7. Conclusion: The Case for Functional Design

In conclusion, while nature provides useful models for inspiration, the ultimate goal in robotics should be efficiency, reliability, and sustainability, rather than direct imitation. Just as airplanes have evolved beyond the bird-inspired designs of early inventors, robots should be designed with mechanical and environmental efficiency in mind. By blending AI-driven optimization with smart engineering principles, future robots will not only perform better but also be more cost-effective and sustainable.

The future of robotics lies in balancing inspiration from the natural world with the practical demands of mechanical systems, ultimately leading to designs that are not only efficient but also capable of transforming industries and societies.