For decades, robotics has been defined by rigid metals and precise, pre-programmed movements. But in labs around the world, a new revolution is taking shape—one inspired by the soft, adaptable, and multifunctional forms found in nature. At the forefront of this field is Oscar Vail, a leading expert in biomimetic soft robots and advanced materials. His team’s latest creation, OCTOID, a soft robot that perfectly mimics an octopus’s ability to change color, move, and grasp, represents a monumental leap forward. We sat down with him to discuss the intricate science of creating life-like materials, the challenge of integrating complex biological functions into a single system, and his vision for a future populated by intelligent, autonomous soft machines.
The article highlights your development of photonic crystal polymers. Could you walk us through the process of controlling the material’s molecular structure to achieve both color changes and movement? What was the most significant challenge in getting those two distinct functions to work together seamlessly?
It all comes down to mastering the architecture at a near-molecular level. The core of our material is a photonic crystal polymer, which has a very specific, helically arranged molecular structure. Think of it like a microscopic, tightly wound spring. By controlling the precise pitch of that helical coil, we can dictate how it reflects light. When we apply an electrical signal, we’re essentially causing the polymer network to contract or expand ever so slightly. This changes the spacing within the structure, which in turn alters the wavelength of light it reflects back to our eyes. That’s how we get that seamless color transition from blue to green to red. For movement, we engineer asymmetry into the system. By applying the signal unevenly, one side of the material contracts more than the other, creating a bending or curling motion. The biggest challenge, by far, was preventing these two functions from interfering with each other. It was an incredibly delicate balancing act. In the beginning, a strong signal for a sharp bend might cause an unintended and dramatic color flash. The breakthrough came from refining the polymer network itself, allowing us to create distinct thresholds of response for each function within the same electrical input, giving us truly independent control.
Your team successfully created a “triple-in-one” system for camouflaging, moving, and grabbing. Can you elaborate on how a single electrical signal coordinates these three actions? Perhaps you could share an anecdote from the hunting simulation that perfectly demonstrated this integrated capability.
The magic isn’t in a simple on-off switch; it’s in the nuanced language of the electrical signal itself. We can modulate the signal’s location, intensity, and duration to choreograph a complex sequence of actions. A low-voltage, widespread signal might prompt a slow, gentle color bleed to blend into a new background. In contrast, a sharp, high-voltage pulse localized to one side of a tentacle will trigger a rapid, asymmetric contraction, causing it to curl and grasp. We essentially built a library of signal patterns that are mapped to these combined functions—camouflage, locomotion, and capture. I remember one of our early hunting simulations that really brought this to life. We had a target placed near some artificial seaweed. The plan was for OCTOID to match the blue background, creep forward, and then grab the target. During one run, the signal we used for the “creeping” motion was a little too strong, and it unintentionally caused the skin to shift to a faint greenish hue. Initially, we saw it as a glitch, but that subtle green tint made it blend in with the nearby seaweed almost perfectly. It was a happy accident that proved how deeply integrated these functions could become, and it pushed us to develop even more complex signaling that could produce these beautiful, multi-layered behaviors on purpose.
Octopuses are described as a “perfectly transformed robot created by nature.” Beyond the obvious color and motion, what subtle octopus behaviors proved most difficult to replicate in OCTOID, and what specific breakthrough allowed your team to overcome that particular engineering hurdle?
That description is absolutely spot-on. What we’ve achieved is remarkable, but nature is still the master engineer. The most difficult behavior to replicate isn’t just one thing, but the sheer fluidity and continuousness of an octopus’s actions. A real octopus doesn’t “stop camouflaging” to “start moving.” It all happens at once, in a single, flowing, adaptive process. They also change their skin texture to create bumps and ridges, which is a level of complexity we haven’t even begun to tackle. Our robot, while flexible, still performs actions that are somewhat discrete. The central engineering hurdle was moving beyond the old paradigm of building separate systems for each function. You can’t have a color-changing “screen” layered on top of a robotic “actuator” and expect it to feel organic. The real breakthrough, led by Dr. Dae-Yoon Kim’s team, was the development of the photonic crystal polymer itself—a material where the potential for color and the potential for movement are intrinsic properties of the same structure. By creating one unified material that responds to a single type of stimulus, we were able to fuse these functions at their very foundation. That integration is the first and most crucial step toward achieving the seamless, natural grace of the real animal.
You listed several future applications, including deep-sea rescue and defense technology. Could you describe a step-by-step scenario of how OCTOID might perform a task in one of these fields? What key performance metrics would it need to meet to be considered successful?
Let’s take a deep-sea rescue scenario, which is something we’re incredibly excited about. Imagine a delicate scientific instrument has fallen into a complex, fragile coral reef. Sending a conventional, claw-equipped submersible down there would be disastrous; it would shatter the very ecosystem it was meant to study. First, we would deploy OCTOID. As it descends, its skin would autonomously shift from the deep blue of the open water to the mottled patterns of the seafloor, minimizing its disturbance to wildlife. Second, upon reaching the reef, it wouldn’t just plow through. It would use its soft, flexible arms to gently snake through the intricate coral branches, navigating the tight spaces without causing any damage. Once it locates the instrument, it would wrap around it, conforming to its specific shape to secure it without applying any single point of high pressure. Finally, it would gently extract itself and return to the surface. For this mission to be a success, we’d be looking at very specific metrics: zero breakage of the surrounding coral, confirmed by high-resolution imaging; the instrument retrieved with all its own sensors showing no pressure damage; and acoustic and visual signatures so low that they don’t trigger stress responses in nearby marine life. It’s about performance, but also about a fundamental respect for the environment it’s operating in.
Your future goal is to create “intelligent soft machines.” What is the first major step in evolving OCTOID from a pre-programmed robot into one that is self-aware and learning-based? What kind of sensory feedback systems are you currently exploring to achieve this?
The single most important step is closing the loop. Right now, OCTOID is an open-loop system; it executes a command we send it, but it doesn’t truly perceive the outcome of that action or its own state in the world. To become intelligent, it needs sensory feedback. It needs to feel. Our most promising approach is embedding a network of microfluidic channels throughout the robot’s body, filled with a conductive liquid metal. As the robot moves, bends, or is touched by an external object, these channels stretch and deform. That deformation changes their electrical resistance, which we can measure with extreme precision. This would effectively give the robot a sense of proprioception—an awareness of its own body’s shape and position—and a sense of touch. Another avenue is integrating fiber optics that can detect subtle changes in light and pressure, allowing the robot to “see” its own color and the environment directly through its skin. Once we have that rich stream of sensory data, we can begin to apply machine learning algorithms. The robot can then start to connect its actions to sensory outcomes, learning through trial and error how to move more efficiently or camouflage more effectively. That feedback system is the bridge from a pre-programmed puppet to a truly autonomous, learning machine.
What is your forecast for the field of biomimetic soft robotics over the next decade?
I believe we are standing at a pivotal moment. The last decade was about deconstruction—isolating specific biological wonders, like a muscle’s strength or a chameleon’s skin, and trying to replicate them in the lab. The next decade will be defined by integration and intelligence. We are moving beyond creating robots that simply look or act like an animal in one specific way, and toward creating systems that behave with the same holistic, adaptive nature as a living organism. The development of multifunctional materials like our photonic polymers is the critical first step. The next leap will be embedding sophisticated sensory networks and on-board AI processing directly into these soft bodies. I forecast that within ten years, we will see the first generations of truly autonomous soft robots being deployed in real-world environments—performing delicate surgeries inside the human body, silently monitoring fragile ecosystems, and adapting on the fly to unpredictable situations. The boundary between the built and the born, the machine and the organism, is going to become wonderfully, productively blurred.
