Soft robotics is a field of engineering that builds robots from flexible, compliant materials instead of the rigid metals and hard plastics used in conventional machines. Where a traditional industrial robot arm is stiff and moves along fixed joints, a soft robot can bend, stretch, squeeze, and twist in virtually any direction, much like a living organism. The global soft robotics market was valued at roughly $2.26 billion in 2025 and is projected to reach $7.22 billion by 2030, growing at about 26.5% per year.
How Soft Robots Differ From Rigid Ones
The core distinction comes down to material stiffness. Conventional robots are built from materials with a bulk elastic modulus above 1 gigapascal, meaning they barely flex under force. Soft robots use materials at or below that threshold, allowing them to deform, compress, and stretch the way biological tissue does. A traditional robot has a fixed number of joints, each moving in a predictable direction. A soft robot’s body is essentially one continuous structure with nearly unlimited ways to move, sometimes described as having “infinite degrees of freedom.”
That flexibility is the whole point. Soft robots can conform to uneven surfaces, squeeze through tight spaces, and interact safely with people and delicate objects. But it also creates a major engineering headache: controlling something that can move in every direction at once is far harder than controlling a jointed arm that swings along two or three axes.
What They’re Made Of
Silicone rubbers and other elastomers are the workhorse materials in soft robotics. They’re stretchy, durable, and relatively easy to mold into complex shapes. Beyond elastomers, researchers are increasingly turning to hydrogels, water-rich polymer networks that closely resemble biological tissue. Hydrogels can be engineered to respond to heat, light, electric fields, or magnetic fields by embedding them with nanomaterials like carbon nanotubes or iron oxide particles. A hydrogel loaded with carbon nanotubes, for example, can respond to temperature five times faster than the same hydrogel without them, and also reacts to near-infrared light.
Another class of materials called electroactive polymers changes shape when exposed to an electric current. Some versions rely on ion movement to generate bending, while others use electrostatic forces to expand and contract. Shape memory alloys, typically made from nickel-titanium, round out the toolkit. These metal wires “remember” a pre-set shape and snap back to it when heated by running an electric current through them. They’re lightweight, generate high force for their size, and are simple to control compared to some alternatives.
How Soft Robots Move
The most common approach is pneumatic or hydraulic actuation: pumping air or liquid into channels embedded in a flexible body. When a chamber inflates, it forces the surrounding material to bend, extend, or curl in a specific direction. By inflating different chambers in sequence, engineers can make a soft robot crawl, grip, or slither. Some newer designs skip the pump entirely, using phase-change materials that switch between liquid and gas states to inflate and deflate chambers without any external hardware.
Magnetically driven actuators embed tiny magnetic particles in a soft body, then use external magnetic fields to pull the robot into different shapes. Heat-driven systems rely on materials that expand or contract with temperature changes. Shape memory alloys work as artificial muscles: a thin wire contracts when heated by electric current, pulling the soft body into a new position, then relaxes as it cools. Dielectric elastomers function similarly, expanding when voltage is applied and returning to their original shape when it’s removed. Each method has trade-offs in speed, force, precision, and how much supporting equipment the robot needs.
Designs Borrowed From Nature
Much of soft robotics takes direct inspiration from animals that move without rigid skeletons. The octopus is a favorite model because its arms can bend at any point, squeeze through tiny openings, and grip objects of almost any shape. Researchers have built octopus-inspired robots using shape memory alloy springs controlled by feedback loops that mimic the coordinated muscle contractions of a real tentacle.
An elephant’s trunk is another popular blueprint. A trunk contains thousands of muscles but no bones, giving it the dexterity to uproot a tree and the delicacy to pick up a single peanut. One research team built a trunk-like gripper by embedding shape memory alloy wires in a soft polymer body, creating a device that can bend in multiple directions and wrap around irregularly shaped objects. Other teams have built robots that crawl like earthworms by sequentially contracting and expanding body segments, or swim like turtles using bending motions driven by on-off activation of shape memory alloy springs.
Medical Uses
Soft robots are a natural fit for medicine, where the ability to move gently through delicate tissue is critical. Minimally invasive surgery already reduces recovery time, hospital stays, and complications compared to open procedures, but current surgical instruments are either rigid (precise but potentially traumatic) or flexible (gentler but hard to steer). Soft robotics aims to combine the controllability of rigid tools with the safety of compliant materials.
One promising application is self-propelling colonoscopy devices. Conventional colonoscopes push through the colon from the outside, stretching the tissue and causing significant discomfort. Soft peristaltic robots could inch forward the way a worm does, advancing without stretching the surrounding tissue. Researchers have also developed entirely soft, self-propelling catheters using silicone valves inspired by the cone-shaped mitral valve in the human heart. Soft pneumatic mechanisms attached to the tips of endoscopes give surgeons the ability to steer around corners inside the body with less risk of puncturing or scraping tissue. Clinicians working on gastrointestinal procedures have expressed a clear preference for soft devices that reduce patient pain and trauma.
Grippers for Food and Agriculture
In factories and warehouses, the typical robotic gripper is a rigid claw or suction cup designed for uniform, sturdy objects. That works fine for metal parts or sealed boxes, but it fails with ripe tomatoes, pastries, or oddly shaped produce. Soft grippers conform to whatever they’re holding, distributing force evenly across the surface instead of concentrating it at a few contact points. This reduces bruising and breakage, which translates directly into less waste and higher profits for food producers.
Soft grippers also require less reconfiguration when switching between products. A rigid system handling apples in the morning and avocados in the afternoon might need different tooling for each. A compliant gripper can adapt to both with minimal or no adjustment, making it especially useful for seasonal crops that vary in size and shape. Most commercial applications are still in early stages, but suction-based soft grippers have already been deployed in industrial settings, and the technology is advancing quickly.
Wearable Soft Robots
Rigid powered exoskeletons can help paralyzed people walk, but they’re heavy, bulky, and overkill for someone who retains partial mobility after a stroke or injury. Soft robotic exosuits take a different approach: they use garment-like functional textiles with integrated cable or pneumatic actuators to gently assist movement rather than replace it. The result is something that looks and feels closer to wearing a pair of pants with built-in assistance than strapping into a mechanical frame.
Clinical research on stroke survivors has shown that wearing a soft exosuit helps people walk faster and farther. That has two practical implications. As an everyday assistive device, it could help someone move more independently in their community. As a rehabilitation tool, it increases the amount of walking practice a patient can fit into a single physical therapy session, potentially accelerating recovery.
Why They’re Hard to Control
The same infinite flexibility that makes soft robots useful also makes them enormously difficult to direct. Traditional robotics relies on mathematical models that predict exactly where a rigid arm will end up when a motor turns a certain number of degrees. Those models break down when the entire robot body can deform in unpredictable ways under load, gravity, or contact with objects.
Classical control approaches hit a wall with soft systems because the underlying math becomes too complex and nonlinear. Researchers have turned to alternative strategies: machine learning algorithms that let the robot figure out its own movement patterns through trial and error, neuroscience-inspired architectures that mimic how biological brains manage redundant degrees of freedom, and hybrid systems that pair neural networks with simpler proportional controllers. One approach uses a two-level hierarchy where a high-level planner manages the overall movement goal while a low-level controller handles the moment-to-moment physics of the soft body. These methods work, but none yet match the speed and precision of rigid robot control, which remains one of the field’s biggest open problems.