Why Sim2Real for Deformable Object Manipulation Is Still an Unsolved ProblemZaina HaiderFollow3 min readJust now--Robots can stack blocks like champion in simulations. But ask it to fold a towel, cut a tomato, or untangle a power cord in the real world and it gets confused and just makes a mess.This isn't just a glitch. Its a frustrating issue in robotics.Generative AI on YouTube discusses AI at length and I would recommend them to anyone willing to gain knowledge on the world of tech.Sim2Real 101In robotics, Sim2Real means training a robot in simulation and then transferring that knowledge to the real world. It saves time, money, and hardware wear. You can run thousands of training episodes in parallel and randomize environments without touching a physical robot.In theory, once a robot learns a skill in sim like picking up an object or walking, it should be able to do the same thing in the real world. This generally works with predictable and stable environments.But thats where the dream ends.The Rigid Body Success StorySim2real has seen solid progress in tasks involving rigid objects such as stacking blocks, navigating mazes, opening doors. The physics are relatively straightforward. The visual appearance and dynamics of a cube or a wall dont change much under pressure.Researchers have gotten pretty good at faking these conditions in sim and transferring the results to real world robots. But the moment you introduce deformable, soft, or squishy materials, everything falls apart.Why Deformables Break EverythingDeformable object manipulation is a completely different beast. Were talking about objects like cloth (folding shirts), cables (charging cords), food (cutting vegetables), skin and tissue (in surgical robotics)These objects change shape dynamically during manipulation. Their behavior is hard to predict, model, or simulate accurately. And even small differences between sim and real can throw everything off.The problems with dealing with deformables are listed below:1. Simulators Struggle with Soft PhysicsMost popular simulators are optimized for rigid bodies. They treat objects like blocks, not bags of flour.Simulating deformables requires modeling elasticity, friction, plasticity, and contact forces in real time. Thats extremely hard to get right. Materials behave differently depending on how you touch them. A towel folds one way on a table, another in your hands.2. You Cant Fake Randomness at This ScaleOne workaround in sim2real is domain randomization. Train the robot with random colours, lighting, object shapes, and physics parameters so it learns to adapt.This works well for rigid tasks. But with deformables, the space of possible behaviors is too large and chaotic. You cant just randomize cloth properties and hope your policy generalizes. There are too many edge cases, and the sim still cant match real-world behavior closely enough.3. Contact is UnpredictableWith rigid bodies, contact happens at fixed points. With deformables, the contact surface changes continuously. A cable might bend, twist, or wrap around itself depending on how its touched.In sim, contact might look clean and stable. In real life, its noisy, imprecise, and constantly shifting. That throws off learned policies completely especially if they rely on exact timing or positioning.How Researchers Are Tackling ItDespite the mess, progress is happening.Learning dynamics from data: Instead of modeling physics, some teams train neural networks to predict how deformable objects behave based on real-world interaction data.Differentiable physics engines: New simulators are being built to support gradients, allowing robots to optimize their actions based on simulated feedback.Residual policy learning: A sim-trained policy is used as a starting point, and real-world data is used to correct or fine tune it.Domain adaptation in perception: Instead of transferring control policies directly, some work focuses on learning a shared perception space between sim and real, especially for visual tracking of deformable shapes.If we want robots to be useful outside the lab such as handling laundry, assisting in surgery, preparing meals, they need to work with deformable, messy, everyday objects.Until we solve sim2real for deformables, were stuck with brittle demos and robots that only work in controlled conditions.If your robot folds a towel correctly in the real world, just know, thats cutting edge stuff.Visit Generative AI on YouTube to remain technologically updated.