Why building a human-like robotic hand is so incredibly difficult

A humanoid robot can run, jump, dance, and even perform a backflip, yet ask it to fold a shirt, pick up a wet sponge, or turn a key, and the problem suddenly becomes much harder. That sounds counterintuitive. A backflip looks vastly more complicated than picking up a shirt. But for a robot, the backflip is a largely predictable sequence of precisely controlled movements. Household work is full of uncertainty. Objects bend, slip, deform, break, and appear in slightly different places every time. This is why the robotic hand may be one of the hardest parts of building a genuinely useful humanoid. A hand is more than a collection of fingers A human hand combines mechanics, sensing, and control in an extraordinarily compact package. We constantly adjust our grip without consciously thinking about it. Loosen your fingers when an object starts slipping, squeeze harder when something is heavy, or change your grip when an object is unexpectedly soft. Robots have to build all of that from sensors and software. A modern robotic hand may have multiple motors and degrees of freedom, but that does not automatically make it dexterous. The robot also needs to know where its fingers are, where the object is, how hard it is touching it, whether it is slipping, and how much force is safe. This is particularly difficult because vision cannot provide all of that information. A camera can tell a robot that it is holding an egg; it cannot directly tell the robot how close it is to crushing it. Recent research is therefore putting increasing emphasis on tactile sensing. A 2026 Science Robotics study from Zhejiang University combined visual and tactile information with reinforcement learning and online imitation learning, achieving an 85% success rate across five complex tasks involving 25 objects. The real world is annoyingly unpredictable Factory robots have an enormous advantage because engineers can control their environment. A robotic arm assembling the same component thousands of times can be given a fixed trajectory, predictable lighting, known object dimensions, and specialized tooling. A home is the opposite. A shirt might be crumpled differently every time. A glass can be partly filled. A drawer may be slightly open. A sponge changes shape when squeezed. A plastic bag has no fixed geometry at all. Researchers at Ohio State University describe this as one of the fundamental problems with household robots: physical contact is difficult to model and control, particularly when objects are soft, fragile, or irregular. And the robot is often moving while manipulating. Research on household robotics has identified bimanual coordination, stable navigation, and sufficient reachability as three major requirements for real-world whole-body manipulation. In other words, the hand cannot be considered separately from the arm, body, and environment. Why a backflip can be easier than laundry This explains the strange-looking progress in humanoid robotics. Unitree’s H1 became notable for performing a standing backflip, while its G1 has demonstrated increasingly athletic movements. But a backflip is a tightly defined movement. The robot knows what it is trying to do, the environment can be controlled, and the sequence can be rehearsed repeatedly. Laundry is different. A robot folding a shirt has to locate the fabric, understand its changing shape, choose a grasp point, pull without losing the garment, reposition its hands, account for wrinkles, and then place the result accurately. Do that with a hundred different shirts and suddenly the problem looks less like a simple movement and more like an enormous physics experiment. Chinese robotics companies are confronting exactly this problem. Recently, Reuters aptly titled a report “After running and dancing, Chinese robot firms target household chores.” The company X Square Robot, which has been mentioned in this report, alongside many other robotic companies, has also been testing humanoids on household chores such as picking up litter and arranging flowers. The missing ingredient is data Modern robots are increasingly being trained rather than simply programmed. But collecting useful manipulation data is much harder than collecting images for an AI model. A useful demonstration may need synchronized information about the robot’s joint positions, cameras, force, torque, and tactile sensors. And there simply isn’t an internet-scale dataset of people manipulating thousands of objects while simultaneously recording everything their hands feel. IEEE recently highlighted tactile data as a major bottleneck, noting that vision-language-action models are already helping robots with tasks such as laundry and tidying, while fine manipulation remains difficult partly because tactile datasets are still tiny compared with visual datasets. That is why companies and researchers are experimenting with teleoperation, wearable sensors, imitation learning, reinforcement learning and simulation. The goal is to turn human demonstrations into enough training experience for a robot to eventually handle unfamiliar situations on its own. Everyone is chasing the same missing skill The industry’s biggest names are approaching the problem from different directions. Unitree’s G1, for example, can be fitted with a force-controlled three-fingered dexterous hand and optional tactile sensing. Apptronik’s Apollo is being developed around manipulation as well as locomotion, with the company describing more than 35 iterations of its core actuators. Tesla’s Optimus is pursuing the same goal of a general-purpose humanoid, while 1X’s NEO is explicitly aimed at domestic work. None of these examples means the household robot problem is solved. Demonstrating that a robot can perform one task reliably is very different from asking it to enter an unfamiliar home and work for eight hours without supervision. That is ultimately what makes robotic hands so difficult. Humans don’t just move their fingers. We constantly sense, predict and correct what those fingers are doing. Until robots can do something closer to that loop, a machine that can perform a spectacular backflip may still struggle with the humble task of picking up a towel.Get the latest in engineering, tech, space & science - delivered daily to your inbox.Kaif Shaikh is a journalist and writer passionate about turning complex information into clear, impactful stories. His writing covers technology, sustainability, geopolitics, and occasionally fiction. A graduate in Journalism and Mass Communication, his work has appeared in the Times of India and beyond. After a near-fatal experience, Kaif began seeing both stories and silences differently. Outside work, he juggles far too many projects and passions, but always makes time to read, reflect, and hold onto the thread of wonder.