The Design Challenge of Humanoid Robot Hands: Why Household Labor Is So Difficult for Robots

A humanoid robot can run, jump, backflip, and even dance, but ask it to fold a shirt, pick up a wet sponge, or turn a key, and things get much harder. This seems somewhat counterintuitive. Backflipping appears to be far more complex than folding a shirt, but for a robot, backflipping is a predictable sequence of actions with a highly precise control process. Household work, on the other hand, is full of uncertainty. Objects bend, slip, deform, break, and appear in slightly different positions each time. As a result, the robot hand may be one of the most challenging parts of building a truly useful humanoid robot. A hand is more than just a collection of fingers. The human hand combines mechanics, sensing, and control in an extremely compact package. We constantly adjust our grip without conscious thought. We loosen our fingers when an object starts to slip, increase grip strength when an object is heavy, or change our grip when an object is unexpectedly soft. Robots need to rely on sensors and software to achieve these functions. Modern robot hands may have multiple motors and degrees of freedom, but this does not automatically make them flexible. Robots also need to know the position of their fingers, the position of the object, the force of contact, whether it is slipping, and the safe amount of force to apply. This is particularly difficult because vision cannot provide all this information. A camera can tell a robot it is holding an egg, but it cannot directly tell the robot how close it is to crushing it. Recent research has therefore increasingly emphasized tactile sensing. In 2026, a study in Science Robotics by Zhejiang University combined visual and tactile information, using reinforcement learning and online imitation learning to achieve an 85% success rate in five complex tasks involving 25 objects.

The Fundamental Challenges of Home Robotics

The real world is annoyingly unpredictable. Industrial robots have a huge advantage because engineers can control their environment. Robotic arms that assemble the same components thousands of times can have fixed trajectories, predictable lighting, known object dimensions, and specialized tools. The home environment, however, is the complete opposite. Each time a shirt is folded, the method may differ; a glass may be partially filled; a drawer may be slightly open; a sponge changes shape when squeezed; and a plastic bag has no fixed geometry. Researchers at Ohio State University describe this as one of the fundamental problems of home robotics: physical contact is difficult to model and control, especially when objects are soft, fragile, or irregular. Moreover, when manipulating objects, robots are often in motion. Research in home robotics has identified three key requirements for real-world whole-body manipulation: bilateral coordination, stable navigation, and sufficient reachability. In other words, the hand cannot be considered in isolation; it must be integrated with the arms, body, and environment. This explains the peculiar phenomenon of humanoid robot progress. Unitree's H1 is known for its ability to perform standing backflips, while the G1 showcases increasingly dynamic movements. But backflipping is a well-defined action. The robot knows what it is doing, the environment can be controlled, and the process can be rehearsed repeatedly. Folding clothes is different. A robot that folds shirts must find the fabric, understand its changing shape, choose grip points, pull without losing the garment, reposition its hands, consider wrinkles, and then accurately place the final result. When dealing with a hundred different shirts, the problem suddenly no longer looks like a simple action, but more like a massive physics experiment.

China's Robot Companies' Response Strategies

Chinese robot companies are grappling with this issue. Recently, Reuters aptly titled a report "After Running and Dancing, Chinese Robot Companies Target Household Chores." The report mentioned X Square Robot, along with many other robot companies, testing humanoid robots in household chores such as picking up trash and arranging flowers. The missing key is data. Modern robots increasingly rely on training rather than just programming. However, collecting useful operational data is much more difficult than collecting images for AI models. A useful demonstration may require synchronized information from the robot's joint positions, cameras, forces, torques, and tactile sensors. Moreover, there is no internet-scale dataset that records people operating thousands of objects while simultaneously recording their sense of touch. The IEEE recently highlighted tactile data as a major bottleneck, noting that vision-language-action models are already helping robots handle tasks like laundry and tidying up, while fine manipulation remains difficult, partly because tactile datasets are still very small compared to visual datasets. As a result, companies and researchers are experimenting with remote operation, wearable sensors, imitation learning, reinforcement learning, and simulation. The goal is to translate human demonstrations into sufficient training experience so that robots can eventually handle unfamiliar situations independently. The entire industry is pursuing the same missing skill. Leading companies in the industry are approaching the problem from different directions. For example, Unitree's G1 can be equipped with a force-controlled three-finger dexterous hand and optional tactile sensing. Apptronik's Apollo is being developed around manipulation and mobility, and the company has described more than 35 iterations of its core actuators. Tesla's Optimus is also pursuing the same goal of a general-purpose humanoid robot, while 1X's NEO explicitly targets household work. None of these examples mean that the problem of home robotics has been solved. Proving that a robot can reliably perform a task is very different from asking it to enter an unfamiliar home and work unsupervised for eight hours. This ultimately makes the robot hand so difficult. Humans are not just moving fingers. We constantly sense, predict, and correct the movement of our fingers. Until a robot can achieve a similar cycle, a robot capable of spectacular backflips may still struggle with simple tasks like picking up a towel.

Item	Specification
Research Institution	Zhejiang University
Success Rate	85%
Number of Objects Involved	25

Key Obstacles in the Development of Robot Hand Technology

The unpredictability of the home environment makes the design and control of robot hands extremely difficult. Compared to the controllability of the factory environment, the shape, weight, and position of objects in the home vary, which places higher demands on the flexibility and precision of robot hands. As the importance of tactile sensing is emphasized, robot companies are striving to overcome the challenges of data collection in order to better simulate human manipulation methods. These technological advances are crucial for the realization of true home robots, as they need to be able to independently complete various tasks in a changing environment.