Annotating Data for Humanoid Robot Training

Unlike simpler automated systems, a humanoid robot's perception is built on a complex fusion of dozens of disparate information streams that must be synchronized in time with millisecond precision. The process of collecting and preparing data for such machines goes far beyond the framework of classic two-dimensional image analysis. To enable a robot to navigate safely and perform tasks in a chaotic human environment, annotators perform cross-labeling of multimodal datasets.

Specifically, they combine three-dimensional point clouds obtained from LiDAR with the video stream of high-resolution RGB cameras, which enables the onboard artificial intelligence to flawlessly evaluate the volume, depth of space, and precise distance to surrounding objects. A distinct complexity and uniqueness in this niche is the labeling of internal, or proprioceptive, signals of the system, without which the coordination of a complex biomorphic body is impossible.

Quick Take

• Humanoid robots require complex multimodal labeling that combines 3D LiDAR point clouds, RGB video, and proprioceptive body signals.
• Training bipedal locomotion relies on skeletal joint labeling and the annotation of surface micro-details for continuous control of the center of mass.
• The manipulation of tools and objects requires the precise determination of object orientation in space and the manual labeling of safe contact grasp points.
• Training robots to copy human movements is based on the meticulous segmentation and cleaning of teleoperation VR sessions from random noise and tremors.
• To ensure that skills from virtual simulators transfer successfully to reality, synthetic datasets are artificially saturated with random physical and visual errors.

Specifics of Bipedal Locomotion 

Movement on two legs is a complex process where every step changes the body position and requires an instantaneous recalculation of balance. To train the system to move confidently, developers create a massive bipedal locomotion dataset, in which they meticulously label every detail of the robot's interaction with the surface and the movements of its artificial body. 

Surface Evaluation and Dynamic Balance 

To prevent the robot from falling on its first step, it needs to learn to instantly evaluate the physical properties of the support under its feet. Annotators classify surface types and label the geometry of each spatial section that the foot will encounter. This allows the artificial intelligence model to adapt the stepping speed and force to specific conditions in advance.

For high-quality training of algorithms, several surface types are distinguished in humanoid AI data:

• Slippery surfaces are labeled as low-traction zones where the robot must reduce its stride length.
• Soft and unstable coverings are marked as zones where the support may sag under the weight of the metallic body.
• Inclined surfaces are labeled with the precise indication of the slope angle, so the system can alter the flexion angle of the knees and ankles in advance to maintain balance.

Anatomical Tracking and Skeletal Labeling 

To control the entire body's position in space, full-body pose annotation is used. Annotators overlay digital skeletons onto the robot's video and 3D data – a grid of keypoints corresponding to all movable joints: hips, knees, ankles, and spine. This helps track exactly how the artificial legs bend during weight transfer.

The main goal of such labeling is to teach the AI model to predict and control the robot's center of mass. When a humanoid lifts one leg, its center of mass must shift over the supporting foot; otherwise, it will simply fall sideways. Precise joint marking across thousands of walking examples forms the foundation for effective whole-body control training, where the movements of the arms, torso, and legs are combined into a single, coordinated balance-maintenance system.

Labeling Micro-Obstacles in Everyday Life 

Unlike automotive AI, which searches for large objects on the road, a humanoid robot inside a home or office encounters micro-obstacles. A robot can easily trip over a curled carpet edge, slippers left on the floor, a child's toy, or even accidentally bump into a pet. Therefore, annotation for humanoids includes the detailed isolation of any minor variations in floor height.

Annotators manually trace the three-dimensional boundaries of such micro-objects and indicate their height and density. The artificial intelligence must clearly understand: a box on the floor is a solid obstacle that needs to be bypassed, while a soft mat represents a height change that can be stepped on, though it requires lifting the foot higher. Such detail turns a chaotic room into a map understandable to the robot, where every step is calculated with safety and stability in mind.

Complex Movements of Arms and Fingers 

For a humanoid to fully operate on a factory line, sort goods in a warehouse, or help with household chores at home, its arms and fingers must possess human-like flexibility and precision. To achieve this, developers use movement encoding, where dexterous hand labeling – the detailed annotation of the fine motor skills of artificial palms – plays a key role. 

Orientation in Space via 6DoF Alignment 

For successful interaction with an object, the robot needs to understand exactly how the object is oriented relative to its own palms. Classic two-dimensional bounding boxes are powerless here, so annotators use the 6DoF alignment method. This approach captures three spatial coordinates of the object (forward/backward, left/right, up/down) and three parameters of its rotation in space – roll, pitch, and yaw.

During labeling, the annotator overlays a precise three-dimensional digital model of the object onto the real frame from the robot's sensors, perfectly aligning their axes. This allows the system to see clearly at what exact angle a wrench is lying on the table or which way the handle of a kitchen mug is turned. If the wrench lies at a 45-degree angle, the AI must rotate the robot's wrist to exactly that same angle before the moment of contact, ensuring a smooth and natural grasp on the first attempt.

Such spatial detail is fundamental for collecting humanoid AI data, as it teaches the robot spatial awareness. Thanks to 6DoF annotation, the humanoid can predict how the position of an item in its hand will change during lifting or rotation, which helps avoid clumsy movements where the robot, trying to pick up a tool, accidentally bumps into other things on the desktop with its elbow or drops a part due to an incorrectly calculated palm tilt.

Determining Grasp Points 

Once the object's orientation is clear, the next question arises: which exact part of this object can be safely grasped? The process of grasp point annotation consists of the annotator manually marking zones intended for contact with the robot's fingers on the 3D model of the item. For each zone, the correct grip type is designated – for example, a two-finger pinch grip for a small screw or a firm power grasp with the entire palm for a heavy box or a hammer.

In addition to identifying convenient places to hold, this type of labeling functions as a safety feature, strictly dividing the item into allowed and forbidden zones. The annotator specifies "blind zones" in the dataset that the humanoid's end effectors are strictly forbidden to touch: the blade of a kitchen knife, the hot surface of a recently turned-off iron, or the fragile glass neck of a flask. The AI analyzes these labels and automatically constructs the arm's movement trajectory so that the fingers land exclusively on safe areas, such as the wooden handle of a frying pan.

Quality labeling of grasp points directly affects the stability of executing household and industrial tasks. It trains the robot's algorithms to take into account the specific shape and purpose of things: for example, picking up a cup by its handle rather than its fragile upper rim, or holding a drill precisely by its handle while leaving the trigger button free to be pressed. In this way, the humanoid's manipulations become predictable, precise, and safe for the surrounding objects and the robot itself.

Learning from Demonstration 

Humanoid robots are rarely trained to perform actions in the real world from scratch via trial and error, as this can lead to the breakdown of expensive equipment. The most effective approach is copying human behavior. The process of gathering such knowledge is based on demonstrations, where the artificial intelligence studies correct movement patterns by analyzing the experience of its operators, and the high-quality preparation of these examples forms a reliable foundation for humanoid task planning data. 

Cleaning and Labeling Teleoperation Sessions 

During the collection of teleoperation data, an engineer-operator wears a special VR suit, an exoskeleton, or haptic gloves and performs a certain domestic or production task. At that moment, the robot completely replicates its movements, recording every coordinate change, motor effort, and the visual image from its cameras. The role of annotators at this stage is to thoroughly filter and structure the resulting mass of information.

Annotators perform the delicate work of cleaning the recording: they remove random human hand tremors, micro-delays, or erroneous movements that the robot should not imitate. The cleaned movement track is broken down into distinct logical phases:

• Approach phase: the robot walks up to the desktop and stabilizes its torso.
• Guiding phase: the manipulator extends in the direction of the target object.
• Grasp phase: the fingers delicately compress the part at the pre-annotated points.
• Transport phase: the object is moved along a safe trajectory.

Such step-by-step segmentation helps AI algorithms clearly understand the boundaries of each sub-operation, transforming a continuous stream of movements into a set of comprehensible commands.

The Sim-to-Real Concept and Virtual Annotation 

Since physical data collection in the real world is a lengthy and financially costly process, a significant portion of training is transferred to specialized digital simulators. In these virtual environments, digital copies of humanoids can train for thousands of hours simultaneously, practicing complex scenarios without the risk of physical damage. However, the main challenge here is skill transfer, as the real world always has micro-deviations from an ideal computer model.

To ensure that an AI model trained in a simulator does not break down when entering a real physical warehouse, the domain randomization technique is applied during synthetic data generation. Errors are artificially introduced into virtual datasets: the stiffness levels of objects are changed, random noise is added to the sensors, the friction force of surfaces is varied, and unpredictable visual obstacles are created. Each such parameter is annotated with special deviation tags.

Thanks to this preparation of synthetic data, the artificial intelligence learns to be flexible during whole-body control training. The model grows accustomed to the fact that reality is not static, and when a real robot picks up a box that turns out to be 100 grams heavier or slightly slipperier than expected, its algorithms do not crash from an error but instantly adjust the finger squeezing force, drawing on the experience gained in the simulation.

Main Edge Cases in the Real World 

Creating a reliable humanoid robot is similar to training a novice driver: in ideal laboratory conditions, everything works flawlessly, but stepping out onto a real street brings hundreds of unpredictable surprises. It is precisely in non-standard and critical situations, which the industry calls edge cases, that the logic of standard AI algorithms most often breaks down. To prevent the robot from freezing in place out of confusion and creating dangerous situations, highly qualified annotators carefully gather and label scenarios involving optical traps, unstable physical bodies, and the dynamic behavior of people. 

Optical Traps 

One of the greatest challenges for a humanoid's visual sensors is a sharp change in illumination levels and the presence of specular or transparent surfaces. When a robot executes a task on the boundary between a shaded room and an open space, the cameras go blind for a few seconds from overexposure, and LiDAR data can become distorted. The task of annotators in such datasets is to manually correct object boundaries in the overexposed zone and mark these frames with special exposure adaptation labels, so that the AI learns to temporarily rely on proprioceptive signals and map memory while a stable video stream is being restored.

An even greater threat to the robot's logic is posed by glass office partitions, panoramic windows, or mirrored cabinet doors. Seeing its own reflection in a large mirror, an unprepared artificial intelligence perceives it as another robot or a person moving directly toward it, leading to a complete freeze of the navigation system. During the preparation of humanoid AI data, labeling specialists apply the technique of semantic segmentation to glass and mirrored zones, marking them as "non-physical objects with high reflection". This teaches the humanoid to understand the illusory nature of such surfaces, ignore phantom silhouettes, and safely walk past glass atriums or store windows.

Deformation of Objects in Hands 

While lifting a heavy metal part or a wooden block from a table is a relatively simple geometric task for a robot, interacting with soft, flexible, or deformable items can become a challenge. A solid object keeps its 6DoF coordinates unchanged during manipulations, whereas a plastic water bottle, a soft carton of milk, or an ordinary towel deforms at the slightest touch of artificial fingers. When the geometry of an item changes unpredictably right during the grasping process, classic rigid 3D bounding boxes lose their meaning, and the robot control system may release the manipulator due to a shape calculation error.

To solve this problem, advanced dynamic labeling based on 3D meshes is used, which consists of thousands of small interconnected triangles. Annotators track changes in the shape of a soft object on every millisecond frame of the video stream, capturing exactly how the wall of a carton bends under the pressure of the manipulator and how the liquid flows inside it. This complex process makes it possible to teach the neural network dynamic shape prediction: the robot begins to understand that an object can change its contours during compression and automatically recalculates the position of each finger, ensuring a reliable hold of the item without damaging it or spilling the contents.

Safety in Society 

The most difficult and, at the same time, the highest priority edge case is operating in direct contact with people. Unlike predictable industrial machines, human behavior in domestic life or in a live warehouse is completely spontaneous. A person can suddenly run across the robot's path, reach out to take a tool, or accidentally bump its shoulder while moving in a narrow corridor. Without high-quality safety training, a heavy metal humanoid structure moving at speed could become a source of serious injury to others.

To prevent accidents, annotators create specialized datasets where every human movement around the robot is parsed down to the smallest detail. They label the velocity vectors of human bodies, gaze directions, and sign language, using full-body skeletal annotation to predict a person's next action a second in advance.

FAQ

What is "sensor drift" in humanoid robots, and how do annotators help resolve it? 

Sensor drift occurs due to micro-vibrations during walking, when the LiDAR and cameras physically shift relative to the original axis, leading to the desynchronization of spatial data. Annotators identify such frames and apply dynamic calibration algorithms, manually aligning point clouds with the video stream. This teaches the onboard AI to automatically correct sensor errors in real time, relying on stationary landmarks in the room.  

How is the process of a robot's interaction with objects that have a variable center of gravity annotated? 

Objects like a half-empty water bottle or a kettle change their center of gravity during tilting due to the shifting of liquid. Annotators mark such objects with dynamic tags, linking the tilt angle of the arm with data from torque sensors in the robot's joints. This allows the artificial intelligence model to predict the change in load on the wrist in advance and smoothly adjust motor efforts while pouring water or carrying vessels. 

What is a "grasp taxonomy" and how is it used in dexterous hand labeling? 

A grasp taxonomy is a standardized classification of how the human hand holds various objects depending on their shape and weight. Annotators use this system to assign a specific digital label type to each grasp point: for example, a cylindrical grasp for a hammer handle, a spherical one for an apple, or a pinch grasp for a small screw. This simplifies task planning for the robot by offering ready-made finger configuration templates for different classes of objects. 

How is data regarding material fatigue and joint wear of the robot itself marked in humanoid AI data? 

Over time, the mechanical joints of a robot wear out, a backlash appears in them, and the motors begin to require more energy to execute the same movements. To compensate for this, annotators add degradation coefficients to datasets of proprioceptive data, marking the change in system response over long operational distances. Training on such data helps the AI adapt the whole-body control system to the current physical state of the hardware, extending the robot's service life without a capital overhaul. 

What is the difficulty of annotating textiles and clothing for domestic humanoid robots? 

Textiles do not have a permanent shape, structure, or clear boundaries, making them one of the most difficult objects for computer vision. Annotators do not simply trace clothing; they mark key functional points – collars, sleeves, towel corners – as well as the directions of fabric folds on 3D models. This allows the assistant robot to understand the topology of a soft item, find the correct places to hold the fabric, and successfully execute tasks like sorting or folding laundry. 

How is data labeling coordinated for the group interaction of multiple humanoid robots in the same warehouse? 

When multiple robots operate in the same space, their routes and manipulation zones begin to intersect. Annotators label such sessions in cross-system coordinates, highlighting mutual priority zones and communication signals between the machines. This trains the robots' algorithms to coordinate their movements collectively: yielding the right of way to a robot with a heavy load or synchronizing arm movements during the joint transport of a long, bulky object. 

How is personal data protected during the annotation of video streams from domestic assistant robots? 

Operating in private homes inevitably leads to confidential information entering the robot's cameras: people's faces, documents, computer screens, or personal belongings. Before the data reaches annotators, it passes through automated de-identification algorithms that completely blur faces and confidential text zones. Labeling specialists work exclusively with depersonalized contours of space geometry and body silhouettes, which completely eliminates the leakage of users' personal information.