From Factory Floors to Family Homes
By Yunchong, published by Aofeisi Temple
For a long time, robotic systems have largely relied on proprietary models.
Whenever the object, environment, or control method changed, teams often had to collect new data, retrain the model, and repeat deployment and debugging.
Now, embodied intelligence is entering its second half, with large-scale deployment at its core. The competition is no longer about whether a robot can complete an isolated task, but whether the same intelligent system can cross the boundaries of environments, embodiments, and tasks to achieve capability transfer and reuse.
Recently, TARS Robotics’ general-purpose embodied foundation model AWE 3.7 (AI World Engine) released a series of tasks in rapid succession over ten days. Using the same “embodied brain,” it connected a wide range of scenarios, including industrial production lines, logistics operations, lifestyle services, and mobile manipulation—responding through real-world tasks to the industry’s question of how embodied intelligence can achieve “generalization across scenarios.”
Industrial Production Lines: Three Tests of Precision, Force, and Stability
Industrial environments are a strict test of embodied intelligence: millimeter-level alignment, real-time force control, and long-term stability are all essential.
With the same base model, TARS AWE 3.7 brings fine manipulation up to production-line standards.
In parts-sorting operations, the robot must distinguish subtle differences among randomly scattered screws and nuts, pick up the small components, and place them into their corresponding metal trays. When the operation is disrupted, the system can also correct itself autonomously and continue sorting.
Wrapping tape is a highly distinctive fine-control task that also tests a robot’s ability to handle flexible materials. Wire harnesses are soft and prone to deformation, while the tape must be wrapped evenly and securely, requiring coordination between both hands.
The left hand holds the wire harness in place while the right hand guides the tape along a predefined path. When tape tension or the target position deviates, the system adjusts its trajectory in real time, ensuring that the tape stays flush with the harness without breaking.
Inserting an Ethernet cable is a contest measured in millimeters. Working with both hands, the robot guides the connector into the network port, understands and predicts the contact state, adjusts its grasp angle in real time, and uses the wrist’s multiple degrees of freedom to turn flexibly. This ensures accurate insertion with millimeter-level precision, even in the presence of disturbances.
Placing a phone into a box is a typical long-horizon manipulation task. The robot first puts the phone into the packaging box, then aligns and fits the lid with millimeter-level precision. Even under disturbances such as changing light conditions, it can complete the entire packaging process in a single run.
Tightening screws requires the robot to have a strong sense of “touch,” continuously assessing contact, resistance, and structural state.
The robot must first determine the spatial relationship between the screwdriver and the screw, lower the screwdriver, and align it precisely with the target. It then adjusts its movements in real time based on torque feedback and stops promptly once the screw is tightened—aligning accurately, tightening steadily, and stopping cleanly.
Dynamic Operations: As the Scene Changes, So Must the Robot
Real-world operations often take place in constantly changing environments: objects keep moving along conveyor belts, storage bins may be relocated at any time, and the robot itself may need to move through space while working.
Completing recognition, grasping, transport, and placement amid a continuous stream of changes depends on the seamless coordination of the robot’s dynamic perception and manipulation.
When faced with soft toys constantly moving along a conveyor belt, the robot continues observing and determining the right moment to bag them after grasping them.
By matching the conveyor belt’s rhythm, it synchronizes both hands to place the toys accurately into the bag, adapting in real time to the available window for dynamic operation.
Sorting building blocks may appear simple, but it is more challenging than it seems.
The colored blocks must each be placed into the correct bin, testing the robot’s understanding of the “color–target bin” relationship. The scene also keeps changing: if a block is placed in the wrong bin, the robot can detect the anomaly and move it to the correct location. If a storage bin is moved, the robot tracks it in real time and dynamically adjusts its trajectory, still completing the task accurately.
When the task extends across a larger space, the robot’s lower and upper limbs must work as a coupled system. Powered by AWE 3.7, the robot can navigate autonomously through an office environment, pick up an object upon reaching the target location, and then place it above the designated drop-off point.
Movement and manipulation are highly integrated, with the upper and lower limbs working in close coordination throughout the operation. The model handles the entire process end to end, without any human intervention.
Homes and Lifestyle Services: The More Familiar the Scene, the Harder It Is to Transfer Capabilities
Compared with highly structured industrial environments, home settings feature highly variable object shapes, long workflows, and few constraints, making them a natural proving ground for the transfer of general-purpose capabilities.
Tidying clothes requires the robot to use its “whole-body skills.” With the clothing lying on the floor, the robot must identify its shape, bend down to pick it up, and place it into a laundry basket.
As it transitions between bending down and standing up, its center of gravity is constantly shifting, yet its movements remain stable. This demonstrates the highly uncommon full-body control capability known in the industry as loco-manipulation—the coordination of locomotion and manipulation.
Wiping a whiteboard is an operation that most clearly showcases “hands-on skill.” Holding an eraser, the robot gently removes arbitrary writing. Either hand can perform the task, and the robot can switch hands during execution. At the same time, it continuously perceives the task state and can continue carrying out the task completely even when disturbances occur. It applies just the right amount of force, removing the writing while keeping the whiteboard stable.
Packing a schoolbag may seem routine, but it is a complex long-horizon task. Pens, erasers, and pencil cases scattered across a desk come in different shapes. The robot first identifies and categorizes them, then plans a sensible packing order, picking up and organizing the items one by one before placing them into the schoolbag in a systematic manner.
Preparing breakfast begins with autonomously opening a cabinet and retrieving bowls, plates, and other tableware. The robot places the tableware and food onto a tray in sequence. Understanding multiple objects and categories, along with spatial memory, is required throughout the process, while disturbance rejection and task recovery remain active at the same time.
Making a smoothie places even higher demands on the manipulation of flexible objects.
The robot grasps a soft plastic cup, positions it beneath the ice dispenser, and presses the device with one hand to start dispensing ice. It continuously monitors the dispensing process and the amount in the cup, stopping promptly when the cup is nearly full before delivering it. The entire workflow can be executed autonomously and continuously, without human takeover.
Behind all of this is TARS’s methodology loop for its general-purpose embodied model AWE: “native architecture—dual-prior pretraining—world-model-driven post-training—large-scale validation—data feedback.”
Built on more than one million hours of real-world human-centric data, together with rich visual and tactile information, the AWE 3.7 model enables robots to understand what they see, accurately sense what they touch, and operate reliably in real environments—laying a solid foundation for “trustworthy physical AI.”
The compounding effects of this “general-purpose brain” are now beginning to emerge:
In March 2026, the TARS A1 robot, powered by the AWE model, set a Guinness World Records title for the most sub-millimeter-precision wire-harness assemblies completed within one hour;
At WAIC that same year, the AWE general-purpose embodied foundation model won the SAIL Award, becoming the only embodied foundation model to receive the honor.
On the industrial side, this brain has already entered real production lines for continuous operation. It has achieved large-scale deployment in industrial scenarios such as precision assembly of automotive wire harnesses and collaborative inspection, providing replicable and scalable embodied-intelligence solutions for the intelligent transformation and upgrading of manufacturing.
△ A fleet of TARS robots deployed on production lines featured on Xinwen Lianbo
The competition in embodied intelligence is shifting from isolated task performance to the reuse of foundational capabilities.
AWE 3.7 is demonstrating that general-purpose deployment is no longer a distant vision, but an engineering path that has already been thoroughly validated by data, results, and industrial applications.