For a long time, robot systems have mostly relied on proprietary models.
Any change in objects, scenes, or control methods typically requires fresh data collection, model training, and deployment debugging.
Embodied intelligence is now entering the second half, centered on large-scale deployment. The test is no longer whether a single task can be completed, but whether the same intelligent system can cross boundaries of environment, embodiment, and task to achieve capability transfer and reuse.
Recently, Tashi Zhihang's general-purpose embodied large model AWE3.7 (AI World Engine) released a dense series of tasks within ten days, using the same 'embodied brain' to span industrial production lines, logistics operations, life services, and mobile manipulation. These real-world tasks respond to the industry's question of how embodied intelligence can achieve 'general generalization'.
Industrial Production Lines: The Triple Test of Precision, Power, and Stability
Industrial sites are the hard yardstick for embodied intelligence—millimeter-level positioning, real-time force control, and long-term stability are all essential.
Tashi AWE3.7, built on the same base model, has achieved production-line-grade precision in fine manipulation.
In parts-sorting tasks, the robot must identify subtle differences among randomly scattered screws and nuts, grasp small parts, and drop them into corresponding iron boxes. When disturbed, the system can autonomously correct and continue sorting.
Taping is a highly recognizable fine-control task that also tests flexible materials. Wire harnesses are soft and prone to deformation; the tape must be applied evenly and firmly, requiring two-handed coordination.
A robot's left hand secures the wiring harness while its right hand wraps tape along a preset path. Real-time corrections to the trajectory compensate for any deviation in tape tension or position, keeping the wrap tight and continuous.
Plugging in an Ethernet cable demands millimeter precision. The robot coordinates both hands to guide the connector into the jack, anticipating contact, adjusting its grip on the fly, and using a multi-axis wrist to maneuver, ensuring a correct, disturbance-resistant insertion.
Packaging a phone is a long-horizon task. The robot places the handset in the box, then aligns and snaps the lid shut with millimeter accuracy, completing the full packaging sequence in one pass despite lighting changes and other disruptions.
Screw driving tests a robot's tactile sense, requiring continuous evaluation of contact, resistance, and structural condition.
The robot first establishes the spatial relationship between screwdriver and screw, lowering to align precisely on target. It then adjusts in real time based on torque feedback, stopping as soon as the screw is snug—accurate, steady, and controlled.
Dynamic Operations: As Scenes Change, Robots Must Adapt
Real-world operations are fluid: items stream down conveyor belts, bins can be moved at any moment, and the robot itself must navigate the workspace.
Succeeding at recognition, grasping, transport, and placement amid constant change depends on tight coordination between a robot's dynamic perception and manipulation.
With soft toys moving along a conveyor, the robot grabs one and then watches to time the bagging.
By following the conveyor belt's rhythm, its hands synchronously place the dolls accurately, achieving real-time adaptation to the dynamic work window.
Sorting building blocks may seem simple, but it holds hidden complexities.
Colored blocks are placed into their respective frames, testing the understanding of the color-to-target-frame rule relationship. The scene keeps changing: if a block is placed in the wrong frame, the robot detects the anomaly and removes it to the correct position; if the storage frame is moved, it tracks in real time and dynamically adjusts its trajectory, still completing the task accurately.
When the task extends to a larger space, the robot's lower and upper limbs must be coupled. Based on AWE3.7, the robot can autonomously navigate an office environment, walk to the target location, pick up an item, and then move to the drop-off point to complete the placement.
Movement and manipulation are highly integrated, with deep coordination between upper and lower limbs during operation. The model executes end-to-end seamlessly, requiring no human intervention throughout.
Home & Life Services: Familiar Scenes, Hardest to Move Online
Compared with highly structured industrial environments, home scenes feature varied object shapes, long processes, and fewer constraints, making them a natural touchstone for general capability transfer.
Organizing clothes requires full-body effort. With clothes at a low position, the robot must recognize their shape, bend down to pick them up, and place them into a storage basket.
During the transition between bending down and standing up, the center of gravity constantly shifts, yet the movements remain stable, demonstrating the full-body control capability of loco-manipulation, which is rare in the industry.
Erasing a whiteboard is the ultimate test of manual dexterity. The robot holds an eraser and gently removes any markings. Both left and right hands can perform the task and even switch mid-task. It perceives the task state in real time, continues to execute fully even when interrupted, and applies just the right amount of force—cleaning the board while keeping it stable.
Packing a schoolbag may seem ordinary, but it is a complex, long-horizon task. Pens, erasers, and pencil cases scattered on the desk come in various shapes. The robot first identifies and categorizes them, then plans a sensible packing order, picking up each item and placing it into the bag in an orderly, methodical process.
Preparing breakfast starts with autonomously taking bowls and plates from the cupboard. The robot arranges tableware and food onto a tray in sequence, with multi-object, multi-category understanding and spatial memory throughout. Its resistance to disturbance and task recovery remain active at all times.
Making a smoothie, meanwhile, places higher demands on manipulating deformable objects.
The robot grasps a soft plastic cup, aligns it with the ice dispenser, and presses the device with one hand to start dispensing ice. It continuously monitors the dispensing process and the cup's fill level, stopping promptly when it is nearly full, then delivers the cup. The entire process runs autonomously without human intervention.
Underpinning all this is the closed-loop methodology of Tashi's general-purpose embodied foundation model AWE: native architecture, dual-prior pretraining, world-model-driven post-training, large-scale validation, and data feedback.
Leveraging over a million hours of human-centric real-world data and rich visual-tactile information, the AWE 3.7 model enables robots to see, feel, and act reliably in real environments, laying a solid foundation for trustworthy physical AI.
Now, the compounding effect of this "general-purpose brain" is beginning to emerge.
In March 2026, the Tashi A1 robot, powered by the AWE model, set a Guinness World Record for the most sub-millimeter wire harness assemblies completed in one hour.
That year at WAIC, the AWE general-purpose embodied large model won the SAIL Star, becoming the only embodied foundation model to receive the honor.
On the industrial side, this brain is already running continuously on real production lines, with large-scale deployments in automotive wire-harness precision assembly and collaborative inspection. It provides a replicable, scalable embodied-intelligence solution for upgrading manufacturing.
Competition in embodied intelligence is shifting from single-task performance to the reuse of foundational capabilities.
AWE3.7 is proving that general-purpose deployment is no longer a distant vision but an engineering path fully validated by data, results, and industrial scenarios.
This article is republished with permission from QbitAI; the views are solely those of the original author.