ASUS Sets Human-Pace Packing Target for Gallop Humanoid Robot

ASUS on November 13, 2026 detailed Gallop, its first humanoid robot, which is learning to pack 15 motherboard accessories at one of the group’s electronics factories, with a target of 99.5% success within 20 seconds per board.

Many of the parts are held in thin, clear bags, making the workstation a difficult assignment for simple grippers. ASUS selected it to test whether the robot can take on most of a job without a person continuously standing beside it. The company applies the same speed-and-success standard to people; Gallop’s figures are targets, not reported achieved results.

The complete demonstration, learning and line-testing loop now works: a person shows the task, the robot learns it and ASUS tests it on the line. The robot is developed by the Humanoid Robotics business unit within the Physical AI Solutions Business Group, in a program that began in early 2026.

ASUS’s broader case for the humanoid form is that arms and dexterous hands can use existing workstations, tools and trays designed for people, avoiding the need to rebuild a factory around the robot, while acknowledging that not every task requires a humanoid.

Training Based on Skilled Workers

ASUS starts with open AI models instead of building a giant model internally. Experienced workers, with their consent, wear head-mounted cameras so their demonstrations show how the job is actually performed. Simulation generates variations of those examples, and an operator wearing a VR headset and gloves then guides Gallop through practice with the same parts and cameras used at the real station.

Recordings made once the robot is deployed feed back into training. ASUS describes its edge as the right data from real stations, parts and quality rules, with its own electronics manufacturing services factories supplying a test environment that most robotics companies must rent.

Four Software Layers and In-House Hardware

Gallop’s software is divided into four layers. A planner interprets the scene and splits the job into steps; a second AI supervisor checks each step and replans after a problem; a skills layer handles reaching, grasping and placing; and a control layer moves the arms and hands precisely and stops them if something is wrong.

Fixed interfaces between the layers are intended to let ASUS replace AI models without rebuilding the robot. The system runs on one NVIDIA Jetson AGX Thor computer inside Gallop, using standard robotics software.

ASUS designs the body and a touch-sensing dexterous hand in-house, while motors and other standard components come from established partners. The current robot uses a fixed base and two arms for stationary pick-and-place work; the in-house hand has not yet been integrated.

Pilot and Commercialization Plans

ASUS plans to integrate that hand, add wheels and show Gallop to customers at Computex 2027, with customer pilots also planned for that year. Market availability is targeted for the second half of 2028.

Electronics manufacturing is the first intended market. ASUS expects pick-and-place skills learned in one job to transfer to others, creating possible later uses in hospitals and service sites.

ASUS positions the robot for repetitive, physically demanding stations that are difficult to staff, not as a replacement for workers. Employees who teach it would move into setting up, supervising and improving robots.

The planned pilots will test whether learning from skilled workers and station-specific data can sustain factory quality and pace through daily operation. ASUS has made that consistency the threshold for broader use: Gallop’s working training loop is the starting point, and the pilots must show that it can produce repeatable results under everyday factory conditions.