NAO
The NAO is a compact 58 cm bipedal humanoid robot originally developed by Aldebaran Robotics and now sold by SoftBank Robotics. With 25 degrees of freedom, dual HD cameras, four directional microphones, and over 30 sensors, it supports speech in 20+ languages and is programmable via Choregraphe, Python, and C++. It has been deployed in over 70 countries across education, research, autism therapy, and RoboCup competitions, with more than 13,000 units shipped globally. The latest version, NAO V6, runs NAOqi OS on an Intel Atom processor.
Primary use case: Education
Height
58 cm
Weight
5.4 kg
DOF
25
Battery
1.5 hrs
Price
$9K
Specifications
Physical
Height
58 cm
Weight
5.4 kg
Degrees of Freedom
25
Locomotion
Bipedal
Manipulation
Hand Type
5-finger, limited grip
Max Lift/Arm
0.3 kg
Power & Autonomy
Battery Life
1.5 hrs
Charging Time
2 hrs
Commercial
Price
$9K
Availability
Available
Primary Use Case
Education
Related news
Robot NAO Learns to Plug Itself In With a Low-Cost Autonomous Charging Dock
The article title states that the NAO robot can autonomously plug itself into a low-cost charging dock. The supplied text is only a Google consent and cookie-selection page, so no details about the docking system, its developer, testing, or availability could be extracted.
Chinese Team Wins Robot Football World Cup - Chinese National Football Team's Unattainable Goal Achieved by Robots
At RoboCup 2026 in Incheon, South Korea, Chinese teams Tsinghua University Vulcan Team and China Agricultural University Mountain-Sea Team faced off in the Large-Size Humanoid League final, with Vulcan winning 6-2 to successfully defend their world championship. Unitree Evolution's Booster T1 robot dominated all bipedal humanoid football categories, with the same Chinese company taking all gold medals across different leagues and 38 teams using their products overall.
Stable Walking and Dynamic Obstacle Avoidance of Humanoid Robots Using Optimized Step Planner
International Journal of Humanoid Robotics, Ahead of Print. Developing and maintaining a collision-free trajectory is one of the most challenging tasks for autonomous robotics in unpredictable and dynamic situations. This work describes the development of a mechanism for navigating humanoid robots. Sensors are used to determine the positioning of the developed system. The breadth-first search algorithm has yielded positive results in the task of step adjustment to avoid dynamic barriers. Rule-based machine learning provides two driving angles based on the position evaluation of static and moving barriers and the goal. Further, the footstep is optimized based on the JAYA algorithm. It takes the outputs of machine learning as its input and provides an optimum driving angle to avoid static and dynamic barriers and generates a smooth trajectory. The proposed step planner is integrated into humanoid NAO for autonomous navigation. For avoiding dynamic barriers (same robot), a separate a