H1-2 vs NAO V6: Specs, Price & ROS Support Compared (2024)
Unitree Robotics vs Aldebaran (Maxvision) — independent, side-by-side analysis.
Verdict rapide
H1-2 leads in more DOF, longer runtime, faster speed, while NAO V6 leads in lower price. Overall, H1-2 has an edge in 3 out of 4 compared categories.
H1-2
Unitree Robotics
NAO V6
Aldebaran (Maxvision)
Superposition ADN
H1-2 NAO V6
Caractéristiques
$99,900Prix$16,990
178 cmTaille58 cm
70 kgPoids5.6 kg
27DDL25
21 kgCharge utile—
2hAutonomie1h
2 m/sVitesse0.5 m/s
OuiEscaliersNon
OuiLiDARNon
Intel i5 + Intel i7; optional NVIDIA Jetson Orin NXCalculIntel Atom (upgraded in V6)
Ouverture de l'écosystème
—
H1-2
/100
70
NAO V6
NAOqi + Choregraphe. Massive academic community. Future uncertain post-bankruptcy.
Pros and cons at a glance
H1-2
Pros
- Shipping to customers (verified commercial availability)
- Ships with LiDAR for robust depth sensing in low-light environments
Cons
- Short runtime (120 min) — frequent recharging or battery swaps
NAO V6
Pros
- Shipping to customers (verified commercial availability)
- Affordable at $16,990 — accessible to research labs and SMEs
Cons
- Short runtime (75 min) — frequent recharging or battery swaps
- Cannot navigate stairs — restricts deployment environments
Frequently asked questions
Which is cheaper: H1-2 or NAO V6?
NAO V6 is cheaper at $16,990 vs $99,900 for H1-2. Note that lower price does not imply lower total cost of ownership — see the TCO calculator on the Explore page for utilisation-adjusted figures.
Are both H1-2 and NAO V6 shipping to customers?
Yes. Both are commercially available as of the last review. Delivery timelines and customer geographies vary per vendor — check the manufacturer page for each.
Which is better for ROS-based development: H1-2 or NAO V6?
H1-2 has ros2; NAO V6 has ROS 1 only. For research and multi-vendor robotics stacks, native ROS 2 is the strongest signal of ecosystem maturity.
Does H1-2 or NAO V6 use LiDAR?
H1-2 ships with LiDAR for depth sensing; NAO V6 does not. LiDAR adds cost and complexity but improves obstacle detection in low-light or low-contrast environments.
Dernière mise à jour: 2024