Figure AI Figure 02vsUnitree Robotics Unitree H2
Side-by-side comparison of Figure AI Figure 02 and Unitree Robotics Unitree H2: specs, price, use cases and SDKs.

Figure 02
Commercial humanoid for BMW plants

Unitree H2
A full-sized, agile humanoid robot designed for research and real-world tasks.
Specifications
| Spec | Figure 02 | Unitree H2 |
|---|---|---|
| Price (USD) | $150,000 | $29,900 |
| Category | humanoid | humanoid |
| Payload | 20 kg | 7 kg |
| Runtime | 5 h | 3 h |
| Speed | — | 2 m/s |
| Weight | 70 kg | 70 kg |
| Reach | — | 690mm (total arm length) |
| Degrees of Freedom | — | 31 |
| Battery | — | Premium Lithium Battery, 15Ah (0.972KWh), 75.6V max |
- Manufacturing
- Logistics
- Warehousing
- Robotics Research & Development
- Human-Humanoid Interaction
- Industrial Automation & Inspection
- Logistics & Service Tasks
- Education & Training
When to pick which
Select the Figure 02 for high-intensity industrial environments like automotive assembly or heavy-duty logistics. Its 20kg payload capacity is nearly triple that of the H2, making it suitable for moving heavy components or crates on a factory floor. With a 5-hour runtime, it supports longer shifts in a commercial setting, reducing downtime for charging. Large-scale enterprises requiring a robust, proprietary API for secure integration into existing manufacturing execution systems should prioritize this model. It is the superior choice for high-volume production lines where reliability and heavy lifting are non-negotiable requirements.
Choose the Unitree H2 for academic research, software prototyping, or light-duty service applications where budget efficiency is paramount. At roughly one-fifth the cost of the Figure 02, it allows universities or startups to deploy small fleets for multi-agent testing. The native ROS 2 support and 31 degrees of freedom make it ideal for developers focusing on gait optimization or human-robot interaction. While its 7kg payload limits heavy industrial use, its agility and 2m/s speed suit inspection tasks or educational environments. It is the logical choice for R&D teams prioritizing open-source flexibility over heavy-duty industrial throughput.
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