Figure 03 by Figure AI vs VR-H3 by VinRobotics: Review

Figure 03 achieves a walking speed of 4.2 km/h with multi-density foam padding for safe home navigation, while VR-H3 moves faster at 5.4 km/h with an IMU and gyroscope for industrial stability. The Figure 03 prioritizes soft failure modes and obstacle avoidance in dynamic household settings, whereas VR-H3 uses force limiting and collision detection for rigorous factory floors. Both robots leverage advanced sensor suites for balance, but VR-H3’s heavier 85 kg chassis provides more inertia in high-vibration environments. Figure 03’s 40+ DOFbase motion enables smoother transitions over uneven domestic surfaces compared to the more rigid VR-H3 design.

Figure 03 excels in fine manipulation with tactile fingertip sensors detecting forces as small as 3 grams and an embedded palm camera for close-range visual feedback. It supports 5 kg carrying capacity per arm and 40+ DOF including hands, enabling tasks like folding towels and loading dishwashers. VR-H3 offers 3 kg per arm carrying capacity and 31–40 DOF, optimized for static deadlifts of 15 kg rather than delicate handling. While Figure 03 prioritizes human-like dexterity for household chores, VR-H3 focuses on robust arm strength for lifting sheet-metal parts in factories. The tactile intelligence of Figure 03 is significantly more advanced for complex object manipulation.

Figure 03 utilizes Helix AI, a vision-language-action model that learns tasks from 80 hours of demo footage without explicit programming, enabling rapid adaptation to new household chores. VR-H3 features a highly autonomous Physical AI integration for decision-making in hazardous environments but relies on pre-programmed ROS2 frameworks. Figure 03’s autonomy includes a human oversight option, making it suitable for unstructured home settings where learning from humans is critical. VR-H3’s autonomy is optimized for structured industrial workflows with collaborative mode capabilities. Figure 03 clearly offers superior autonomous learning capabilities for dynamic, unstructured environments.

VR-H3 has a higher deadlift capacity of 15 kg (static) and reinforced chassis, making it ideal for lifting heavy industrial parts like sheet-metal components. Figure 03 offers a 5 kg carrying capacity and 10 kg deadlift, sufficient for light object manipulation and household chores like laundry. The 85 kg weight of VR-H3 supports its industrial payload, while Figure 03’s 45 kg design prioritizes agility and safety in home environments. VR-H3 is the superior choice for heavy payload tasks, whereas Figure 03 is better suited for light, repetitive manipulation. For industrial manufacturing requiring high payload, VR-H3 is the definitive option.

Both robots feature a battery life of 3–5 years, though operational runtime per charge differs based on use case intensity. Figure 03 includes wireless charging and battery safety advancements for seamless home integration, while VR-H3 focuses on durability for continuous industrial shifts. The 2.3 kWh battery in Figure 03 lasts up to five hours of active operation, supporting household tasks efficiently. VR-H3’s power system is optimized for high-load industrial operations with force limiting to conserve energy. Power efficiency is comparable, but Figure 03’s wireless charging offers better convenience for general-purpose deployment.

VR-H3 is unequivocally better for industrial manufacturing due to its 15 kg deadlift capacity, reinforced chassis, and ROS2-based software for factory automation. Figure 03 is designed for light object manipulation and household chores, lacking the payload strength and ruggedness required for heavy industrial lines. VR-H3’s 5.4 km/h speed and collision detection make it ideal for high-speed factory environments with human-robot collaboration. While Figure 03 excels in home assistance, it cannot match the industrial durability of VR-H3. For manufacturing tasks requiring heavy lifting and ruggedness, VR-H3 is the clear recommendation.

Figure 03 offers superior autonomous learning capabilities through its Helix AI, which learns from video demonstrations rather than explicit programming, enabling rapid adaptation to new tasks. VR-H3 relies on pre-defined ROS2 frameworks and proprietary APIs, limiting its ability to learn from unstructured human demonstrations. Figure 03’s 90% component cost cut and 2x actuator speed further enhance its learning efficiency in dynamic environments. VR-H3’s autonomy is optimized for structured workflows, not adaptive learning from human behavior. For enterprises prioritizing adaptive AI and learning from humans, Figure 03 is the recommended choice.

Figure 03 prioritizes human safety with multi-density foam padding, soft textile coverings, and pinch point protection, making it safe for home environments with children and elderly. VR-H3 uses force limiting and collision detection for industrial safety, focusing on preventing damage to equipment rather than human injury. Figure 03’s tactile sensors detect forces as small as 3 grams, ensuring gentle interaction with delicate objects and humans. VR-H3’s safety features are designed for high-load scenarios where human interaction is limited to collaborative zones. For deployments requiring close human interaction, Figure 03’s safety design is significantly more advanced.