Compare Figure 03 by Figure AI & K2 Bumblebee by Kepler

Figure 03 is compact and relatively light at 167 cm and 45 kg with soft textile covering and multi-density foam for home safety, optimized for domestic form factors and gentle interactions. K2 Bumblebee measures 175 cm and weighs 75 kg, indicating a heavier, more robust frame suited to industrial stability and payload resilience. Figure 03 emphasizes softer external materials and pinch-point protection for close human contact, while K2 Bumblebee prioritizes structural robustness and disturbance-resistant gait for operational reliability in demanding environments.

Figure 03 achieves higher top speed at 4.2 km/h and uses vision-language-action Helix combined with SLAM and mmWave data offload to improve fleet-level navigation learning in cluttered home spaces. K2 Bumblebee has a lower top speed of 3 km/h and relies on LiDAR, SLAM, and GPS for navigation, reflecting a focus on precise pathing in warehouses and outdoor/large indoor facilities. Both systems support autonomous AI control and remote operation, but Figure 03’s stack emphasizes vision and language integration while K2 Bumblebee emphasizes LiDAR-based spatial mapping and industrial localization.

Figure 03’s sensor suite includes cameras (notably a palm camera), tactile fingertip sensors, audio system upgrades, and 10 Gbps mmWave data offload for fleet learning, prioritizing fine manipulation and audio-driven interaction. K2 Bumblebee offers 3D and fisheye cameras, up to 25 tactile sensors per finger, six-axis force-torque wrist sensors, IMUs, pressure and torque sensors, and estimated ultrasonic sensors, designed for force-aware manipulation and rich proprioception. The two platforms reflect different sensing trade-offs: Figure 03 for lightweight, manipulable home tasks and large-scale data collection; K2 Bumblebee for dense force sensing and task resilience in industrial settings.

Figure 03 runs the Helix vision-language-action AI on a ROS/Linux basis with Python and C++ APIs, enabling natural-language delegation and pixels-to-action learning across a fleet via mmWave offload. K2 Bumblebee uses Kepler OS (Linux-based), is ROS compatible, and exposes APIs for vision, motion, and voice, supporting autonomous behaviors and remote or app-based control. Figure 03 emphasizes integrated language-driven autonomy and fleet learning, whereas K2 Bumblebee emphasizes modular, ROS-compatible control with strong motion and force-feedback capabilities for industrial tasks.

Figure 03 lists battery life in a multi-year replacement expectancy of 3–5 years and design features like wireless charging and battery safety improvements, reflecting user-focused maintenance intervals for home deployments. K2 Bumblebee specifies a 4-year battery expectancy, consistent with industrial replacement cycles where predictable longevity is prioritized. Runtime and energy-per-task trade-offs differ: Figure 03’s documentation stresses home-safety and charge convenience while K2 Bumblebee emphasizes consistent operational availability for extended industrial use.

Figure 03 targets home assistance, household chores, elderly care support, light object manipulation, and cleaning tasks, aligning hardware and software around safe, human-centric interactions and fleet learning. K2 Bumblebee targets warehouse automation, assembly-line assistance, hazardous environment operations, guided tours, and research, aligning sensors and force-sensing hardware to handle heavier, higher-risk tasks. Choice depends on deployment context: homes and human-adjacent services favor Figure 03’s soft safety and Helix language integration, while industrial environments favor K2 Bumblebee’s sensing density and structural robustness.

Figure 03’s Helix system is described as vision-language-action AI built on ROS and Linux with Python and C++ APIs, and includes mmWave data offload for fleet-wide model updates and continuous learning. K2 Bumblebee runs Kepler OS (Linux-based), is ROS compatible, and provides APIs for vision, motion, and voice integration, supporting research customization and industrial integration. Both platforms support remote monitoring and app control, but Figure 03’s ecosystem is tailored to language-driven, fleet-scale learning while Kepler’s emphasizes modular industrial integration and force-aware control hooks.

Figure 03 incorporates multi-density foam, soft textile coverings, emergency stop, obstacle avoidance, and pinch-point protections designed specifically for frequent close contact with non-expert users. K2 Bumblebee includes emergency stop, obstacle avoidance, disturbance-resistant gait, and force feedback to enable safe operation in dynamic industrial environments. Both systems provide standard emergency-stop and obstacle-avoidance features, but Figure 03’s listed measures focus on mitigating harm in home interactions while K2 Bumblebee emphasizes operational stability and safe force-limited interactions.

Figure 03 is priced between $50,000 and $70,000, reflecting its home-oriented design, fleet-learning capability, and sensor/AI package aimed at early consumer/commercial deployments. K2 Bumblebee is offered at $34,000–$40,000, representing a lower entry price for an industrial-class humanoid with dense tactile and force sensing. The pricing trade-off presents Figure 03 as a higher-cost, software-driven home platform versus K2 Bumblebee as a lower-cost, sensor- and payload-oriented industrial platform.