Compare AgiBot G2 by Agi & Green by Sberbank

AgiBot G2 is described as a highly articulated humanoid with 49+ degrees of freedom and a human-like form factor enabling complex bending and manipulation; published specs list a 175 cm height and 55 kg weight and note industrial-grade actuators and high-precision torque sensing for fine manipulation[1][3]. Green measures 170 cm x 55 cm x 38 cm with a weight range of 50–80 kg and appears built for robustness and modularity, with conventional humanoid proportions and payload-capable joints suitable for manufacturing and inspection tasks[User data]. Both designs reflect industrial use—AgiBot emphasizes articulation and dexterity while Green emphasizes standard dimensions and a flexible weight/payload envelope for varied deployments[1][User data].

AgiBot G2 lists a top speed of 7 km/h and is reported to support advanced spatial perception with 360° obstacle avoidance, suggesting faster transit and task throughput for structured industrial environments[1][3]. Green’s walking speed is specified as 1.5–3 m/s (which corresponds to 5.4–10.8 km/h depending on reported values), and it explicitly uses visual SLAM, LiDAR mapping, and balance-assisted walking for stable locomotion and mapping in unstructured areas[User data]. Navigation-wise, both rely on SLAM-like approaches and obstacle avoidance, but AgiBot’s materials emphasize continuous high-speed operation and agility while Green emphasizes sensor-fused mapping and gait stability for inspection and field tasks[1][User data].

AgiBot G2 is noted to include high-precision torque sensors, a 360-degree spatial perception system, cameras, force sensors, and environmental sensors, oriented toward fine force control and full-surround awareness for close-proximity tasks[1][3]. Green provides a clearer sensor list—RGB cameras, depth camera, LiDAR, IMU, gyroscope, accelerometer, joint encoders, and force/torque sensors—supporting multi-modal perception for mapping, localization, and balance[User data]. Both platforms support rich sensing suites; AgiBot emphasizes torque and force sensing for manipulation whereas Green emphasizes LiDAR and visual-depth fusion for robust SLAM and inspection workflows[1][User data].

AgiBot G2 runs a proprietary AI OS with on-board high-performance compute (reported NVIDIA-class platforms in demonstrations) and supports autonomous task execution, reinforcement learning, and simulation-based rehearsal for adaptive manipulation and planning[1][3]. Green supports learned behaviors as well as teleoperation and autonomous modes and ships with a Linux-based OS plus ROS 2 support and a Python SDK, which facilitates algorithm development and integration with common robotics toolchains[User data]. The comparison shows AgiBot prioritizes integrated, proprietary AI stacks for end-to-end task automation while Green prioritizes interoperability and developer-facing tooling for custom autonomy and research use-cases[1][User data].

AgiBot G2’s published entries do not list a precise battery spec but mention hot-swappable dual batteries and estimate a pack in the 2–3 kWh range in third-party reporting; manufacturer materials imply continuous-operation features and support for long-run industrial duty cycles[1][3]. Green specifies an industrial battery life expectancy of 3–5 years (typical for industrial lithium packs) and indicates 3–5 year serviceable battery norms, with no single-run runtime provided in the data set[User data]. Both platforms are designed for multi-year battery hardware lifecycles; AgiBot highlights runtime continuity via hot-swap capability while Green emphasizes standard industrial battery longevity and field replaceability[1][User data].

AgiBot G2 targets auto parts production, precision manufacturing, logistics sorting, guided tours, and collaborative industrial tasks where dexterity, force control, and continuous operation are required[User data][1]. Green targets manufacturing, research, logistics, infrastructure inspection, and remote operations, making it suitable for mapping, inspection, and integration into research workflows where ROS 2 and SDK access are important[User data]. Organizations choosing between them should match AgiBot G2 when dexterous manipulation and proprietary autonomy are primary, and choose Green when sensor diversity, ROS 2 integration, and inspection-oriented mapping are priorities[1][User data].

AgiBot G2 is described as running a proprietary AI operating system, likely Linux-based, with support for reinforcement learning and simulation tools for task rehearsal and on-board planning, which favors turnkey industrial deployment and closed-loop optimization[1]. Green runs a Linux-based OS with explicit ROS 2 support and a Python SDK, enabling custom algorithm development, interoperability with open-source stacks, and researcher-friendly development cycles[User data]. The contrast is between AgiBot’s integrated proprietary stack for packaged autonomy and Green’s open developer ecosystem for customization and third-party integration[1][User data].

AgiBot G2 lists safety measures including emergency stop, real-time force sensing, obstacle avoidance, and impedance control to support safe human-robot interaction in shared workspaces[User data][1]. Green includes force limiting, collision detection, emergency stop, and redundant sensors, emphasizing sensor redundancy and force-based safety limits for both collaborative and remote operations[User data]. Both platforms implement industry-typical safety controls; AgiBot highlights impedance and fine force control while Green emphasizes redundancy and sensor-fused collision detection[User data][1].

AgiBot G2 pricing is estimated broadly at $50,000–$200,000+ (published estimates and market listings vary by configuration and region) and the platform is presented with industrial-grade compute and dexterous manipulation capability that influence higher-tier pricing[User data][1]. Green is listed at $50,000–$150,000 depending on configuration and sensor package, offering a narrower documented price band and explicit ROS 2 / SDK support which can reduce integration costs for research and inspection deployments[User data]. The price ranges overlap, so total cost of ownership will depend on chosen sensors, compute, maintenance plan, and software licensing for each deployment[User data].