AgiBot G2 (Agi) vs AgiBot X2-N (Agi)

Both robots use a humanoid form with a 175 cm height and a nominal 55 kg mass, indicating similar overall scale and payload footprint; the G2 notes typical humanoid proportions (~50 cm width, ~40 cm depth) while the X2‑N’s typical proportions list ~50 cm width and ~80 cm length in the provided data, reflecting slightly different body‑depth assumptions. The G2 description emphasizes high‑precision torque sensors and a spatial perception exterior that implies a design optimized for interaction with external environment and tooling, whereas the X2‑N emphasizes proprioceptive components (joint torque, pressure, internal gyros) integrated for internal state sensing and gait control. Both designs include safety features such as emergency stop and real‑time force sensing, indicating comparable hardware safety integration. Weight parity (55 kg) suggests similar structural materials and powertrain sizing between models.

Both units report a top speed of approximately 7 km/h, indicating comparable locomotion hardware and actuator performance. AgiBot G2 lists advanced spatial perception and likely SLAM‑based navigation with obstacle avoidance, enabling environment mapping and external sensor‑based path planning, while AgiBot X2‑N relies on proprioceptive feedback and lacks cameras, GPS, or LiDAR per provided notes, indicating navigation through internal state estimation and reactive gait adjustment rather than map‑based SLAM. The X2‑N’s approach favors environments where internal stability and gait adaptation are essential (uneven terrain, dynamic obstacles), and the G2’s external sensing favors structured or mixed human environments requiring explicit obstacle detection and scene understanding. Both include real‑time control systems to maintain stability and respond to obstacles.

AgiBot G2 includes high‑precision torque sensors, a 360‑degree spatial perception system, cameras, force sensors and environmental sensors, indicating a broad external sensing suite for vision and scene understanding. In contrast, AgiBot X2‑N is described as having primarily proprioceptive sensors — joint torque, pressure, and internal gyros — and explicitly notes the absence of cameras or external sensors, which limits external scene perception but strengthens internal state awareness and joint‑level control. The G2’s external sensors support tasks requiring object recognition, guided tours, and collaborative work with humans, while the X2‑N’s sensor set supports robust locomotion and gait control where external sensing is unnecessary or undesirable. Sensor differences imply different software and data‑processing demands: vision/SLAM pipelines on G2 versus high‑frequency proprioceptive control loops on X2‑N.

AgiBot G2 is reported to use a proprietary AI OS, likely Linux‑based, with support for reinforcement learning and simulation, indicating capabilities for advanced autonomy, learning in simulation, and integration of multimodal inputs including voice. AgiBot X2‑N is described as Linux/ROS‑based with AI autonomous and real‑time control systems, suggesting a more open robotics middleware approach emphasizing deterministic control and integration with ROS tooling. G2’s software emphasis on proprietary AI and reinforcement learning aligns with tasks requiring perception‑driven decision making and continual improvement, while X2‑N’s ROS focus supports interoperability, modular algorithm deployment, and field customization for logistics or rescue operators.

Battery details for AgiBot G2 are not specified beyond an estimated lifecycle of 3–5 years typical for industrial lithium batteries, implying long‑term battery service life but unspecified runtime characteristics. AgiBot X2‑N specifies a 3–5 year battery life estimate and lists battery as 3–5 years in the provided dataset, indicating similar expected pack longevity between models but without consistent runtime or capacity numbers. The lack of explicit runtime/pack capacity for G2 versus X2‑N means operational endurance and charging cadence cannot be directly compared from available data. Both platforms therefore require customer validation of runtime (hours per charge) and battery management specifics prior to deployment.

AgiBot G2 is positioned for auto parts production, precision manufacturing, logistics sorting, guided tours, and collaborative industrial tasks where external perception, fine force control, and integration with manufacturing workflows are required. AgiBot X2‑N is positioned for logistics, search‑and‑rescue, industrial automation, and dynamic environment navigation where robust proprioceptive gait control and real‑time adjustments are prioritized. The G2’s sensor and software suite favors tasks involving object interaction and human environments, while the X2‑N’s sensor and control focus favors locomotion resilience and operations in situations where external sensors may be limited or intentionally omitted.

Both robots include emergency stop capability and real‑time force sensing to reduce collision forces and allow rapid shutdown, with impedance control listed for the G2 and real‑time gait adjustment for the X2‑N, indicating active safety strategies tied to their sensor suites. G2’s obstacle avoidance and impedance control rely on external sensing and force feedback to modulate interactions, while X2‑N’s obstacle avoidance is implemented via proprioception and rapid gait adaptation without external vision. Both approaches provide safety mechanisms appropriate to their sensing philosophies and intended operational environments.

AgiBot G2 is described as running a proprietary AI OS that is likely Linux‑based and supports reinforcement learning and simulation workflows, suggesting a closed but task‑oriented software stack optimized by the manufacturer. AgiBot X2‑N is explicitly Linux/ROS‑based, indicating compatibility with ROS tools, community packages, and easier third‑party integration for custom autonomy or fleet management. The G2’s proprietary stack may provide turnkey features optimized for manufacturing, while the X2‑N’s ROS orientation supports customization and integration into existing robotics infrastructures.