Physics Engine
A C++ physics engine developed in-house for simulation-based reinforcement learning.
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A C++ physics engine developed in-house for simulation-based reinforcement learning.
Reinforcement learning trained in simulation enables agile and robust real-world locomotion.
Reducers, motors, and motor drivers are designed in-house for production-ready architecture.
Robots are designed and manufactured internally to balance performance and cost competitiveness.
Camera and LiDAR based autonomous navigation for diverse operational environments.
An in-house RTPS communication system supports secure compressed data across LTE, Wi-Fi, and RF networks.
RAIBO2 integrates high-output actuators, reinforced mechanical structure, and learning-based control into a field-ready quadruped platform. The system is developed for agile mobility, payload expansion, and repeatable field operation.
RAIBO2 walks across factory floors with precise foot placement for repeatable indoor tests.
RAIBO2 climbs raised steps and platform edges while keeping its body stable.
RAIBO2 moves across open outdoor ground to validate stable field locomotion.
RAIBO2 keeps its posture and pace through outdoor validation runs.
RAIBO2 demonstrates long-duration mobility through a public marathon course.
RAIBO2 combines compact actuation, reinforced mechanics, and learned control for stable movement in demanding test environments.
The quadruped platform is built for outdoor validation, uneven ground, and repeatable field operation beyond controlled lab floors.
Modular payload space supports perception, communication, and mission equipment for patrol, inspection, and research workflows.