Milo, a Fully Autonomous Indoor/Outdoor Robotic Guide Dog

• URL: http://arxiv.org/abs/2607.19530v1
• Date: Tue, 21 Jul 2026 19:19:23 GMT
• Title: Milo, a Fully Autonomous Indoor/Outdoor Robotic Guide Dog
• Abstract summary: Blind and Low-Vision (BLV) people rely on guide dogs for moment-to-moment navigation.<n>We present Milo, the first open-source, low-cost robotic guide dog platform.<n>Milo is fully autonomous, requiring no priori knowledge of the environment.
• Score: 13.556684488160833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many Blind and Low-Vision (BLV) people rely on guide dogs for moment-to-moment navigation, such as staying on path and avoiding obstacles and pedestrians. However, guide dogs are expensive to acquire and maintain (approximately \$50k USD plus ongoing costs), often involve long waiting lists, and have relatively short life expectancies. While robot guide dogs offer a promising alternative, existing approaches exploring this idea suffer from several drawbacks: They often lack the autonomy required for real-world deployment, relying on prior 3D scans of the environment, external computation, or limited awareness of the handler. In this work, we present Milo, the first open-source, low-cost (approximately \$2k USD) robotic guide dog platform capable of fulfilling the basic collaborative navigation role expected of a guide dog. Milo is fully autonomous, requiring no a priori knowledge of the environment, completely self-contained with all computation performed onboard, and suitable for both indoor and outdoor navigation while avoiding obstacles and pedestrians. Our system consists of a modified Unitree Go2 robot (equipped with onboard compute, sensors, and a handle), a perception stack combining voxel mapping with floor, obstacle, and pedestrian detection, and a navigation stack based on an obstacle-avoidance policy trained in a custom bird's-eye-view simulator. We evaluate Milo in real indoor and outdoor obstacle courses and compare it against a costmap-based baseline, demonstrating smoother navigation and fewer handler collisions. To maximize accessibility for BLV users, we release both the robot hardware instructions and the complete software stack as open source.

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