Vision-Language-Action Models for Sewer Inspection Robotics in India

Abstract

We present SafAI, India's first indigenously trained vision-language-action (VLA) model designed for autonomous sewer inspection in non-standardized underground infrastructure. Manual scavenging, despite being legally prohibited since 2013, continues to claim over 400 lives annually in India. Existing robotic solutions, designed for standardized Western sewer systems, fail within meters of deployment in Indian conditions characterized by irregular brick-lined tunnels, open drains, and unmapped septic tanks. SafAI addresses this gap through a compact VLA architecture (SmolVLA-SewerBot) trained on field data collected from Indian sanitation workers wearing sensor arrays under an ethical consent framework. The model achieves autonomous navigation, blockage assessment, sludge extraction, and material deposition on battery-powered hardware costing under INR 2 lakh. We report results from simulation in Isaac Sim with domain randomization calibrated to Indian sewer geometry, and from initial field trials in municipal drain systems. The paper also describes our data collection methodology, which compensates participating workers and ensures informed consent at every stage.

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