Robotic systems are increasingly integrated into complex environments, ranging from warehouses and farms to public roads and factories. As the dependency on robots grows, their ability to perceive and understand the world around them becomes critical. Errors in object detection, navigation, or manipulation can lead to accidents, downtime, or mission failure.

This broad adoption brings new perception challenges. Vision systems must contend with reflective or transparent materials, cluttered scenes, and rapidly changing environments. Moreover, they often fail when lighting conditions are unpredictable, such as under glare, shadows, fog, or low-light. In such cases, traditional cameras cannot ensure consistent performance, and the risk of perception failure becomes significant.

These limitations are particularly problematic when robots must interact with a wide range of materials or handle safety-critical tasks. Without reliable sensing, robots cannot make the right decisions or operate autonomously with confidence. Solving these issues is essential to unlocking the full potential of robotic systems across industries.

SWIR imaging addresses both challenges by delivering high-contrast images even in low visibility and enabling material differentiation, which is critical for safe and efficient operation. However, current InGaAs-based SWIR cameras rely on costly exotic materials, limiting their scalability for robotics.