Is computer vision the most important part of robotics?

Computer vision is a critical component of many robotics systems, but it is not universally the most important part. Robotics integrates multiple subsystems—like sensors, actuators, control algorithms, and planning software—that all depend on the specific application. While vision enables robots to interpret visual data (e.g., identifying objects or navigating environments), other components like motion planning, sensor fusion, or real-time control are equally vital depending on the task. For example, an industrial robot arm assembling parts might prioritize precise motor control and force feedback over vision, while a delivery drone relies heavily on vision for obstacle avoidance.

In applications where interaction with dynamic environments is key, computer vision becomes central. Self-driving cars, for instance, depend on cameras and LiDAR to detect lanes, pedestrians, and traffic signs. Similarly, agricultural robots use vision to distinguish crops from weeds for targeted spraying. However, even in these cases, vision is just one input. A self-driving car also needs radar for distance measurement and GPS for localization, while decision-making algorithms process all data to act safely. Vision alone can’t handle tasks like predicting vehicle trajectories or managing sensor failures—these require integration with other subsystems.

The importance of computer vision ultimately depends on the robot’s purpose. In warehouse robots that follow predefined paths using floor markers, RFID tags or inertial sensors might matter more. Conversely, a social robot interacting with humans needs advanced vision to recognize faces or gestures. What makes robotics effective is the seamless interaction between components: vision informs decisions, but actuators execute actions, control systems maintain stability, and software coordinates everything. For developers, focusing solely on vision risks overlooking critical aspects like latency in real-time systems or robustness in sensor fusion. A balanced approach ensures all parts work cohesively, making vision a powerful tool rather than the sole priority.