Solving Vision Tasks with Simple Photoreceptors Instead of Cameras
This work addresses the problem of reducing computational and hardware complexity in vision systems for robotics and AI applications, offering a novel approach inspired by biological simplicity.
The paper investigates whether simple visual sensors, such as single photoreceptors, can effectively solve vision tasks like navigation and control, achieving performance comparable to high-resolution cameras. It also demonstrates that sensor design is crucial and presents an optimization algorithm to find effective designs, which often outperform human-intuitive ones.
A de facto standard in solving computer vision problems is to use a common high-resolution camera and choose its placement on an agent (i.e., position and orientation) based on human intuition. On the other hand, extremely simple and well-designed visual sensors found throughout nature allow many organisms to perform diverse, complex behaviors. In this work, motivated by these examples, we raise the following questions: 1. How effective simple visual sensors are in solving vision tasks? 2. What role does their design play in their effectiveness? We explore simple sensors with resolutions as low as one-by-one pixel, representing a single photoreceptor First, we demonstrate that just a few photoreceptors can be enough to solve many tasks, such as visual navigation and continuous control, reasonably well, with performance comparable to that of a high-resolution camera. Second, we show that the design of these simple visual sensors plays a crucial role in their ability to provide useful information and successfully solve these tasks. To find a well-performing design, we present a computational design optimization algorithm and evaluate its effectiveness across different tasks and domains, showing promising results. Finally, we perform a human survey to evaluate the effectiveness of intuitive designs devised manually by humans, showing that the computationally found design is among the best designs in most cases.