TaCOS: Task-Specific Camera Optimization with Simulation

The performance of robots in their applications heavily depends on the quality of sensory input. However, designing sensor payloads and their parameters for specific robotic tasks is an expensive process that requires well-established sensor knowledge and extensive experiments with physical hardware. With cameras playing a pivotal role in robotic perception, we introduce a novel end-to-end optimization approach for co-designing a camera with specific robotic tasks by combining derivative-free and gradient-based optimizers. The proposed method leverages recent computer graphics techniques and physical camera characteristics to prototype the camera in software, simulate operational environments and tasks for robots, and optimize the camera design based on the desired tasks in a cost-effective way. We validate the accuracy of our camera simulation by comparing it with physical cameras, and demonstrate the design of cameras with stronger performance than common off-the-shelf alternatives. Our approach supports the optimization of both continuous and discrete camera parameters, manufacturing constraints, and can be generalized to a broad range of camera design scenarios including multiple cameras and unconventional cameras. This work advances the fully automated design of cameras for specific robotics tasks.